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solver.py
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solver.py
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#!/usr/bin/env python
# coding: utf-8
# In[22]:
import numpy as np
from docplex.cp.model import *
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import time
import math
# In[23]:
LARGE_SETUP_PENALTY = 65
SMALL_SETUP_PENALTY = 20
N_MACHINES = 4
N_JOBS = 15
NAME = 'J{}S10'.format(N_JOBS)
DATA_PATH = './data/{}.pkl'.format(NAME) # "./data/tectron1.dat"
# In[24]:
def cut_true_tardiness(z: float) -> float:
if z < 0:
return 0
else:
return z
def calc_tardiness(vars, jobs):
""" Calculates the tardiness.
:param sequences: list of lists of vars
:param jobs: contains the properties of jobs
:return:
"""
tardiness = 0.0
for m in range(N_MACHINES):
# as last_setup is initialized with -1 it will always add the large penalty to the first job
# to counteract this, we initialize the delay with the negative of that penalty
delay, last_setup = -LARGE_SETUP_PENALTY, -1
for j, job in enumerate(jobs):
# setup_equal = 1 if both setups are equal, else 0
# setup_equal = (job['type'] / last_setup) % 1
setup_equal = job['type'] == last_setup
# only add setup penalty if (setup_equal=0 - vars[j][m]=1)
delay += vars[j][m] * (job['pt'] + SMALL_SETUP_PENALTY)
delay += (vars[j][m] - setup_equal * vars[j][m]) * (LARGE_SETUP_PENALTY - SMALL_SETUP_PENALTY)
tardiness += vars[j][m] * max(0, delay - job['dd'])
# if vars[j][m]=1 --> last_setup= job['type'] else -1
last_setup = job['type'] * vars[j][m] + vars[j][m] - 1
return tardiness
def calc_number_large_setups(sequences):
""" Calculates the number of large setups.
:param sequences: list of lists of job properties
:return:
"""
n_setups = len(sequences)
for m in range(len(sequences)):
for i in range(1, len(sequences)[m]):
if sequences[m][i].type != sequences[m][i - 1].type:
n_setups += 1
return n_setups
def calc_makespan(vars, jobs):
""" Calculates the maximum makespan.
:param sequences: list of lists of vars
:param jobs: contains the properties of jobs
:return:
"""
makespan = [0 for _ in range(N_MACHINES)]
for m in range(N_MACHINES):
last_setup = -1
for j, job in enumerate(jobs):
# setup_equal = 1 if both setups are equal, else 0
# setup_equal = (job['type'] / last_setup) % 1
setup_equal = job['type'] == last_setup
# only add setup penalty if (setup_equal=0 - vars[j][m]=1)
makespan[m] += vars[j][m] * (job['pt'] + SMALL_SETUP_PENALTY)
makespan[m] += (vars[j][m] - setup_equal * vars[j][m]) * (LARGE_SETUP_PENALTY - SMALL_SETUP_PENALTY)
# if vars[j][m]=1 --> last_setup= job['type'] else -1
last_setup = job['type'] * vars[j][m] + vars[j][m] - 1
return max(makespan)
def evaluate(sequences):
goodness = calc_tardiness(sequences)
goodness += calc_makespan(sequences)
goodness += calc_number_large_setups(sequences)
return goodness
def load_data(path="tectron1.dat"):
if 'tectron' in path:
jobs = []
with open(path) as job_list_in:
for count, line in enumerate(job_list_in.readlines()):
id, due_date, type, p_time = line.split()
jobs.append({
'id': int(id), 'dd': float(due_date),
'type': int(type), 'pt': float(p_time)
})
else:
jobs = pd.read_pickle(path).to_dict('records')
return jobs
def init_solver(jobs):
mdl = CpoModel()
# add bin decision variables
stack = []
for j in range(len(jobs)):
row = [mdl.integer_var(0, 1, '{}:{}'.format(j, m)) for m in range(N_MACHINES)]
# every job needs to be mapped to exactly one machine constraint
mdl.add(mdl.sum(row) == 1)
stack.append(row)
return mdl, stack
def gantt(sequences, name='gantt', title=''):
"""
:param sequences: list of lists of job properties
:return:
"""
df = []
cumulated_pt_prior = [0. for _ in range(N_MACHINES)]
for m in range(len(sequences)):
for j, job in enumerate(sequences[m]):
df.append({
'Task': m,
'Start': cumulated_pt_prior[m],
'Finish': cumulated_pt_prior[m] + job['pt'],
'Delay': max(0, cumulated_pt_prior[m] + job['pt'] - job['dd']),
'Delta': job['pt'],
'Name': job['id']
})
cumulated_pt_prior[m] += job['pt'] + SMALL_SETUP_PENALTY
if j < len(sequences[m]) - 1 and sequences[m][j+1]['type'] != job['type']:
cumulated_pt_prior[m] += LARGE_SETUP_PENALTY - SMALL_SETUP_PENALTY
df = pd.DataFrame(df)
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Delay", #text="Name",
color_continuous_scale='Hot')#, range_color=[0, 20000], range_x=[0, 20000])
fig.update_layout(xaxis_title="Timesteps", yaxis_title="Machine")
# fig.update_traces(showlegend=False)
fig.update_annotations(align='center')
fig.layout.xaxis.type = 'linear'
fig.layout.yaxis.tickvals = [m for m in range(N_MACHINES)]
fig.update_layout(template="plotly_white")
fig.update_layout(title=title)
# fig.update_coloraxes(colorbar_tickmode='array')
# fig.update_coloraxes(colorbar_tickvals=[0,100])
# this is necessary for a int/float x axis
for i, d in enumerate(fig.data):
fig.data[0].x = df['Delta'].tolist()
return fig
# In[ ]:
jobs = load_data(path=DATA_PATH)[:N_JOBS]
solver, vars = init_solver(jobs)
#calculate sum of all job durations for every machine
# obj_var = [sum(vars[j][m] * job['pt']
# for j, job in enumerate(jobs)) for m in range(N_MACHINES)]
obj_var = calc_tardiness(vars, jobs) #+ calc_makespan(vars, jobs)
# minimize the max duration of all machines
solver.minimize(solver.min(obj_var))
msol = solver.solve()
sequences = [[] for _ in range(N_MACHINES)]
solution = msol.solution.var_solutions_dict
for i, key in enumerate(solution):
if i % 2:
continue
element = msol.solution.var_solutions_dict[key]
if element.value == 1:
job, machine = element.expr.name.split(":")
sequences[int(machine)].append(jobs[int(job)])
# In[ ]:
pd.DataFrame(sequences).to_pickle('./solutions/solver/{}.pkl'.format(NAME))
# In[ ]:
def calc_true_tardiness(sequence):
sum = 0.0
for m in range(N_MACHINES):
delay = 0.0
for j in range(len(sequence[m])):
sum += max(0, sequence[m][j]['pt'] + delay - sequence[m][j]['dd'])
delay += sequence[m][j]['pt'] + SMALL_SETUP_PENALTY
if j > 0 and sequence[m][j]['type'] != sequence[m][j-1]['type']:
delay += LARGE_SETUP_PENALTY - SMALL_SETUP_PENALTY
return sum
def calc_true_makespan(sequence):
makespan = [0. for _ in range(N_MACHINES)]
for m in range(N_MACHINES):
for j in range(len(sequence[m])):
makespan[m] += sequence[m][j]['pt'] + SMALL_SETUP_PENALTY
if j > 0 and sequence[m][j]['type'] != sequence[m][j-1]['type']:
makespan[m] += LARGE_SETUP_PENALTY - SMALL_SETUP_PENALTY
return max(makespan)
# In[ ]:
tardiness = calc_true_tardiness(sequences)
makespan = calc_true_makespan(sequences)
title = 'Makespan: {:.2f} | Tardiness {:.2f}'.format(makespan, tardiness)
fig = gantt(sequences, name='solver/gantt_{}'.format(NAME), title=title)
fig.write_image('figures/solver/gantt_{}.pdf'.format(NAME))