authors/fdabrandao/cookbook/batch_processessing.ipynb

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   "source": [
    "# Scheduling Multipurpose Batch Processes using State-Task Networks in Python\n",
    "[![batch_processessing.ipynb](https://img.shields.io/badge/github-%23121011.svg?logo=github)](https://github.com/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb) [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb) [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb) [![Hits](https://h.ampl.com/https://github.com/ampl/colab.ampl.com/blob/master/authors/fdabrandao/cookbook/batch_processessing.ipynb)](https://colab.ampl.com)\n",
    "\n",
    "Description: The State-Task Network (STN) is an approach to modeling multipurpose batch process for the purpose of short term scheduling. It was first developed by Kondili, et al., in 1993, and subsequently developed and extended by others.\n",
    "\n",
    "Tags: state-task networks, gdp, disjunctive programming, batch processes, batch processing\n",
    "\n",
    "Notebook author: Jeffrey C. Kantor, Filipe Brandão <<fdabrandao@gmail.com>>\n",
    "\n",
    "Based on: [Original notebook from the ND Pyomo Cookbook](https://jckantor.github.io/ND-Pyomo-Cookbook/notebooks/04.05-Scheduling-Multipurpose-Batch-Processes-using-State-Task_Networks.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install dependencies\n",
    "%pip install -q amplpy pandas numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Google Colab & Kaggle integration\n",
    "from amplpy import AMPL, ampl_notebook\n",
    "\n",
    "ampl = ampl_notebook(\n",
    "    modules=[\"highs\", \"gurobi\"],  # modules to install\n",
    "    license_uuid=\"default\",  # license to use\n",
    ")  # instantiate AMPL object and register magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YKnx9tYL-hGz",
    "outputId": "a7b9e375-ed71-4ddb-bd67-bae56976300f"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "SOLVER = \"highs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SrBo7aPF-hG1",
    "pycharm": {}
   },
   "source": [
    "## References\n",
    "\n",
    "Floudas, C. A., & Lin, X. (2005). Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Annals of Operations Research, 139(1), 131-162.\n",
    "\n",
    "Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., ... & Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161-193.\n",
    "\n",
    "Kondili, E., Pantelides, C. C., & Sargent, R. W. H. (1993). A general algorithm for short-term scheduling of batch operations—I. MILP formulation. Computers & Chemical Engineering, 17(2), 211-227.\n",
    "\n",
    "Méndez, C. A., Cerdá, J., Grossmann, I. E., Harjunkoski, I., & Fahl, M. (2006). State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering, 30(6), 913-946.\n",
    "\n",
    "Shah, N., Pantelides, C. C., & Sargent, R. W. H. (1993). A general algorithm for short-term scheduling of batch operations—II. Computational issues. Computers & Chemical Engineering, 17(2), 229-244.\n",
    "\n",
    "Wassick, J. M., & Ferrio, J. (2011). Extending the resource task network for industrial applications. Computers & chemical engineering, 35(10), 2124-2140."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2asoaFQy-hG3",
    "pycharm": {}
   },
   "source": [
    "## Example (Kondili, et al., 1993)\n",
    "\n",
    "A state-task network is a graphical representation of the activities in a multi-product batch process. The representation includes the minimum details needed for short term scheduling of batch operations.\n",
    "\n",
    "A well-studied example due to Kondili (1993) is shown below. Other examples are available in the references cited above.\n",
    "\n",
    "![Kondili_1993.png](https://github.com/jckantor/ND-Pyomo-Cookbook/blob/master/notebooks/figures/Kondili_1993.png?raw=1)\n",
    "\n",
    "Each circular node in the diagram designates material in a particular state.  The materials are generally held in suitable vessels with a known capacity. The relevant information for each state is the initial inventory, storage capacity, and the unit price of the material in each state. The price of materials in intermediate states may be assigned penalties in order to minimize the amount of work in progress.\n",
    "\n",
    "The rectangular nodes denote process tasks. When scheduled for execution, each task is assigned an appropriate piece of equipment, and assigned a batch of material according to the incoming arcs. Each incoming arc begins at a state where the associated label indicates the mass fraction of the batch coming from that particular state. Outgoing arcs indicate the disposition of the batch to product states. The outgoing are labels indicate the fraction of the batch assigned to each product state, and the time necessary to produce that product.\n",
    "\n",
    "Not shown in the diagram is the process equipment used to execute the tasks. A separate list of process units is available, each characterized by a capacity and list of tasks which can be performed in that unit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MR_W5rIc-hG3",
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   },
   "source": [
    "### Exercise\n",
    "\n",
    "Read this recipe for Hollandaise Sauce: http://www.foodnetwork.com/recipes/tyler-florence/hollandaise-sauce-recipe-1910043. Assume the available equipment consists of one sauce pan and a double-boiler on a stove. Draw a state-task network outlining the basic steps in the recipe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hXtiTJty-hG4",
    "pycharm": {}
   },
   "source": [
    "## Encoding the STN data\n",
    "\n",
    "The basic data structure specifies the states, tasks, and units comprising a state-task network. The intention is for all relevant problem data to be contained in a single JSON-like structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "NtMJJFdh-hG4",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# planning horizon\n",
    "H = 10\n",
    "\n",
    "Kondili = {\n",
    "    # time grid\n",
    "    \"TIME\": range(0, H + 1),\n",
    "    # states\n",
    "    \"STATES\": {\n",
    "        \"Feed_A\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Feed_B\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Feed_C\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Hot_A\": {\"capacity\": 100, \"initial\": 0, \"price\": -100},\n",
    "        \"Int_AB\": {\"capacity\": 200, \"initial\": 0, \"price\": -100},\n",
    "        \"Int_BC\": {\"capacity\": 150, \"initial\": 0, \"price\": -100},\n",
    "        \"Impure_E\": {\"capacity\": 100, \"initial\": 0, \"price\": -100},\n",
    "        \"Product_1\": {\"capacity\": 500, \"initial\": 0, \"price\": 10},\n",
    "        \"Product_2\": {\"capacity\": 500, \"initial\": 0, \"price\": 10},\n",
    "    },\n",
    "    # state-to-task arcs indexed by (state, task)\n",
    "    \"ST_ARCS\": {\n",
    "        (\"Feed_A\", \"Heating\"): {\"rho\": 1.0},\n",
    "        (\"Feed_B\", \"Reaction_1\"): {\"rho\": 0.5},\n",
    "        (\"Feed_C\", \"Reaction_1\"): {\"rho\": 0.5},\n",
    "        (\"Feed_C\", \"Reaction_3\"): {\"rho\": 0.2},\n",
    "        (\"Hot_A\", \"Reaction_2\"): {\"rho\": 0.4},\n",
    "        (\"Int_AB\", \"Reaction_3\"): {\"rho\": 0.8},\n",
    "        (\"Int_BC\", \"Reaction_2\"): {\"rho\": 0.6},\n",
    "        (\"Impure_E\", \"Separation\"): {\"rho\": 1.0},\n",
    "    },\n",
    "    # task-to-state arcs indexed by (task, state)\n",
    "    \"TS_ARCS\": {\n",
    "        (\"Heating\", \"Hot_A\"): {\"dur\": 1, \"rho\": 1.0},\n",
    "        (\"Reaction_2\", \"Product_1\"): {\"dur\": 2, \"rho\": 0.4},\n",
    "        (\"Reaction_2\", \"Int_AB\"): {\"dur\": 2, \"rho\": 0.6},\n",
    "        (\"Reaction_1\", \"Int_BC\"): {\"dur\": 2, \"rho\": 1.0},\n",
    "        (\"Reaction_3\", \"Impure_E\"): {\"dur\": 1, \"rho\": 1.0},\n",
    "        (\"Separation\", \"Int_AB\"): {\"dur\": 2, \"rho\": 0.1},\n",
    "        (\"Separation\", \"Product_2\"): {\"dur\": 1, \"rho\": 0.9},\n",
    "    },\n",
    "    # unit data indexed by (unit, task)\n",
    "    \"UNIT_TASKS\": {\n",
    "        (\"Heater\", \"Heating\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 100,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_1\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_2\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_3\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_1\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_2\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_3\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Still\", \"Separation\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 200,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "K3wNIWD6-hG5"
   },
   "outputs": [],
   "source": [
    "H = 16\n",
    "\n",
    "Hydrolubes = {\n",
    "    # time grid\n",
    "    \"TIME\": range(0, H + 1),\n",
    "    # states\n",
    "    \"STATES\": {\n",
    "        \"Feed_A\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Feed_B\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Feed_C\": {\"capacity\": 500, \"initial\": 500, \"price\": 0},\n",
    "        \"Hot_A\": {\"capacity\": 100, \"initial\": 0, \"price\": -100},\n",
    "        \"Int_AB\": {\"capacity\": 200, \"initial\": 0, \"price\": -100},\n",
    "        \"Int_BC\": {\"capacity\": 150, \"initial\": 0, \"price\": -100},\n",
    "        \"Impure_E\": {\"capacity\": 100, \"initial\": 0, \"price\": -100},\n",
    "        \"Product_1\": {\"capacity\": 500, \"initial\": 0, \"price\": 10},\n",
    "        \"Product_2\": {\"capacity\": 500, \"initial\": 0, \"price\": 10},\n",
    "    },\n",
    "    # state-to-task arcs indexed by (state, task)\n",
    "    \"ST_ARCS\": {\n",
    "        (\"Feed_A\", \"Heating\"): {\"rho\": 1.0},\n",
    "        (\"Feed_B\", \"Reaction_1\"): {\"rho\": 0.5},\n",
    "        (\"Feed_C\", \"Reaction_1\"): {\"rho\": 0.5},\n",
    "        (\"Feed_C\", \"Reaction_3\"): {\"rho\": 0.2},\n",
    "        (\"Hot_A\", \"Reaction_2\"): {\"rho\": 0.4},\n",
    "        (\"Int_AB\", \"Reaction_3\"): {\"rho\": 0.8},\n",
    "        (\"Int_BC\", \"Reaction_2\"): {\"rho\": 0.6},\n",
    "        (\"Impure_E\", \"Separation\"): {\"rho\": 1.0},\n",
    "    },\n",
    "    # task-to-state arcs indexed by (task, state)\n",
    "    \"TS_ARCS\": {\n",
    "        (\"Heating\", \"Hot_A\"): {\"dur\": 1, \"rho\": 1.0},\n",
    "        (\"Reaction_2\", \"Product_1\"): {\"dur\": 2, \"rho\": 0.4},\n",
    "        (\"Reaction_2\", \"Int_AB\"): {\"dur\": 2, \"rho\": 0.6},\n",
    "        (\"Reaction_1\", \"Int_BC\"): {\"dur\": 2, \"rho\": 1.0},\n",
    "        (\"Reaction_3\", \"Impure_E\"): {\"dur\": 1, \"rho\": 1.0},\n",
    "        (\"Separation\", \"Int_AB\"): {\"dur\": 2, \"rho\": 0.1},\n",
    "        (\"Separation\", \"Product_2\"): {\"dur\": 1, \"rho\": 0.9},\n",
    "    },\n",
    "    # unit data indexed by (unit, task)\n",
    "    \"UNIT_TASKS\": {\n",
    "        (\"Heater\", \"Heating\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 100,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_1\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_2\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_1\", \"Reaction_3\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_1\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_2\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Reactor_2\", \"Reaction_3\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 80,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "        (\"Still\", \"Separation\"): {\n",
    "            \"Bmin\": 0,\n",
    "            \"Bmax\": 200,\n",
    "            \"Cost\": 1,\n",
    "            \"vCost\": 0,\n",
    "            \"Tclean\": 0,\n",
    "        },\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "laaJKof0njZ0"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "\n",
    "def remove_isolated_states(stn, verbose=False):\n",
    "    states = set()\n",
    "    for state in stn[\"STATES\"]:\n",
    "        states.add(state)\n",
    "\n",
    "    tasks = set()\n",
    "    connected_tasks = set()\n",
    "    connected_states = set()\n",
    "    for s, t in stn[\"ST_ARCS\"]:\n",
    "        tasks.add(t)\n",
    "        if s not in states:\n",
    "            continue\n",
    "        connected_tasks.add(t)\n",
    "        connected_states.add(s)\n",
    "    for t, s in stn[\"TS_ARCS\"]:\n",
    "        tasks.add(t)\n",
    "        if s not in states:\n",
    "            continue\n",
    "        connected_tasks.add(t)\n",
    "        connected_states.add(s)\n",
    "    units = set()\n",
    "    connected_units = set()\n",
    "    tasks_with_units = set()\n",
    "    for u, t in stn[\"UNIT_TASKS\"]:\n",
    "        units.add(u)\n",
    "        tasks_with_units.add(t)\n",
    "        if t not in connected_tasks:\n",
    "            continue\n",
    "        connected_units.add(u)\n",
    "\n",
    "    if verbose:\n",
    "        print(f\"isolated satates: {states-connected_states}\")\n",
    "        print(f\"isolated tasks: {tasks-connected_tasks}\")\n",
    "        print(f\"isolated units: {units-connected_units}\")\n",
    "        print(f\"tasks without units: {tasks-tasks_with_units}\")\n",
    "\n",
    "    return (\n",
    "        {\n",
    "            # time grid\n",
    "            \"TIME\": stn[\"TIME\"],\n",
    "            # states\n",
    "            \"STATES\": {s: stn[\"STATES\"][s] for s in connected_states},\n",
    "            # state-to-task arcs indexed by (state, task)\n",
    "            \"ST_ARCS\": {\n",
    "                (s, t): stn[\"ST_ARCS\"][(s, t)]\n",
    "                for (s, t) in stn[\"ST_ARCS\"]\n",
    "                if s in connected_states and t in connected_tasks\n",
    "            },\n",
    "            # task-to-state arcs indexed by (task, state)\n",
    "            \"TS_ARCS\": {\n",
    "                (t, s): stn[\"TS_ARCS\"][(t, s)]\n",
    "                for (t, s) in stn[\"TS_ARCS\"]\n",
    "                if s in connected_states and t in connected_tasks\n",
    "            },\n",
    "            # unit data indexed by (unit, task)\n",
    "            \"UNIT_TASKS\": {\n",
    "                (u, t): stn[\"UNIT_TASKS\"][(u, t)]\n",
    "                for (u, t) in stn[\"UNIT_TASKS\"]\n",
    "                if t in connected_tasks\n",
    "            },\n",
    "        },\n",
    "        connected_states,\n",
    "        connected_tasks,\n",
    "    )\n",
    "\n",
    "\n",
    "def build_graph(stn, verbose=False):\n",
    "    # Remove isolated nodes\n",
    "    stn, connected_states, connected_tasks = remove_isolated_states(\n",
    "        stn, verbose=verbose\n",
    "    )\n",
    "    # Create a directed graph\n",
    "    graph = nx.DiGraph()\n",
    "\n",
    "    # Add states and tasks as nodes\n",
    "    graph.add_nodes_from(connected_states, type=\"state\")\n",
    "    graph.add_nodes_from(connected_tasks, type=\"task\")\n",
    "\n",
    "    # Add state-to-task and task-to-state arcs\n",
    "    for (s, t), v in stn[\"ST_ARCS\"].items():\n",
    "        if s not in connected_states or t not in connected_tasks:\n",
    "            continue\n",
    "        graph.add_edge(s, t, rho=v[\"rho\"])\n",
    "    for (t, s), v in stn[\"TS_ARCS\"].items():\n",
    "        if s not in connected_states or t not in connected_tasks:\n",
    "            continue\n",
    "        graph.add_edge(t, s, rho=v[\"rho\"])\n",
    "    return stn, graph\n",
    "\n",
    "\n",
    "def draw_graph(stn, graph, with_labels=False, verbose=False):\n",
    "    # Determine initial and final states\n",
    "    initial_states = {\n",
    "        state\n",
    "        for state, attr in stn[\"STATES\"].items()\n",
    "        if attr[\"initial\"] > 0 and graph.in_degree(state) == 0\n",
    "    }\n",
    "    final_states = {state for state in stn[\"STATES\"] if graph.out_degree(state) == 0}\n",
    "    if verbose:\n",
    "        print(f\"initial states: {initial_states}\")\n",
    "        print(f\"final states: {final_states}\")\n",
    "\n",
    "    tasks = set()\n",
    "    for s, t in stn[\"ST_ARCS\"]:\n",
    "        tasks.add(t)\n",
    "    for t, s in stn[\"TS_ARCS\"]:\n",
    "        tasks.add(t)\n",
    "\n",
    "    # Position nodes\n",
    "    initial_x = 0.1\n",
    "    intermediate_x = 0.5\n",
    "    final_x = 0.9\n",
    "    y = 0.5\n",
    "    new_pos = {}\n",
    "    for node in graph.nodes():\n",
    "        if node in initial_states:\n",
    "            new_pos[node] = (initial_x, y)\n",
    "            initial_x += 0.1\n",
    "        elif node in final_states:\n",
    "            new_pos[node] = (final_x, y)\n",
    "            final_x -= 0.1\n",
    "        else:\n",
    "            new_pos[node] = (intermediate_x, y)\n",
    "            intermediate_x += 0.1\n",
    "\n",
    "    # Adjust positions of tasks based on their connected states\n",
    "    task_y_offset = 0.2\n",
    "    for node in tasks:\n",
    "        avg_x = sum(\n",
    "            new_pos[pred][0] for pred in graph.predecessors(node)\n",
    "        ) / graph.in_degree(node)\n",
    "        new_pos[node] = (avg_x, y - task_y_offset)\n",
    "\n",
    "    # Draw the network graph\n",
    "    plt.figure(figsize=(14, 8))\n",
    "    node_colors = [\n",
    "        (\n",
    "            \"yellow\"\n",
    "            if n in initial_states\n",
    "            else (\n",
    "                \"red\"\n",
    "                if n in final_states\n",
    "                else \"skyblue\" if graph.nodes[n][\"type\"] == \"state\" else \"lightgreen\"\n",
    "            )\n",
    "        )\n",
    "        for n in graph.nodes()\n",
    "    ]\n",
    "    nx.draw(\n",
    "        graph,\n",
    "        new_pos,\n",
    "        with_labels=with_labels,\n",
    "        node_color=node_colors,\n",
    "        node_size=100,\n",
    "        font_size=9,\n",
    "        font_weight=\"bold\",\n",
    "        arrowstyle=\"-|>\",\n",
    "        arrowsize=20,\n",
    "    )\n",
    "    edge_labels = nx.get_edge_attributes(graph, \"rho\")\n",
    "    if with_labels:\n",
    "        nx.draw_networkx_edge_labels(\n",
    "            graph, new_pos, edge_labels=edge_labels, font_color=\"red\"\n",
    "        )\n",
    "    plt.title(\"Network Graph with Spatially Separated Initial and Final States\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "D9EQP2vlv7xV"
   },
   "outputs": [],
   "source": [
    "initial_stn = Hydrolubes  # Hydrolubes, Kondili"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "_vnvE3fmtcJv",
    "outputId": "7c6c4b9e-42fd-4d04-ebeb-70f4e6e30829"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_stn, full_graph = build_graph(initial_stn, verbose=False)\n",
    "draw_graph(full_stn, full_graph, with_labels=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "xleO4BaguIM3"
   },
   "outputs": [],
   "source": [
    "def list_predecessors(stn, graph, node, visited=set(), depth=0, verbose=False):\n",
    "    visited.add(node)\n",
    "    pred = list(graph.predecessors(node))\n",
    "    if verbose:\n",
    "        print(\n",
    "            \"  \" * depth,\n",
    "            node,\n",
    "            graph.nodes[node][\"type\"],\n",
    "            \"->\",\n",
    "            [(p, graph.edges[p, node][\"rho\"]) for p in pred],\n",
    "        )\n",
    "    for p in pred:\n",
    "        list_predecessors(stn, graph, p, visited, depth + 1, verbose)\n",
    "    return visited\n",
    "\n",
    "\n",
    "def clean_stn(stn, graph, target, verbose=False):\n",
    "    visited = list_predecessors(stn, graph, target, verbose=verbose)\n",
    "    return {\n",
    "        # time grid\n",
    "        \"TIME\": stn[\"TIME\"],\n",
    "        # states\n",
    "        \"STATES\": {s: stn[\"STATES\"][s] for s in stn[\"STATES\"] if s in visited},\n",
    "        # state-to-task arcs indexed by (state, task)\n",
    "        \"ST_ARCS\": {\n",
    "            (s, t): stn[\"ST_ARCS\"][(s, t)]\n",
    "            for (s, t) in stn[\"ST_ARCS\"]\n",
    "            if s in visited and t in visited\n",
    "        },\n",
    "        # task-to-state arcs indexed by (task, state)\n",
    "        \"TS_ARCS\": {\n",
    "            (t, s): stn[\"TS_ARCS\"][(t, s)]\n",
    "            for (t, s) in stn[\"TS_ARCS\"]\n",
    "            if s in visited and t in visited\n",
    "        },\n",
    "        # unit data indexed by (unit, task)\n",
    "        \"UNIT_TASKS\": {\n",
    "            (u, t): stn[\"UNIT_TASKS\"][(u, t)]\n",
    "            for (u, t) in stn[\"UNIT_TASKS\"]\n",
    "            if t in visited\n",
    "        },\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "Y75ANFrRvek0",
    "outputId": "d6a6b283-dc40-4f45-cf69-ff75c4350439"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stn = clean_stn(full_stn, full_graph, \"Product_1\", verbose=False)\n",
    "filtered_stn, filtered_graph = build_graph(stn)\n",
    "draw_graph(filtered_stn, filtered_graph, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "vmp6YXiAzPFw"
   },
   "outputs": [],
   "source": [
    "STN = full_stn  # filtered_stn or full_stn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "gNC3V0d5sUJd"
   },
   "outputs": [],
   "source": [
    "TIME = STN[\"TIME\"]\n",
    "unit_tasks_pd = pd.DataFrame.from_dict(STN[\"UNIT_TASKS\"], orient=\"index\")\n",
    "states_df = pd.DataFrame.from_dict(STN[\"STATES\"], orient=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hPpMARrN-hG6",
    "pycharm": {}
   },
   "source": [
    "### Setting a time grid\n",
    "\n",
    "The following computations can be done on any time grid, including real-valued time points. TIME is a list of time points commencing at 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yA59xbWK-hG6",
    "pycharm": {}
   },
   "source": [
    "## Creating an AMPL model\n",
    "\n",
    "The following AMPL model closely follows the development in Kondili, et al. (1993). In particular, the first step in the model is to process the STN data to create sets as given in Kondili.  \n",
    "\n",
    "One important difference from Kondili is the adoption of a more natural time scale that starts at $t = 0$ and extends to $t = H$ (rather than from 1 to H+1).\n",
    "\n",
    "A second difference is the introduction of an additional decision variable denoted by $Q_{j,t}$ indicating the amount of material in unit $j$ at time $t$. A material balance then reads\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "Q_{jt} & = Q_{j(t-1)} + \\sum_{i\\in I_j}B_{ijt} - \\sum_{i\\in I_j}\\sum_{\\substack{s \\in \\bar{S}_i\\\\s\\ni t-P_{is} \\geq 0}}\\bar{\\rho}_{is}B_{ij(t-P_{is})} \\qquad \\forall j,t\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Following Kondili's notation, $I_j$ is the set of tasks that can be performed in unit $j$, and $\\bar{S}_i$ is the set of states fed by task $j$. We assume the units are empty at the beginning and end of production period, i.e.,\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "Q_{j(-1)} & = 0 \\qquad \\forall j \\\\\n",
    "Q_{j,H} & = 0 \\qquad \\forall j\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "The unit allocation constraints are written the full backward aggregation method described by Shah (1993).  The allocation constraint reads\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\sum_{i \\in I_j} \\sum_{t'=t}^{t-p_i+1} W_{ijt'} & \\leq 1 \\qquad \\forall j,t\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Each processing unit $j$ is tagged with a minimum and maximum capacity, $B_{ij}^{min}$ and $B_{ij}^{max}$, respectively, denoting the minimum and maximum batch sizes for each task $i$. A minimum capacity may be needed to cover heat exchange coils in a reactor or mixing blades in a blender, for example. The capacity may depend on the nature of the task being performed.  These constraints are written\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "B_{ij}^{min}W_{ijt} & \\leq B_{ijt} \\leq  B_{ij}^{max}W_{ijt} \\qquad \\forall j, \\forall i\\in I_j, \\forall t\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "febV001l-hG7",
    "pycharm": {}
   },
   "source": [
    "### Characterization of tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "XrZOyg5m-hG7",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "STATES = STN[\"STATES\"]\n",
    "ST_ARCS = STN[\"ST_ARCS\"]\n",
    "TS_ARCS = STN[\"TS_ARCS\"]\n",
    "UNIT_TASKS = STN[\"UNIT_TASKS\"]\n",
    "TIME = STN[\"TIME\"]\n",
    "H = max(TIME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "R8CZH8h3-hG7",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# set of tasks\n",
    "TASKS = set([i for (j, i) in UNIT_TASKS])\n",
    "\n",
    "# S[i] input set of states which feed task i\n",
    "S = {i: set() for i in TASKS}\n",
    "for s, i in ST_ARCS:\n",
    "    S[i].add(s)\n",
    "\n",
    "# S_[i] output set of states fed by task i\n",
    "S_ = {i: set() for i in TASKS}\n",
    "for i, s in TS_ARCS:\n",
    "    S_[i].add(s)\n",
    "\n",
    "# rho[(i,s)] input fraction of task i from state s\n",
    "rho = {(i, s): ST_ARCS[(s, i)][\"rho\"] for (s, i) in ST_ARCS}\n",
    "\n",
    "# rho_[(i,s)] output fraction of task i to state s\n",
    "rho_ = {(i, s): TS_ARCS[(i, s)][\"rho\"] for (i, s) in TS_ARCS}\n",
    "\n",
    "# P[(i,s)] time for task i output to state s\n",
    "P = {(i, s): TS_ARCS[(i, s)][\"dur\"] for (i, s) in TS_ARCS}\n",
    "\n",
    "# p[i] completion time for task i\n",
    "p = {i: max([P[(i, s)] for s in S_[i]]) for i in TASKS}\n",
    "\n",
    "# K[i] set of units capable of task i\n",
    "K = {i: set() for i in TASKS}\n",
    "for j, i in UNIT_TASKS:\n",
    "    K[i].add(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sh8epKLx-hG8",
    "pycharm": {}
   },
   "source": [
    "### Characterization of states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "V3SBX0PC-hG8",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# T[s] set of tasks receiving material from state s\n",
    "T = {s: set() for s in STATES}\n",
    "for s, i in ST_ARCS:\n",
    "    T[s].add(i)\n",
    "\n",
    "# set of tasks producing material for state s\n",
    "T_ = {s: set() for s in STATES}\n",
    "for i, s in TS_ARCS:\n",
    "    T_[s].add(i)\n",
    "\n",
    "# C[s] storage capacity for state s\n",
    "C = {s: STATES[s][\"capacity\"] for s in STATES}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k_g7-ddj-hG8",
    "pycharm": {}
   },
   "source": [
    "### Characterization of units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "OIvcOP5V-hG8",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "UNITS = list(sorted(set([j for (j, i) in UNIT_TASKS])))\n",
    "\n",
    "# I[j] set of tasks performed with unit j\n",
    "I = {j: set() for j in UNITS}\n",
    "for j, i in UNIT_TASKS:\n",
    "    I[j].add(i)\n",
    "\n",
    "# Bmax[(i,j)] maximum capacity of unit j for task i\n",
    "Bmax = {(i, j): UNIT_TASKS[(j, i)][\"Bmax\"] for (j, i) in UNIT_TASKS}\n",
    "\n",
    "# Bmin[(i,j)] minimum capacity of unit j for task i\n",
    "Bmin = {(i, j): UNIT_TASKS[(j, i)][\"Bmin\"] for (j, i) in UNIT_TASKS}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pa0qA66U-hG9",
    "pycharm": {}
   },
   "source": [
    "### AMPL model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZxX9e0yKMqGC",
    "outputId": "46bbf3a3-d69a-405e-979f-e190bb6ca9b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing batch_process.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile batch_process.mod\n",
    "set TASKS;\n",
    "set UNITS;\n",
    "set TIME;\n",
    "param H;  # End time or horizon\n",
    "\n",
    "set STATES;\n",
    "param price{STATES};\n",
    "param initial{STATES};\n",
    "\n",
    "set I{j in UNITS};  # I[j] set of tasks performed with unit j\n",
    "set K{i in TASKS};  # K[i] set of units capable of task i\n",
    "\n",
    "set S_In{i in TASKS};  # S_In[i] input set of states which feed task i\n",
    "set S_Out{i in TASKS};  # S_Out[i] output set of states fed by task i\n",
    "set T_In{s in STATES};  # T_In[s] set of tasks producing material for state s\n",
    "set T_Out{s in STATES};  # T_Out[s] set of tasks receiving material from state s\n",
    "\n",
    "param P{i in TASKS, s in S_Out[i]};  # P[i,s] time for task i output to state s\n",
    "param p{i in TASKS};  # Processing times for tasks\n",
    "param Tclean{(j,i) in UNITS cross TASKS};\n",
    "\n",
    "param C{s in STATES};  # Capacity constraints for states\n",
    "\n",
    "param rho_in{i in TASKS, s in S_In[i]};  # rho_in[i,s] input fraction of task i from state s\n",
    "param rho_out{i in TASKS, s in S_Out[i]};  # rho_out[i,s] output fraction of task i to state s\n",
    "\n",
    "param Bmin{(i,j) in UNITS cross TASKS};\n",
    "param Bmax{(i,j) in UNITS cross TASKS};\n",
    "param Cost{(i,j) in UNITS cross TASKS} > 0;\n",
    "param vCost{(i,j) in UNITS cross TASKS} > 0;\n",
    "\n",
    "# W[i,j,t] 1 if task i starts in unit j at time t\n",
    "var W{i in TASKS, j in K[i], t in TIME} binary;\n",
    "\n",
    "# B[i,j,t] size of batch assigned to task i in unit j at time t\n",
    "var B{i in TASKS, j in K[i], t in TIME} >= 0;\n",
    "\n",
    "# S[s,t] inventory of state s at time t\n",
    "var S{s in STATES, t in TIME} >= 0, <= C[s];\n",
    "\n",
    "# Q[j,t] inventory of unit j at time t\n",
    "var Q{j in UNITS, t in TIME} >= 0;\n",
    "\n",
    "# Objective function\n",
    "var TotalValue = sum{s in STATES} price[s] * S[s,H];\n",
    "var TotalCost = sum{i in TASKS, j in K[i], t in TIME} (Cost[j,i] * W[i,j,t] + vCost[j,i] * B[i,j,t]);;\n",
    "\n",
    "# Objective function\n",
    "maximize Total_Profit: TotalValue - TotalCost;\n",
    "\n",
    "# Constraints\n",
    "\n",
    "# a unit can only be allocated to one task\n",
    "subject to Unit_Allocation{j in UNITS, t in TIME}:\n",
    "    sum{i in I[j], tprime in TIME: tprime >= (t - p[i] + 1 - Tclean[j,i]) && tprime <= t} W[i,j,tprime] <= 1;\n",
    "\n",
    "# state mass balances\n",
    "subject to State_Balance{s in STATES, t in TIME}:\n",
    "    S[s,t] = (if t > 0 then S[s,t-1] else initial[s])\n",
    "           + sum {i in T_In[s], j in K[i]: t >= P[i,s]} rho_out[i,s]*B[i,j,t-P[i,s]]\n",
    "           - sum {i in T_Out[s], j in K[i]} rho_in[i,s] * B[i,j,t];\n",
    "\n",
    "# unit capacity constraints\n",
    "subject to Unit_Capacity_Min{i in TASKS, j in K[i], t in TIME}:\n",
    "  W[i,j,t]*Bmin[j,i] <= B[i,j,t];\n",
    "\n",
    "subject to Unit_Capacity_Max{i in TASKS, j in K[i], t in TIME}:\n",
    "  B[i,j,t] <= W[i,j,t]*Bmax[j,i];\n",
    "\n",
    "# unit mass balances\n",
    "subject to Unit_Balance{j in UNITS, t in TIME}:\n",
    "    Q[j,t] = (if t > 0 then Q[j,t-1] else 0)\n",
    "           + sum {i in I[j]} B[i,j,t]\n",
    "           - sum {i in I[j], s in S_Out[i]: t >= P[i,s]} rho_out[i,s] * B[i,j,t-P[i,s]];\n",
    "\n",
    "# unit terminal condition\n",
    "subject to Terminal_Condition{j in UNITS}:\n",
    "    Q[j,H] = 0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "gkHeCBLq-hG9",
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "ampl = AMPL()\n",
    "ampl.read(\"batch_process.mod\")\n",
    "TIME = np.array(TIME)\n",
    "ampl.set[\"TIME\"] = TIME\n",
    "ampl.set[\"TASKS\"] = TASKS\n",
    "ampl.set[\"UNITS\"] = UNITS\n",
    "ampl.set[\"STATES\"] = STATES.keys()\n",
    "ampl.set[\"I\"] = I\n",
    "ampl.set[\"K\"] = K\n",
    "ampl.set[\"T_In\"] = T_\n",
    "ampl.set[\"T_Out\"] = T\n",
    "ampl.set[\"S_In\"] = S\n",
    "ampl.set[\"S_Out\"] = S_\n",
    "ampl.param[\"H\"] = H\n",
    "ampl.param[\"price\"] = states_df[[\"price\"]]\n",
    "ampl.param[\"initial\"] = states_df[[\"initial\"]]\n",
    "ampl.param[\"P\"] = P\n",
    "ampl.param[\"p\"] = p\n",
    "ampl.param[\"C\"] = states_df[[\"capacity\"]]\n",
    "ampl.param[\"rho_in\"] = rho\n",
    "ampl.param[\"rho_out\"] = rho_\n",
    "ampl.param[\"Bmin\"] = unit_tasks_pd[[\"Bmin\"]]\n",
    "ampl.param[\"Bmax\"] = unit_tasks_pd[[\"Bmax\"]]\n",
    "ampl.param[\"Cost\"] = unit_tasks_pd[[\"Cost\"]]\n",
    "ampl.param[\"vCost\"] = unit_tasks_pd[[\"vCost\"]]\n",
    "ampl.param[\"Tclean\"] = unit_tasks_pd[[\"Tclean\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-_WwyE93uFcC",
    "outputId": "f95d27d0-12a2-4c3a-da4a-201ec209dbb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HiGHS 1.7.0:   tech:outlev = 1\n",
      "Running HiGHS 1.7.0 (git hash: 50670fd): Copyright (c) 2024 HiGHS under MIT licence terms\n",
      "Coefficient ranges:\n",
      "  Matrix [1e-01, 2e+02]\n",
      "  Cost   [1e+00, 1e+02]\n",
      "  Bound  [1e+00, 5e+02]\n",
      "  RHS    [1e+00, 5e+02]\n",
      "Presolving model\n",
      "341 rows, 413 cols, 1316 nonzeros  0s\n",
      "268 rows, 340 cols, 1151 nonzeros  0s\n",
      "220 rows, 293 cols, 881 nonzeros  0s\n",
      "194 rows, 255 cols, 757 nonzeros  0s\n",
      "\n",
      "Solving MIP model with:\n",
      "   194 rows\n",
      "   255 cols (100 binary, 0 integer, 0 implied int., 155 continuous)\n",
      "   757 nonzeros\n",
      "\n",
      "        Nodes      |    B&B Tree     |            Objective Bounds              |  Dynamic Constraints |       Work      \n",
      "     Proc. InQueue |  Leaves   Expl. | BestBound       BestSol              Gap |   Cuts   InLp Confl. | LpIters     Time\n",
      "\n",
      "         0       0         0   0.00%   10000           -inf                 inf        0      0      0         0     0.0s\n",
      " R       0       0         0   0.00%   4923.399757     3303.666667       49.03%        0      0      0       238     0.0s\n",
      " C       0       0         0   0.00%   4919.574236     3898              26.21%      329     40      0       310     0.1s\n",
      " L       0       0         0   0.00%   4901.716371     4870.333333        0.64%     1247     88      0       586     0.4s\n",
      " L       0       0         0   0.00%   4901.673117     4870.333333        0.64%     1302     93      0      1191     0.5s\n",
      "\n",
      "19.0% inactive integer columns, restarting\n",
      "Model after restart has 161 rows, 215 cols (81 bin., 0 int., 0 impl., 134 cont.), and 615 nonzeros\n",
      "\n",
      "         0       0         0   0.00%   4901.673117     4870.333333        0.64%       29      0      0      1485     0.5s\n",
      "         0       0         0   0.00%   4901.673117     4870.333333        0.64%       29     26      3      1585     0.5s\n",
      " L       0       0         0   0.00%   4896.157012     4870.333333        0.53%      976     81      3      1893     1.1s\n",
      " L       0       0         0   0.00%   4896.157012     4870.333333        0.53%      976     81      3      3130     1.4s\n",
      "\n",
      "Symmetry detection completed in 0.0s\n",
      "Found 5 generators\n",
      "\n",
      " L       0       0         0   0.00%   4896.157012     4870.333333        0.53%      976     31      3      3763     1.8s\n",
      "\n",
      "Solving report\n",
      "  Status            Optimal\n",
      "  Primal bound      4870.33333333\n",
      "  Dual bound        4870.81944444\n",
      "  Gap               0.00998% (tolerance: 0.01%)\n",
      "  Solution status   feasible\n",
      "                    4870.33333333 (objective)\n",
      "                    0 (bound viol.)\n",
      "                    1.07802655691e-13 (int. viol.)\n",
      "                    0 (row viol.)\n",
      "  Timing            2.32 (total)\n",
      "                    0.01 (presolve)\n",
      "                    0.00 (postsolve)\n",
      "  Nodes             173\n",
      "  LP iterations     7978 (total)\n",
      "                    2037 (strong br.)\n",
      "                    886 (separation)\n",
      "                    3465 (heuristics)\n",
      "HiGHS 1.7.0: optimal solution; objective 4870.333333\n",
      "7978 simplex iterations\n",
      "173 branching nodes\n",
      "absmipgap=0.486111, relmipgap=9.98106e-05\n"
     ]
    }
   ],
   "source": [
    "# ampl.option[\"presolve_logfile\"] = \"presolve.log\"\n",
    "# ampl.option[\"presolve\"] = 0\n",
    "if SOLVER == \"highs\":\n",
    "    ampl.solve(solver=\"highs\", highs_options=\"outlev=1\")\n",
    "elif SOLVER == \"gurobi\":\n",
    "    ampl.solve(solver=\"gurobi\", gurobi_options=\"outlev=1 iis=1 timelim=600\")\n",
    "elif SOLVER == \"cplex\":\n",
    "    ampl.solve(\n",
    "        solver=\"cplexmp\",\n",
    "        highs_options=\"outlev=1\",\n",
    "        cplex_options=\"outlev=1 iis=1 timelim=30\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t20oaWBWxD5b",
    "outputId": "e35c224e-ef9e-474d-d067-3c15e50aa753"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solution: {\"W['Reaction_1','Reactor_2',0]\": 1, \"W['Reaction_1','Reactor_2',4]\": -4.973799150320706e-15, \"W['Reaction_1','Reactor_2',5]\": 1, \"W['Reaction_1','Reactor_2',9]\": 9.947598300641404e-16, \"W['Reaction_1','Reactor_1',0]\": 1, \"W['Reaction_1','Reactor_1',4]\": -2.615241356807019e-14, \"W['Reaction_1','Reactor_1',5]\": 8.688161301506626e-14, \"W['Reaction_1','Reactor_1',6]\": 0.9999999999998922, \"W['Reaction_1','Reactor_1',9]\": 1.978625596699146e-14, \"W['Separation','Still',5]\": 6.389999640532549e-14, \"W['Separation','Still',8]\": 1, \"W['Separation','Still',11]\": 1, \"W['Separation','Still',14]\": 1, \"W['Reaction_3','Reactor_2',4]\": 0.9999999999999983, \"W['Reaction_3','Reactor_2',7]\": 1, \"W['Reaction_3','Reactor_2',10]\": 1, \"W['Reaction_3','Reactor_2',13]\": 1, \"W['Reaction_3','Reactor_1',4]\": 8.688161301506626e-14, \"W['Reaction_3','Reactor_1',7]\": 8.693046282814976e-14, \"W['Reaction_3','Reactor_1',8]\": 1.978625596699146e-14, \"W['Reaction_3','Reactor_1',10]\": 0.9999999999999803, \"W['Reaction_3','Reactor_1',13]\": 1, \"W['Heating','Heater',1]\": 1, \"W['Heating','Heater',5]\": 1, \"W['Heating','Heater',7]\": 1, \"W['Reaction_2','Reactor_2',2]\": 1, \"W['Reaction_2','Reactor_2',4]\": 6.59472476627343e-15, \"W['Reaction_2','Reactor_2',8]\": 0.999999999999999, \"W['Reaction_2','Reactor_2',11]\": 1, \"W['Reaction_2','Reactor_1',2]\": 1, \"W['Reaction_2','Reactor_1',4]\": 0.9999999999999393, \"W['Reaction_2','Reactor_1',6]\": 2.08425869156296e-14, \"W['Reaction_2','Reactor_1',8]\": 0.9999999999999803, \"W['Reaction_2','Reactor_1',11]\": 1, \"B['Reaction_1','Reactor_2',0]\": 80, \"B['Reaction_1','Reactor_2',5]\": 80, \"B['Reaction_1','Reactor_2',9]\": 7.958078640513123e-14, \"B['Reaction_1','Reactor_1',0]\": 80, \"B['Reaction_1','Reactor_1',4]\": -2.092193085445615e-12, \"B['Reaction_1','Reactor_1',5]\": 6.9505290412053e-12, \"B['Reaction_1','Reactor_1',6]\": 79.99999999999137, \"B['Reaction_1','Reactor_1',9]\": 1.588229547877518e-12, \"B['Separation','Still',5]\": 5.115907697472721e-12, \"B['Separation','Still',8]\": 160.0000000000086, \"B['Separation','Still',11]\": 160, \"B['Separation','Still',14]\": 120, \"B['Reaction_3','Reactor_2',4]\": 79.99999999999987, \"B['Reaction_3','Reactor_2',7]\": 80, \"B['Reaction_3','Reactor_2',10]\": 80, \"B['Reaction_3','Reactor_2',13]\": 39.99999999999996, \"B['Reaction_3','Reactor_1',4]\": 6.9505290412053e-12, \"B['Reaction_3','Reactor_1',7]\": 6.954437026251981e-12, \"B['Reaction_3','Reactor_1',8]\": 1.582900477359317e-12, \"B['Reaction_3','Reactor_1',10]\": 79.99999999999842, \"B['Reaction_3','Reactor_1',13]\": 80, \"B['Heating','Heater',1]\": 95.99999999999866, \"B['Heating','Heater',5]\": 17.33333333332865, \"B['Heating','Heater',6]\": -2.886579864025406e-14, \"B['Heating','Heater',7]\": 99.99999999999993, \"B['Reaction_2','Reactor_2',2]\": 80, \"B['Reaction_2','Reactor_2',4]\": 5.275779813018744e-13, \"B['Reaction_2','Reactor_2',8]\": 79.99999999999991, \"B['Reaction_2','Reactor_2',11]\": 53.33333333333329, \"B['Reaction_2','Reactor_1',2]\": 80, \"B['Reaction_2','Reactor_1',4]\": 79.99999999999514, \"B['Reaction_2','Reactor_1',6]\": 1.667406953250368e-12, \"B['Reaction_2','Reactor_1',8]\": 79.99999999999842, \"B['Reaction_2','Reactor_1',11]\": 80, \"S['Feed_B',0]\": 420, \"S['Feed_B',1]\": 420, \"S['Feed_B',2]\": 420, \"S['Feed_B',3]\": 420, \"S['Feed_B',4]\": 420.000000000001, \"S['Feed_B',5]\": 379.9999999999976, \"S['Feed_B',6]\": 340.0000000000019, \"S['Feed_B',7]\": 340.0000000000019, \"S['Feed_B',8]\": 340.0000000000019, \"S['Feed_B',9]\": 340.000000000001, \"S['Feed_B',10]\": 340.000000000001, \"S['Feed_B',11]\": 340.000000000001, \"S['Feed_B',12]\": 340.000000000001, \"S['Feed_B',13]\": 340.000000000001, \"S['Feed_B',14]\": 340.000000000001, \"S['Feed_B',15]\": 340.000000000001, \"S['Feed_B',16]\": 340.000000000001, \"S['Int_BC',2]\": 64, \"S['Int_BC',3]\": 64, \"S['Int_BC',4]\": 16.0000000000026, \"S['Int_BC',5]\": 16.0000000000026, \"S['Int_BC',6]\": 15.99999999999911, \"S['Int_BC',7]\": 96.00000000000605, \"S['Int_BC',8]\": 79.99999999999831, \"S['Int_BC',9]\": 79.99999999999831, \"S['Int_BC',10]\": 79.9999999999983, \"S['Hot_A',2]\": 31.99999999999866, \"S['Hot_A',3]\": 31.99999999999866, \"S['Hot_A',4]\": 3.929301328753354e-13, \"S['Hot_A',6]\": 17.33333333333276, \"S['Hot_A',7]\": 17.33333333333273, \"S['Hot_A',8]\": 53.33333333333331, \"S['Hot_A',9]\": 53.33333333333331, \"S['Hot_A',10]\": 53.33333333333331, \"S['Impure_E',5]\": 80.0000000000017, \"S['Impure_E',6]\": 80.0000000000017, \"S['Impure_E',7]\": 80.0000000000017, \"S['Impure_E',9]\": 1.582900477359317e-12, \"S['Impure_E',10]\": 1.582900477359317e-12, \"S['Product_1',4]\": 64, \"S['Product_1',5]\": 64, \"S['Product_1',6]\": 95.99999999999825, \"S['Product_1',7]\": 95.99999999999825, \"S['Product_1',8]\": 95.99999999999892, \"S['Product_1',9]\": 95.99999999999892, \"S['Product_1',10]\": 159.9999999999983, \"S['Product_1',11]\": 159.9999999999983, \"S['Product_1',12]\": 159.9999999999983, \"S['Product_1',13]\": 213.3333333333316, \"S['Product_1',14]\": 213.3333333333316, \"S['Product_1',15]\": 213.3333333333316, \"S['Product_1',16]\": 213.3333333333316, \"S['Product_2',6]\": 4.604316927725449e-12, \"S['Product_2',7]\": 4.604316927725449e-12, \"S['Product_2',8]\": 4.604316927725449e-12, \"S['Product_2',9]\": 144.0000000000124, \"S['Product_2',10]\": 144.0000000000124, \"S['Product_2',11]\": 144.0000000000124, \"S['Product_2',12]\": 288.0000000000124, \"S['Product_2',13]\": 288.0000000000124, \"S['Product_2',14]\": 288.0000000000124, \"S['Product_2',15]\": 396.0000000000124, \"S['Product_2',16]\": 396.0000000000124, \"S['Feed_A',0]\": 500, \"S['Feed_A',1]\": 404.0000000000013, \"S['Feed_A',2]\": 404.0000000000013, \"S['Feed_A',3]\": 404.0000000000013, \"S['Feed_A',4]\": 404.0000000000013, \"S['Feed_A',5]\": 386.6666666666727, \"S['Feed_A',6]\": 386.6666666666727, \"S['Feed_A',7]\": 286.6666666666728, \"S['Feed_A',8]\": 286.6666666666728, \"S['Feed_A',9]\": 286.6666666666728, \"S['Feed_A',10]\": 286.6666666666728, \"S['Feed_A',11]\": 286.6666666666728, \"S['Feed_A',12]\": 286.6666666666728, \"S['Feed_A',13]\": 286.6666666666728, \"S['Feed_A',14]\": 286.6666666666728, \"S['Feed_A',15]\": 286.6666666666728, \"S['Feed_A',16]\": 286.6666666666728, \"S['Int_AB',4]\": 31.99999999999454, \"S['Int_AB',5]\": 31.99999999999454, \"S['Int_AB',6]\": 79.99999999999194, \"S['Int_AB',7]\": 15.99999999999908, \"S['Int_AB',8]\": 15.99999999999882, \"S['Int_AB',9]\": 15.99999999999882, \"S['Int_AB',16]\": 12, \"S['Feed_C',0]\": 420, \"S['Feed_C',1]\": 420, \"S['Feed_C',2]\": 420, \"S['Feed_C',3]\": 420, \"S['Feed_C',4]\": 403.9999999999997, \"S['Feed_C',5]\": 363.9999999999962, \"S['Feed_C',6]\": 324.0000000000005, \"S['Feed_C',7]\": 307.9999999999991, \"S['Feed_C',8]\": 307.9999999999988, \"S['Feed_C',9]\": 307.999999999998, \"S['Feed_C',10]\": 275.9999999999983, \"S['Feed_C',11]\": 275.9999999999983, \"S['Feed_C',12]\": 275.9999999999983, \"S['Feed_C',13]\": 251.9999999999983, \"S['Feed_C',14]\": 251.9999999999983, \"S['Feed_C',15]\": 251.9999999999983, \"S['Feed_C',16]\": 251.9999999999983, \"Q['Heater',1]\": 95.99999999999866, \"Q['Heater',5]\": 17.33333333332865, \"Q['Heater',6]\": -2.886579864025406e-14, \"Q['Heater',7]\": 99.99999999999993, \"Q['Reactor_1',0]\": 80, \"Q['Reactor_1',1]\": 80, \"Q['Reactor_1',2]\": 80, \"Q['Reactor_1',3]\": 80, \"Q['Reactor_1',4]\": 80, \"Q['Reactor_1',5]\": 80, \"Q['Reactor_1',6]\": 79.99999999999999, \"Q['Reactor_1',7]\": 80, \"Q['Reactor_1',8]\": 80, \"Q['Reactor_1',9]\": 80.00000000000001, \"Q['Reactor_1',10]\": 80.00000000000001, \"Q['Reactor_1',11]\": 80, \"Q['Reactor_1',12]\": 80, \"Q['Reactor_1',13]\": 80, \"Q['Reactor_2',0]\": 80, \"Q['Reactor_2',1]\": 80, \"Q['Reactor_2',2]\": 80, \"Q['Reactor_2',3]\": 80, \"Q['Reactor_2',4]\": 80.0000000000004, \"Q['Reactor_2',5]\": 80.00000000000053, \"Q['Reactor_2',6]\": 80, \"Q['Reactor_2',7]\": 80, \"Q['Reactor_2',8]\": 79.99999999999991, \"Q['Reactor_2',9]\": 80, \"Q['Reactor_2',10]\": 80.00000000000009, \"Q['Reactor_2',11]\": 53.33333333333329, \"Q['Reactor_2',12]\": 53.33333333333329, \"Q['Reactor_2',13]\": 39.99999999999996, \"Q['Still',5]\": 5.115907697472721e-12, \"Q['Still',6]\": 5.11590769747272e-13, \"Q['Still',8]\": 160.0000000000086, \"Q['Still',9]\": 16.00000000000086, \"Q['Still',11]\": 160, \"Q['Still',12]\": 16, \"Q['Still',14]\": 120, \"Q['Still',15]\": 11.99999999999999, 'TotalValue': 4893.333333333439, 'TotalCost': 23.000000000000153}\n"
     ]
    }
   ],
   "source": [
    "# %load_ext google.colab.data_table\n",
    "assert ampl.solve_result in [\"solved\", \"limit\", \"infeasible\"]\n",
    "if ampl.solve_result == \"infeasible\":\n",
    "    var_iis, con_iis = ampl.get_iis()\n",
    "    print(\"var_iis:\", var_iis)\n",
    "    print(\"con_iis:\", con_iis)\n",
    "else:\n",
    "    solution = ampl.get_solution()\n",
    "    print(\"solution:\", solution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "LASTNyK6vElh"
   },
   "outputs": [],
   "source": [
    "assert ampl.solve_result in [\"solved\", \"limit\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u0uyunzq-hG-",
    "pycharm": {}
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ARz85t2--hG-",
    "pycharm": {}
   },
   "source": [
    "### Profitability\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mi5yt304-hG-",
    "outputId": "2796d132-3699-4b2f-92d7-b85a60f5a755",
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of State Inventories =      4893.33\n",
      "  Cost of Unit Assignments =        23.00\n",
      "             Net Objective =      4870.33\n"
     ]
    }
   ],
   "source": [
    "total_value, total_cost = ampl.get_value(\"TotalValue\"), ampl.get_value(\"TotalCost\")\n",
    "print(\"Value of State Inventories = {0:12.2f}\".format(total_value))\n",
    "print(\"  Cost of Unit Assignments = {0:12.2f}\".format(total_cost))\n",
    "print(\"             Net Objective = {0:12.2f}\".format(total_value - total_cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NLlMmJ77-hG_",
    "pycharm": {}
   },
   "source": [
    "### Unit assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "UKzd6C9kCVd_"
   },
   "outputs": [],
   "source": [
    "solution = ampl.get_solution(flat=False, zeros=True)\n",
    "# solution = {\n",
    "#    \"W\": ampl.var[\"W\"].to_dict(),\n",
    "#    \"B\": ampl.var[\"B\"].to_dict(),\n",
    "#    \"S\": ampl.var[\"S\"].to_dict(),\n",
    "#    \"Q\": ampl.var[\"Q\"].to_dict(),\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "56dl8FKT-hG_",
    "outputId": "618c7efa-04a6-47c4-c100-5f4ebf3f6396",
    "pycharm": {}
   },
   "outputs": [
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       "    </tr>\n",
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       "      <th>6</th>\n",
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       "      <td>(Reaction_2, 1.667406953250368e-12)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>(Heating, 99.99999999999993)</td>\n",
       "      <td>(Reaction_3, 6.954437026251981e-12)</td>\n",
       "      <td>(Reaction_3, 80)</td>\n",
       "      <td>None</td>\n",
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       "      <td>(Reaction_2, 79.99999999999842)</td>\n",
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       "      <td>(Separation, 160.0000000000086)</td>\n",
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       "      <td>(Reaction_1, 1.588229547877518e-12)</td>\n",
       "      <td>(Reaction_1, 7.958078640513123e-14)</td>\n",
       "      <td>None</td>\n",
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       "      <th>10</th>\n",
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       "      <td>(Reaction_3, 79.99999999999842)</td>\n",
       "      <td>(Reaction_3, 80)</td>\n",
       "      <td>None</td>\n",
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       "      <td>(Reaction_2, 53.33333333333329)</td>\n",
       "      <td>(Separation, 160)</td>\n",
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       "      <th>12</th>\n",
       "      <td>None</td>\n",
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       "  @keyframes spin {\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-c9381391-3c1a-43d2-b934-9b599dbe96b1 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                          Heater                            Reactor_1  \\\n",
       "0                           None                     (Reaction_1, 80)   \n",
       "1   (Heating, 95.99999999999866)                                 None   \n",
       "2                           None                     (Reaction_2, 80)   \n",
       "3                           None                                 None   \n",
       "4                           None      (Reaction_2, 79.99999999999514)   \n",
       "5   (Heating, 17.33333333332865)    (Reaction_1, 6.9505290412053e-12)   \n",
       "6                           None  (Reaction_2, 1.667406953250368e-12)   \n",
       "7   (Heating, 99.99999999999993)  (Reaction_3, 6.954437026251981e-12)   \n",
       "8                           None      (Reaction_2, 79.99999999999842)   \n",
       "9                           None  (Reaction_1, 1.588229547877518e-12)   \n",
       "10                          None      (Reaction_3, 79.99999999999842)   \n",
       "11                          None                     (Reaction_2, 80)   \n",
       "12                          None                                 None   \n",
       "13                          None                     (Reaction_3, 80)   \n",
       "14                          None                                 None   \n",
       "15                          None                                 None   \n",
       "16                          None                                 None   \n",
       "\n",
       "                              Reactor_2                                Still  \n",
       "0                      (Reaction_1, 80)                                 None  \n",
       "1                                  None                                 None  \n",
       "2                      (Reaction_2, 80)                                 None  \n",
       "3                                  None                                 None  \n",
       "4   (Reaction_2, 5.275779813018744e-13)                                 None  \n",
       "5                      (Reaction_1, 80)  (Separation, 5.115907697472721e-12)  \n",
       "6                                  None                                 None  \n",
       "7                      (Reaction_3, 80)                                 None  \n",
       "8       (Reaction_2, 79.99999999999991)      (Separation, 160.0000000000086)  \n",
       "9   (Reaction_1, 7.958078640513123e-14)                                 None  \n",
       "10                     (Reaction_3, 80)                                 None  \n",
       "11      (Reaction_2, 53.33333333333329)                    (Separation, 160)  \n",
       "12                                 None                                 None  \n",
       "13      (Reaction_3, 39.99999999999996)                                 None  \n",
       "14                                 None                    (Separation, 120)  \n",
       "15                                 None                                 None  \n",
       "16                                 None                                 None  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UnitAssignment = pd.DataFrame({j: [None for t in TIME] for j in UNITS}, index=TIME)\n",
    "\n",
    "for t in TIME:\n",
    "    for j in UNITS:\n",
    "        for i in I[j]:\n",
    "            for s in S_[i]:\n",
    "                if t - p[i] >= 0:\n",
    "                    if solution[\"W\"][i, j, max(TIME[TIME <= t - p[i]])] > 0:\n",
    "                        UnitAssignment.loc[t, j] = None\n",
    "        for i in I[j]:\n",
    "            if solution[\"W\"][i, j, t] > 0:\n",
    "                UnitAssignment.loc[t, j] = (i, solution[\"B\"][i, j, t])\n",
    "\n",
    "UnitAssignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3isqwe6g-hG_",
    "pycharm": {}
   },
   "source": [
    "### State inventories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "YZsuZOT3-hG_",
    "outputId": "0a3a429b-5c97-4721-9def-144c7a7c3a4a",
    "pycharm": {}
   },
   "outputs": [
    {
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       "\n",
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       "      <th></th>\n",
       "      <th>Feed_B</th>\n",
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       "      <th>Hot_A</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>420.0</td>\n",
       "      <td>64.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>420.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3.929301e-13</td>\n",
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       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>16.0</td>\n",
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       "      <td>8.000000e+01</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>364.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>16.0</td>\n",
       "      <td>1.733333e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>324.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>340.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.733333e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>4.604317e-12</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>308.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>80.0</td>\n",
       "      <td>5.333333e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>96.000000</td>\n",
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       "      <td>286.666667</td>\n",
       "      <td>16.0</td>\n",
       "      <td>308.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>340.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.333333e+01</td>\n",
       "      <td>1.582900e-12</td>\n",
       "      <td>96.000000</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>308.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>80.0</td>\n",
       "      <td>5.333333e+01</td>\n",
       "      <td>1.582900e-12</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>276.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>340.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1.440000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>276.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>340.0</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>2.880000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>213.333333</td>\n",
       "      <td>2.880000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>340.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>213.333333</td>\n",
       "      <td>2.880000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>213.333333</td>\n",
       "      <td>3.960000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
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       "      <th>16</th>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>3.960000e+02</td>\n",
       "      <td>286.666667</td>\n",
       "      <td>12.0</td>\n",
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       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-24230f7d-78f2-4f17-a62d-564ab3dea7c6 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-24230f7d-78f2-4f17-a62d-564ab3dea7c6');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-2aa0e358-eef0-49c7-9e41-7f997fc84886\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2aa0e358-eef0-49c7-9e41-7f997fc84886')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-2aa0e358-eef0-49c7-9e41-7f997fc84886 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    Feed_B  Int_BC         Hot_A      Impure_E   Product_1     Product_2  \\\n",
       "0    420.0     0.0  0.000000e+00  0.000000e+00    0.000000  0.000000e+00   \n",
       "1    420.0     0.0  0.000000e+00  0.000000e+00    0.000000  0.000000e+00   \n",
       "2    420.0    64.0  3.200000e+01  0.000000e+00    0.000000  0.000000e+00   \n",
       "3    420.0    64.0  3.200000e+01  0.000000e+00    0.000000  0.000000e+00   \n",
       "4    420.0    16.0  3.929301e-13  0.000000e+00   64.000000  0.000000e+00   \n",
       "5    380.0    16.0  0.000000e+00  8.000000e+01   64.000000  0.000000e+00   \n",
       "6    340.0    16.0  1.733333e+01  8.000000e+01   96.000000  4.604317e-12   \n",
       "7    340.0    96.0  1.733333e+01  8.000000e+01   96.000000  4.604317e-12   \n",
       "8    340.0    80.0  5.333333e+01  0.000000e+00   96.000000  4.604317e-12   \n",
       "9    340.0    80.0  5.333333e+01  1.582900e-12   96.000000  1.440000e+02   \n",
       "10   340.0    80.0  5.333333e+01  1.582900e-12  160.000000  1.440000e+02   \n",
       "11   340.0     0.0  0.000000e+00  0.000000e+00  160.000000  1.440000e+02   \n",
       "12   340.0     0.0  0.000000e+00  0.000000e+00  160.000000  2.880000e+02   \n",
       "13   340.0     0.0  0.000000e+00  0.000000e+00  213.333333  2.880000e+02   \n",
       "14   340.0     0.0  0.000000e+00  0.000000e+00  213.333333  2.880000e+02   \n",
       "15   340.0     0.0  0.000000e+00  0.000000e+00  213.333333  3.960000e+02   \n",
       "16   340.0     0.0  0.000000e+00  0.000000e+00  213.333333  3.960000e+02   \n",
       "\n",
       "        Feed_A  Int_AB  Feed_C  \n",
       "0   500.000000     0.0   420.0  \n",
       "1   404.000000     0.0   420.0  \n",
       "2   404.000000     0.0   420.0  \n",
       "3   404.000000     0.0   420.0  \n",
       "4   404.000000    32.0   404.0  \n",
       "5   386.666667    32.0   364.0  \n",
       "6   386.666667    80.0   324.0  \n",
       "7   286.666667    16.0   308.0  \n",
       "8   286.666667    16.0   308.0  \n",
       "9   286.666667    16.0   308.0  \n",
       "10  286.666667     0.0   276.0  \n",
       "11  286.666667     0.0   276.0  \n",
       "12  286.666667     0.0   276.0  \n",
       "13  286.666667     0.0   252.0  \n",
       "14  286.666667     0.0   252.0  \n",
       "15  286.666667     0.0   252.0  \n",
       "16  286.666667    12.0   252.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    [[solution[\"S\"][s, t] for s in STATES.keys()] for t in TIME],\n",
    "    columns=STATES.keys(),\n",
    "    index=TIME,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "id": "obhjrKoE-hHA",
    "outputId": "ca7f0c0f-fc2d-4830-9730-5d9d6c6c449a",
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import ceil\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "for s, idx in zip(STATES.keys(), range(0, len(STATES.keys()))):\n",
    "    plt.subplot(ceil(len(STATES.keys()) / 3), 3, idx + 1)\n",
    "    tlast, ylast = 0, STATES[s][\"initial\"]\n",
    "    for t, y in zip(list(TIME), [solution[\"S\"][s, t] for t in TIME]):\n",
    "        plt.plot([tlast, t, t], [ylast, ylast, y], \"b\")\n",
    "        # plt.plot([tlast,t],[ylast,y],'b.',ms=10)\n",
    "        tlast, ylast = t, y\n",
    "    plt.ylim(0, 1.1 * C[s])\n",
    "    plt.plot([0, H], [C[s], C[s]], \"r--\")\n",
    "    plt.title(s)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cFEpDoo5-hHA",
    "pycharm": {}
   },
   "source": [
    "### Unit batch inventories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "uf6-nz9m-hHA",
    "outputId": "8042aa9d-b9b7-4e7d-91b1-a211adfaf282",
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Heater\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.441601630311844,\n        \"min\": -2.886579864025406e-14,\n        \"max\": 99.99999999999993,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          95.99999999999866,\n          99.99999999999993,\n          17.33333333332865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reactor_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.43620991973503,\n        \"min\": 0.0,\n        \"max\": 80.00000000000001,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          79.99999999999999,\n          0.0,\n          80.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reactor_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.311214554257518,\n        \"min\": 0.0,\n        \"max\": 80.00000000000053,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          80.0000000000004,\n          53.33333333333329,\n          80.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Still\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.26923000951273,\n        \"min\": 0.0,\n        \"max\": 160.0000000000086,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          120.0,\n          5.115907697472721e-12,\n          160.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-7fca9fa4-c90a-43cd-a105-dc3b4f4a1483\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heater</th>\n",
       "      <th>Reactor_1</th>\n",
       "      <th>Reactor_2</th>\n",
       "      <th>Still</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.600000e+01</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.733333e+01</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.115908e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.886580e-14</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.115908e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.600000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>1.600000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>1.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fca9fa4-c90a-43cd-a105-dc3b4f4a1483')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-7fca9fa4-c90a-43cd-a105-dc3b4f4a1483 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-7fca9fa4-c90a-43cd-a105-dc3b4f4a1483');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-b3eb08d6-3870-48e4-9212-b3489ffc0b3a\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3eb08d6-3870-48e4-9212-b3489ffc0b3a')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-b3eb08d6-3870-48e4-9212-b3489ffc0b3a button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "          Heater  Reactor_1  Reactor_2         Still\n",
       "0   0.000000e+00       80.0  80.000000  0.000000e+00\n",
       "1   9.600000e+01       80.0  80.000000  0.000000e+00\n",
       "2   0.000000e+00       80.0  80.000000  0.000000e+00\n",
       "3   0.000000e+00       80.0  80.000000  0.000000e+00\n",
       "4   0.000000e+00       80.0  80.000000  0.000000e+00\n",
       "5   1.733333e+01       80.0  80.000000  5.115908e-12\n",
       "6  -2.886580e-14       80.0  80.000000  5.115908e-13\n",
       "7   1.000000e+02       80.0  80.000000  0.000000e+00\n",
       "8   0.000000e+00       80.0  80.000000  1.600000e+02\n",
       "9   0.000000e+00       80.0  80.000000  1.600000e+01\n",
       "10  0.000000e+00       80.0  80.000000  0.000000e+00\n",
       "11  0.000000e+00       80.0  53.333333  1.600000e+02\n",
       "12  0.000000e+00       80.0  53.333333  1.600000e+01\n",
       "13  0.000000e+00       80.0  40.000000  0.000000e+00\n",
       "14  0.000000e+00        0.0   0.000000  1.200000e+02\n",
       "15  0.000000e+00        0.0   0.000000  1.200000e+01\n",
       "16  0.000000e+00        0.0   0.000000  0.000000e+00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    [[solution[\"Q\"][j, t] for j in UNITS] for t in TIME], columns=UNITS, index=TIME\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xkyJKde9-hHA",
    "pycharm": {}
   },
   "source": [
    "### Gannt chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "bt-pftr7-hHA",
    "outputId": "54c0661b-4b04-4add-c655-8ccedaeac832",
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "gap = H / 500\n",
    "idx = 1\n",
    "lbls = []\n",
    "ticks = []\n",
    "for j in sorted(UNITS):\n",
    "    idx -= 1\n",
    "    for i in sorted(I[j]):\n",
    "        idx -= 1\n",
    "        ticks.append(idx)\n",
    "        lbls.append(\"{0:s} -> {1:s}\".format(j, i))\n",
    "        plt.plot([0, H], [idx, idx], lw=20, alpha=0.3, color=\"y\")\n",
    "        for t in TIME:\n",
    "            if solution[\"W\"][i, j, t] > 0:\n",
    "                plt.plot(\n",
    "                    [t + gap, t + p[i] - gap],\n",
    "                    [idx, idx],\n",
    "                    \"b\",\n",
    "                    lw=20,\n",
    "                    solid_capstyle=\"butt\",\n",
    "                )\n",
    "                txt = \"{0:.2f}\".format(solution[\"B\"][i, j, t])\n",
    "                plt.text(\n",
    "                    t + p[i] / 2,\n",
    "                    idx,\n",
    "                    txt,\n",
    "                    color=\"white\",\n",
    "                    weight=\"bold\",\n",
    "                    ha=\"center\",\n",
    "                    va=\"center\",\n",
    "                )\n",
    "plt.xlim(0, H)\n",
    "plt.gca().set_yticks(ticks)\n",
    "plt.gca().set_yticklabels(lbls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gGEBxjVP-hHB",
    "pycharm": {}
   },
   "source": [
    "## Trace of events and states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cKT6FD6q-hHB",
    "outputId": "25099353-4fa3-4eb8-80cb-ff50f0b16c51",
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Starting Conditions\n",
      "    Initial Inventories:\n",
      "        Feed_B       500.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       500.0 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       500.0 kg\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 0 hr\n",
      "    Instructions:\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 40.0 kg from Feed_B to Reactor_1\n",
      "        Transfer 40.0 kg from Feed_C to Reactor_1\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 40.0 kg from Feed_B to Reactor_2\n",
      "        Transfer 40.0 kg from Feed_C to Reactor_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       420.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       500.0 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       420.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 1 hr\n",
      "    Instructions:\n",
      "        Assign Heater with capacity 100 kg to task Heating for 1 hours\n",
      "        Transfer 95.99999999999866 kg from Feed_A to Heater\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       420.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       404.0 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       420.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater performs the Heating task with a 96.00 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_1 performs the Reaction_1 task with a 80.00 kg batch for hour 2.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_1 task with a 80.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 2 hr\n",
      "    Instructions:\n",
      "        Transfer 95.99999999999866 kg from Heater to Hot_A\n",
      "        Transfer 80.0 kg from Reactor_1 to Int_BC\n",
      "        Transfer 80.0 kg from Reactor_2 to Int_BC\n",
      "        Release Heater from Heating\n",
      "        Release Reactor_1 from Reaction_1\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 48.0 kg from Int_BC to Reactor_1\n",
      "        Transfer 32.0 kg from Hot_A to Reactor_1\n",
      "        Release Reactor_2 from Reaction_1\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 48.0 kg from Int_BC to Reactor_2\n",
      "        Transfer 32.0 kg from Hot_A to Reactor_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       420.0 kg\n",
      "        Int_BC        64.0 kg\n",
      "        Hot_A         32.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       404.0 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       420.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 3 hr\n",
      "    Instructions:\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       420.0 kg\n",
      "        Int_BC        64.0 kg\n",
      "        Hot_A         32.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       404.0 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       420.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 2.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 80.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 4 hr\n",
      "    Instructions:\n",
      "        Transfer 32.0 kg from Reactor_1 to Product_1\n",
      "        Transfer 48.0 kg from Reactor_1 to Int_AB\n",
      "        Transfer 32.0 kg from Reactor_2 to Product_1\n",
      "        Transfer 48.0 kg from Reactor_2 to Int_AB\n",
      "        Release Reactor_1 from Reaction_2\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 5.5604232329642404e-12 kg from Int_AB to Reactor_1\n",
      "        Transfer 1.3901058082410601e-12 kg from Feed_C to Reactor_1\n",
      "        Transfer 47.99999999999708 kg from Int_BC to Reactor_1\n",
      "        Transfer 31.999999999998057 kg from Hot_A to Reactor_1\n",
      "        Release Reactor_2 from Reaction_2\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 63.9999999999999 kg from Int_AB to Reactor_2\n",
      "        Transfer 15.999999999999975 kg from Feed_C to Reactor_2\n",
      "        Transfer 3.165467887811246e-13 kg from Int_BC to Reactor_2\n",
      "        Transfer 2.1103119252074978e-13 kg from Hot_A to Reactor_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       420.0 kg\n",
      "        Int_BC        16.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1     64.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       404.0 kg\n",
      "        Int_AB        32.0 kg\n",
      "        Feed_C       404.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 5 hr\n",
      "    Instructions:\n",
      "        Transfer 6.9505290412053e-12 kg from Reactor_1 to Impure_E\n",
      "        Transfer 79.99999999999987 kg from Reactor_2 to Impure_E\n",
      "        Assign Heater with capacity 100 kg to task Heating for 1 hours\n",
      "        Transfer 17.33333333332865 kg from Feed_A to Heater\n",
      "        Release Reactor_1 from Reaction_3\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 3.47526452060265e-12 kg from Feed_B to Reactor_1\n",
      "        Transfer 3.47526452060265e-12 kg from Feed_C to Reactor_1\n",
      "        Release Reactor_2 from Reaction_3\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 40.0 kg from Feed_B to Reactor_2\n",
      "        Transfer 40.0 kg from Feed_C to Reactor_2\n",
      "        Assign Still with capacity 200 kg to task Separation for 2 hours\n",
      "        Transfer 5.115907697472721e-12 kg from Impure_E to Still\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       380.0 kg\n",
      "        Int_BC        16.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E      80.0 kg\n",
      "        Product_1     64.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       386.7 kg\n",
      "        Int_AB        32.0 kg\n",
      "        Feed_C       364.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater performs the Heating task with a 17.33 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_1 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Still performs the Separation task with a 0.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 6 hr\n",
      "    Instructions:\n",
      "        Transfer 17.33333333332865 kg from Heater to Hot_A\n",
      "        Transfer 31.999999999998057 kg from Reactor_1 to Product_1\n",
      "        Transfer 47.99999999999708 kg from Reactor_1 to Int_AB\n",
      "        Transfer 2.1103119252074978e-13 kg from Reactor_2 to Product_1\n",
      "        Transfer 3.165467887811246e-13 kg from Reactor_2 to Int_AB\n",
      "        Transfer 4.604316927725449e-12 kg from Still to Product_2\n",
      "        Release Heater from Heating\n",
      "        Release Reactor_1 from Reaction_2\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 39.99999999999569 kg from Feed_B to Reactor_1\n",
      "        Transfer 39.99999999999569 kg from Feed_C to Reactor_1\n",
      "        Transfer 1.0004441719502207e-12 kg from Int_BC to Reactor_1\n",
      "        Transfer 6.669627813001472e-13 kg from Hot_A to Reactor_1\n",
      "        Release Reactor_2 from Reaction_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC        16.0 kg\n",
      "        Hot_A         17.3 kg\n",
      "        Impure_E      80.0 kg\n",
      "        Product_1     96.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       386.7 kg\n",
      "        Int_AB        80.0 kg\n",
      "        Feed_C       324.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Still performs the Separation task with a 0.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 7 hr\n",
      "    Instructions:\n",
      "        Transfer 6.9505290412053e-12 kg from Reactor_1 to Int_BC\n",
      "        Transfer 80.0 kg from Reactor_2 to Int_BC\n",
      "        Transfer 5.115907697472721e-13 kg from Still to Int_AB\n",
      "        Assign Heater with capacity 100 kg to task Heating for 1 hours\n",
      "        Transfer 99.99999999999993 kg from Feed_A to Heater\n",
      "        Release Reactor_1 from Reaction_1\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 5.563549621001585e-12 kg from Int_AB to Reactor_1\n",
      "        Transfer 1.3908874052503963e-12 kg from Feed_C to Reactor_1\n",
      "        Release Reactor_2 from Reaction_1\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 64.0 kg from Int_AB to Reactor_2\n",
      "        Transfer 16.0 kg from Feed_C to Reactor_2\n",
      "        Release Still from Separation\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC        96.0 kg\n",
      "        Hot_A         17.3 kg\n",
      "        Impure_E      80.0 kg\n",
      "        Product_1     96.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB        16.0 kg\n",
      "        Feed_C       308.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater performs the Heating task with a 100.00 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_1 performs the Reaction_3 task with a 80.00 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_2 performs the Reaction_3 task with a 80.00 kg batch for hour 1.000000 of 1.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 8 hr\n",
      "    Instructions:\n",
      "        Transfer 99.99999999999993 kg from Heater to Hot_A\n",
      "        Transfer 79.99999999999137 kg from Reactor_1 to Int_BC\n",
      "        Transfer 6.954437026251981e-12 kg from Reactor_1 to Impure_E\n",
      "        Transfer 6.669627813001472e-13 kg from Reactor_1 to Product_1\n",
      "        Transfer 1.0004441719502207e-12 kg from Reactor_1 to Int_AB\n",
      "        Transfer 80.0 kg from Reactor_2 to Impure_E\n",
      "        Release Heater from Heating\n",
      "        Release Reactor_1 from Reaction_1\n",
      "        Release Reactor_1 from Reaction_3\n",
      "        Release Reactor_1 from Reaction_2\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 1.2663203818874536e-12 kg from Int_AB to Reactor_1\n",
      "        Transfer 3.165800954718634e-13 kg from Feed_C to Reactor_1\n",
      "        Transfer 47.999999999999055 kg from Int_BC to Reactor_1\n",
      "        Transfer 31.99999999999937 kg from Hot_A to Reactor_1\n",
      "        Release Reactor_2 from Reaction_3\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 47.99999999999995 kg from Int_BC to Reactor_2\n",
      "        Transfer 31.999999999999968 kg from Hot_A to Reactor_2\n",
      "        Assign Still with capacity 200 kg to task Separation for 2 hours\n",
      "        Transfer 160.0000000000086 kg from Impure_E to Still\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC        80.0 kg\n",
      "        Hot_A         53.3 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1     96.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB        16.0 kg\n",
      "        Feed_C       308.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Still performs the Separation task with a 160.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 9 hr\n",
      "    Instructions:\n",
      "        Transfer 1.582900477359317e-12 kg from Reactor_1 to Impure_E\n",
      "        Transfer 144.00000000000776 kg from Still to Product_2\n",
      "        Release Reactor_1 from Reaction_3\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 7.94114773938759e-13 kg from Feed_B to Reactor_1\n",
      "        Transfer 7.94114773938759e-13 kg from Feed_C to Reactor_1\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_1 for 2 hours\n",
      "        Transfer 3.9790393202565614e-14 kg from Feed_B to Reactor_2\n",
      "        Transfer 3.9790393202565614e-14 kg from Feed_C to Reactor_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC        80.0 kg\n",
      "        Hot_A         53.3 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1     96.0 kg\n",
      "        Product_2    144.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB        16.0 kg\n",
      "        Feed_C       308.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_1 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Still performs the Separation task with a 16.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 10 hr\n",
      "    Instructions:\n",
      "        Transfer 31.99999999999937 kg from Reactor_1 to Product_1\n",
      "        Transfer 47.999999999999055 kg from Reactor_1 to Int_AB\n",
      "        Transfer 31.999999999999968 kg from Reactor_2 to Product_1\n",
      "        Transfer 47.99999999999995 kg from Reactor_2 to Int_AB\n",
      "        Transfer 16.000000000000863 kg from Still to Int_AB\n",
      "        Release Reactor_1 from Reaction_2\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 63.99999999999874 kg from Int_AB to Reactor_1\n",
      "        Transfer 15.999999999999686 kg from Feed_C to Reactor_1\n",
      "        Release Reactor_2 from Reaction_2\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 64.0 kg from Int_AB to Reactor_2\n",
      "        Transfer 16.0 kg from Feed_C to Reactor_2\n",
      "        Release Still from Separation\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC        80.0 kg\n",
      "        Hot_A         53.3 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    160.0 kg\n",
      "        Product_2    144.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       276.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_3 task with a 80.00 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_2 performs the Reaction_3 task with a 80.00 kg batch for hour 1.000000 of 1.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 11 hr\n",
      "    Instructions:\n",
      "        Transfer 1.588229547877518e-12 kg from Reactor_1 to Int_BC\n",
      "        Transfer 79.99999999999842 kg from Reactor_1 to Impure_E\n",
      "        Transfer 7.958078640513123e-14 kg from Reactor_2 to Int_BC\n",
      "        Transfer 80.0 kg from Reactor_2 to Impure_E\n",
      "        Release Reactor_1 from Reaction_1\n",
      "        Release Reactor_1 from Reaction_3\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 48.0 kg from Int_BC to Reactor_1\n",
      "        Transfer 32.0 kg from Hot_A to Reactor_1\n",
      "        Release Reactor_2 from Reaction_1\n",
      "        Release Reactor_2 from Reaction_3\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_2 for 2 hours\n",
      "        Transfer 31.999999999999975 kg from Int_BC to Reactor_2\n",
      "        Transfer 21.333333333333318 kg from Hot_A to Reactor_2\n",
      "        Assign Still with capacity 200 kg to task Separation for 2 hours\n",
      "        Transfer 160.0 kg from Impure_E to Still\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    160.0 kg\n",
      "        Product_2    144.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       276.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 1.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 53.33 kg batch for hour 1.000000 of 2.000000\n",
      "        Still performs the Separation task with a 160.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 12 hr\n",
      "    Instructions:\n",
      "        Transfer 144.0 kg from Still to Product_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    160.0 kg\n",
      "        Product_2    288.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       276.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_2 task with a 80.00 kg batch for hour 2.000000 of 2.000000\n",
      "        Reactor_2 performs the Reaction_2 task with a 53.33 kg batch for hour 2.000000 of 2.000000\n",
      "        Still performs the Separation task with a 16.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 13 hr\n",
      "    Instructions:\n",
      "        Transfer 32.0 kg from Reactor_1 to Product_1\n",
      "        Transfer 48.0 kg from Reactor_1 to Int_AB\n",
      "        Transfer 21.333333333333318 kg from Reactor_2 to Product_1\n",
      "        Transfer 31.999999999999975 kg from Reactor_2 to Int_AB\n",
      "        Transfer 16.0 kg from Still to Int_AB\n",
      "        Release Reactor_1 from Reaction_2\n",
      "        Assign Reactor_1 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 64.0 kg from Int_AB to Reactor_1\n",
      "        Transfer 16.0 kg from Feed_C to Reactor_1\n",
      "        Release Reactor_2 from Reaction_2\n",
      "        Assign Reactor_2 with capacity 80 kg to task Reaction_3 for 1 hours\n",
      "        Transfer 31.999999999999968 kg from Int_AB to Reactor_2\n",
      "        Transfer 7.999999999999992 kg from Feed_C to Reactor_2\n",
      "        Release Still from Separation\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    213.3 kg\n",
      "        Product_2    288.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       252.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Reactor_1 performs the Reaction_3 task with a 80.00 kg batch for hour 1.000000 of 1.000000\n",
      "        Reactor_2 performs the Reaction_3 task with a 40.00 kg batch for hour 1.000000 of 1.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 14 hr\n",
      "    Instructions:\n",
      "        Transfer 80.0 kg from Reactor_1 to Impure_E\n",
      "        Transfer 39.99999999999996 kg from Reactor_2 to Impure_E\n",
      "        Release Reactor_1 from Reaction_3\n",
      "        Release Reactor_2 from Reaction_3\n",
      "        Assign Still with capacity 200 kg to task Separation for 2 hours\n",
      "        Transfer 120.0 kg from Impure_E to Still\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    213.3 kg\n",
      "        Product_2    288.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       252.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Still performs the Separation task with a 120.00 kg batch for hour 1.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 15 hr\n",
      "    Instructions:\n",
      "        Transfer 108.0 kg from Still to Product_2\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    213.3 kg\n",
      "        Product_2    396.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB         0.0 kg\n",
      "        Feed_C       252.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Still performs the Separation task with a 12.00 kg batch for hour 2.000000 of 2.000000\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Time = 16 hr\n",
      "    Instructions:\n",
      "        Transfer 12.0 kg from Still to Int_AB\n",
      "        Release Still from Separation\n",
      "\n",
      "    Inventories are now:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    213.3 kg\n",
      "        Product_2    396.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB        12.0 kg\n",
      "        Feed_C       252.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "Final Conditions\n",
      "    Final Inventories:\n",
      "        Feed_B       340.0 kg\n",
      "        Int_BC         0.0 kg\n",
      "        Hot_A          0.0 kg\n",
      "        Impure_E       0.0 kg\n",
      "        Product_1    213.3 kg\n",
      "        Product_2    396.0 kg\n",
      "        Feed_A       286.7 kg\n",
      "        Int_AB        12.0 kg\n",
      "        Feed_C       252.0 kg\n"
     ]
    }
   ],
   "source": [
    "sep = \"\\n--------------------------------------------------------------------------------------------\\n\"\n",
    "print(sep)\n",
    "print(\"Starting Conditions\")\n",
    "print(\"    Initial Inventories:\")\n",
    "for s in STATES.keys():\n",
    "    print(\"        {0:10s}  {1:6.1f} kg\".format(s, STATES[s][\"initial\"]))\n",
    "\n",
    "units = {j: {\"assignment\": \"None\", \"t\": 0} for j in UNITS}\n",
    "\n",
    "for t in TIME:\n",
    "    print(sep)\n",
    "    print(\"Time =\", t, \"hr\")\n",
    "    print(\"    Instructions:\")\n",
    "    for j in UNITS:\n",
    "        units[j][\"t\"] += 1\n",
    "        # transfer from unit to states\n",
    "        for i in I[j]:\n",
    "            for s in S_[i]:\n",
    "                if t - P[(i, s)] >= 0:\n",
    "                    amt = (\n",
    "                        rho_[(i, s)]\n",
    "                        * solution[\"B\"][i, j, max(TIME[TIME <= t - P[(i, s)]])]\n",
    "                    )\n",
    "                    if amt > 0:\n",
    "                        print(\"        Transfer\", amt, \"kg from\", j, \"to\", s)\n",
    "    for j in UNITS:\n",
    "        # release units from tasks\n",
    "        for i in I[j]:\n",
    "            if t - p[i] >= 0:\n",
    "                if solution[\"W\"][i, j, max(TIME[TIME <= t - p[i]])] > 0:\n",
    "                    print(\"        Release\", j, \"from\", i)\n",
    "                    units[j][\"assignment\"] = \"None\"\n",
    "                    units[j][\"t\"] = 0\n",
    "        # assign units to tasks\n",
    "        for i in I[j]:\n",
    "            if solution[\"W\"][i, j, t] > 0:\n",
    "                print(\n",
    "                    \"        Assign\",\n",
    "                    j,\n",
    "                    \"with capacity\",\n",
    "                    Bmax[(i, j)],\n",
    "                    \"kg to task\",\n",
    "                    i,\n",
    "                    \"for\",\n",
    "                    p[i],\n",
    "                    \"hours\",\n",
    "                )\n",
    "                units[j][\"assignment\"] = i\n",
    "                units[j][\"t\"] = 1\n",
    "        # transfer from states to starting tasks\n",
    "        for i in I[j]:\n",
    "            for s in S[i]:\n",
    "                amt = rho[(i, s)] * solution[\"B\"][i, j, t]\n",
    "                if amt > 0:\n",
    "                    print(\"        Transfer\", amt, \"kg from\", s, \"to\", j)\n",
    "    print(\"\\n    Inventories are now:\")\n",
    "    for s in STATES.keys():\n",
    "        print(\"        {0:10s}  {1:6.1f} kg\".format(s, solution[\"S\"][s, t]))\n",
    "    print(\"\\n    Unit Assignments are now:\")\n",
    "    for j in UNITS:\n",
    "        if units[j][\"assignment\"] != \"None\":\n",
    "            fmt = \"        {0:s} performs the {1:s} task with a {2:.2f} kg batch for hour {3:f} of {4:f}\"\n",
    "            i = units[j][\"assignment\"]\n",
    "            print(fmt.format(j, i, solution[\"Q\"][j, t], units[j][\"t\"], p[i]))\n",
    "\n",
    "print(sep)\n",
    "print(\"Final Conditions\")\n",
    "print(\"    Final Inventories:\")\n",
    "for s in STATES.keys():\n",
    "    print(\"        {0:10s}  {1:6.1f} kg\".format(s, solution[\"S\"][s, H]))"
   ]
  }
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