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Fastest_Food_Finished.py
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Fastest_Food_Finished.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Mar 8 12:52:58 2022
@author: email
"""
import collections
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import random
from ortools.sat.python import cp_model
import time
start_time = time.time()
attrs = {0: {"kid": True, "prep": 5, "cook": 20},
1: {"kid": False, "prep": 15, "cook": 15},
2: {"kid": True, "prep": 20, "cook": 25},
3: {"kid": True, "prep": 5, "cook": 55},
4: {"kid": True, "prep": 40, "cook": 45},
5: {"kid": False, "prep": 45, "cook": 35},
6: {"kid": False, "prep": 50, "cook": 55},
7: {"kid": False, "prep": 5, "cook": 10},
8: {"kid": True, "prep": 10, "cook": 10},
9: {"kid": True, "prep": 55, "cook": 30},
10: {"kid": False, "prep": 25, "cook": 20},
11: {"kid": True, "prep": 30, "cook": 40},
12: {"kid": False, "prep": 15, "cook": 5},
13: {"kid": True, "prep": 35, "cook": 15}}
def getJobs(attrs):
jobs=list()
for key, value in attrs.items():
jobs.append([[(value['prep']//5,0),(value['prep']//5,1)],[(value['cook']//5,2),(value['cook']//5,3)]])
return jobs
jobs_list=getJobs(attrs)
class SolutionPrinter(cp_model.CpSolverSolutionCallback):
"""Print intermediate solutions."""
def __init__(self):
cp_model.CpSolverSolutionCallback.__init__(self)
self.__solution_count = 0
def on_solution_callback(self):
"""Called at each new solution."""
print('Solution %i, time = %f s, objective = %i' %
(self.__solution_count, self.WallTime(), self.ObjectiveValue()))
self.__solution_count += 1
def fastest_food(jobs):
# Data part.
num_jobs = len(jobs)
all_jobs = range(num_jobs)
num_machines = 4
all_machines = range(num_machines)
# Model the flexible jobshop problem.
model = cp_model.CpModel()
#total time of all the tasks at hand
horizon = 0
for job in jobs:
for task in job:
horizon+=task[0][0]
print('Horizon = %i' % horizon)
# Global storage of variables.
intervals_per_resources = collections.defaultdict(list)
starts = {} # indexed by (job_id, task_id).
presences = {} # indexed by (job_id, task_id, alt_id).
job_ends = []
# Scan the jobs and create the relevant variables and intervals.
for job_id in all_jobs:
job = jobs[job_id]
num_tasks = len(job)
previous_end = None
for task_id in range(num_tasks):
task = job[task_id]
min_duration = task[0][0]
max_duration = task[0][0]
#in our case min and max duration are the same
#because both stations are equally fast.
num_alternatives = len(task)
all_alternatives = range(num_alternatives)
# Create main interval for the task.
suffix_name = '_job%i_task%i' % (job_id, task_id)
start = model.NewIntVar(0, horizon, 'start' + suffix_name)
duration = model.NewIntVar(min_duration, max_duration,
'duration' + suffix_name)
#this interval is a workaround becuase the model
#expects the times on the two alternative machines
#to be distinct.
end = model.NewIntVar(0, horizon, 'end' + suffix_name)
interval = model.NewIntervalVar(start, duration, end,
'interval' + suffix_name)
# Store the start for the solution.
starts[(job_id, task_id)] = start
# Add precedence with previous task in the same job,
# i.e. make sure the preparation(0)
# happens before cooking (1).
if previous_end is not None:
model.Add(start >= previous_end)
previous_end = end
#Create alternative intervals.
if num_alternatives > 1:
l_presences = []
for alt_id in all_alternatives:
alt_suffix = '_j%i_t%i_a%i' % (job_id, task_id, alt_id)
l_presence = model.NewBoolVar('presence' + alt_suffix)
l_start = model.NewIntVar(0, horizon, 'start' + alt_suffix)
l_duration = task[alt_id][0]
l_end = model.NewIntVar(0, horizon, 'end' + alt_suffix)
l_interval = model.NewOptionalIntervalVar(
l_start, l_duration, l_end, l_presence,
'interval' + alt_suffix)
l_presences.append(l_presence)
# Link the master variables with the local ones.
model.Add(start == l_start).OnlyEnforceIf(l_presence)
model.Add(duration == l_duration).OnlyEnforceIf(l_presence)
model.Add(end == l_end).OnlyEnforceIf(l_presence)
# Add the local interval to the right machine.
intervals_per_resources[task[alt_id][1]].append(l_interval)
# Store the presences for the solution.
presences[(job_id, task_id, alt_id)] = l_presence
# Select exactly one presence variable.
# This adds a bounded linear expression to the model,
# which is a bool in this case, and returns
# an instance of a constraint class.
model.Add(sum(l_presences) == 1)
else:
intervals_per_resources[task[0][1]].append(interval)
presences[(job_id, task_id, 0)] = model.NewConstant(1)
job_ends.append(previous_end)
# Create machines constraints.
for machine_id in all_machines:
intervals = intervals_per_resources[machine_id]
if len(intervals) > 1:
model.AddNoOverlap(intervals)
# make Makespan objective
makespan = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(makespan, job_ends)
model.Minimize(makespan)
# Solve model.
solver = cp_model.CpSolver()
solution_printer = SolutionPrinter()
status = solver.Solve(model, solution_printer)
# using this for easier plotting later
number_of_colors = len(jobs)
color = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])
for i in range(number_of_colors)]
plot_list=[]
for machine_id in all_machines:
if machine_id in [0,1]:
print('Preparation station', machine_id)
else:
print('Cooking station', machine_id-2)
for job_id in all_jobs:
for task_id in range(2):
start_value = solver.Value(starts[(job_id, task_id)])
machine = -1
duration = -1
selected = -1
for alt_id in range(2):
if solver.Value(presences[(job_id, task_id, alt_id)]):
duration = jobs[job_id][task_id][alt_id][0]
machine = jobs[job_id][task_id][alt_id][1]
selected = alt_id
if machine_id==machine:
#print(' Job_%i Task_%i starts at %i (duration %i)' %
#(job_id, task_id, start_value, duration))
plot_list.append(dict(Machine=machine, Job=job_id, Start=start_value, Finish=start_value+duration, Duration=duration, Color=color[job_id]))
#PRINT GANTT CHART
df=pd.DataFrame(plot_list)
proj_start = df.Start.min()
# minutes from project start to task start
df['start_num'] = (df.Start-proj_start)
# minutes from project start to end of tasks
df['end_num'] = (df.Finish-proj_start)
# mins between start and end of each task
df['mins_start_to_end'] = (df.end_num - df.start_num)
fig, ax = plt.subplots(1, figsize=(16,6))
# bars
ax.barh(df.Machine, df.mins_start_to_end, left=df.start_num, color=df.Color)
xticks_minor = np.arange(0, df.end_num.max()+1, 1)
yticks=[0,1,2,3]
yticks_labels=['Prep1', 'Prep2', 'Cook1', 'Cook2']
ax.set_yticks(yticks)
ax.set_xticks(xticks_minor, minor=True)
ax.set_yticklabels(yticks_labels)
plt.show()
#print sequences per machine ordered chronologically:
prep1=list()
prep2=list()
cook1=list()
cook2=list()
for dic in plot_list:
if dic['Machine']==0:
prep1.append((dic['Job'] ,dic['Finish'])) #job_id, start,
if dic['Machine']==1:
prep2.append((dic['Job'] ,dic['Finish'])) #job_id, start,
if dic['Machine']==2:
cook1.append((dic['Job'] ,dic['Finish'])) #job_id, start,
if dic['Machine']==3:
cook2.append((dic['Job'] ,dic['Finish'])) #job_id, start,
ordered_sequences=[prep1,prep2,cook1,cook2]
for x in ordered_sequences:
x.sort(key = lambda x: x[1])
if x==prep1:
print('Preparation station 1: ')
if x==prep2:
print('Preparation station 2: ')
if x==cook1:
print('Cooking station 1: ')
if x==cook2:
print('Cooking station 2: ')
for element in x:
print('\t Job {} finishing at time {}'.format(element[0],element[1]))
print('Solve status: %s' % solver.StatusName(status))
print('Optimal objective value: %i' % solver.ObjectiveValue())
print('Statistics')
#print(' - conflicts : %i' % solver.NumConflicts())
print(' - branches : %i' % solver.NumBranches())
#number of branches explored in a binary search tree
return time.time() - start_time
fastest_food(jobs_list)
print("--- %s seconds ---" % (time.time() - start_time))
#--------------TIME-------------
#generate datasets of various times and measure how well your model scales.
#dataset generator
# orders=[5,10,20,50,100,150,200, 350, 500] #500,1000,2000,5000,10000,50000,100000,1000000
# orders_and_times=list() #list of tupples w number of orders and times of execution
# for i in orders:
# print('Currently obtaining time for the order',i)
# dic=dict()
# for j in range(i):
# dic[j]={'kid':bool(random.getrandbits(42)), 'prep':random.randrange(5,60,5), 'cook':random.randrange(5,60,5)}
# t=fastest_food(getJobs(dic))
# orders_and_times.append((i,t))
# plt.plot(*zip(*orders_and_times))
# plt.show()