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Copy pathUncapacitated_Lot_Sizing_With_Setups_Mod2.py
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Uncapacitated_Lot_Sizing_With_Setups_Mod2.py
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from mip import *
import time
import re
from io import StringIO
import subprocess
#Récupère les données de l'instance
def get_data(datafileName):
with open(datafileName, "r") as file:
line = file.readline()
lineTab = line.split()
nbPeriodes = int(lineTab[0])
line = file.readline()
lineTab = line.split()
demandes = []
for i in range(nbPeriodes):
demandes.append(int(lineTab[i]))
line = file.readline()
lineTab = line.split()
couts = []
for i in range(nbPeriodes):
couts.append(int(lineTab[i]))
line = file.readline()
lineTab = line.split()
cfixes = []
for i in range(nbPeriodes):
cfixes.append(int(lineTab[i]))
line = file.readline()
lineTab = line.split()
cstock = int(lineTab[0])
return nbPeriodes, demandes, couts, cfixes, cstock
# Récupère le nombre de noeuds utilisés dans le modèle grâce à l'output du solveur
def get_node_count(output):
search_result = re.search(r"Search completed", output)
partial_result = re.search(r"Partial search", output)
if search_result:
remaining_text = output[search_result.end():]
values = re.findall(r'\b(\S+)\b', remaining_text)
if len(values) >= 2:
number = values[7]
return number
elif partial_result:
remaining_text = output[partial_result.end():]
values = re.findall(r'\b(\S+)\b', remaining_text)
if len(values) >= 2:
number = values[10]
return number
else:
return "ERROR"
# Résout le modèle (type = "int" ou "cont")
def resolve_modele(type, nbPeriodes, demandes, couts, cfixes, cstock):
model = Model(name = "ULS", solver_name="CBC")
# Variables
y = [model.add_var(name="Y" + str(i), lb=0, ub=1, var_type=BINARY) for i in range(nbPeriodes)]
x = [[model.add_var(name="X(" + str(i) + "," + str(j) + ")", lb=0, ub=1, var_type=BINARY) for i in range(nbPeriodes)] for j in range(nbPeriodes)]
#Fonction objectif
model.objective = minimize(xsum(couts[i] * x[i][j] * demandes[j] for j in range(nbPeriodes) for i in range(nbPeriodes)) + xsum(cfixes[i] * y[i] for i in range(nbPeriodes)) + xsum(cstock * x[i][j] * (j - i) * demandes[j] for i in range(nbPeriodes) for j in range(i+1, nbPeriodes)))
#Contraintes
M = 100000
for j in range(nbPeriodes):
model.add_constr(xsum(x[i][j] * demandes[j] for i in range(j+1)) >= demandes[j])
for i in range (nbPeriodes):
model.add_constr(xsum(x[i][j] * demandes[j] for j in range(i, nbPeriodes)) <= M* y[i])
model.write("model2.lp")
start = time.perf_counter()
status = model.optimize(max_seconds=180)
runtime = time.perf_counter() - start
output = "ERROR"
if type == "int":
commande = "python3 optimize.py 2"
resultat = subprocess.run(commande, shell=True, capture_output=True, text=True)
if resultat.returncode == 0:
output = resultat.stdout
node_count = get_node_count(output)
return model, status, runtime, node_count, y, x
# Affiche les résultats pour une instance dans le terminal
def lot_sizing_resolve(datafileName):
nbPeriodes, demandes, couts, cfixes, cstock = get_data(datafileName)
model_relax, status_relax, runtime_relax, node_count_relax, y_relax, x_relax = resolve_modele("cont", nbPeriodes, demandes, couts, cfixes, cstock)
model, status, runtime, node_count, y, x = resolve_modele("int", nbPeriodes, demandes, couts, cfixes, cstock)
print("---------- " + datafileName + " ----------")
print("Valeur de la relaxation linéaire calculée : ", model_relax.objective_value)
if status == OptimizationStatus.OPTIMAL:
print("Status de la résolution: OPTIMAL")
elif status == OptimizationStatus.FEASIBLE:
print("Status de la résolution: TEMPS LIMITE et SOLUTION REALISABLE CALCULEE")
elif status == OptimizationStatus.NO_SOLUTION_FOUND:
print("Status de la résolution: TEMPS LIMITE et AUCUNE SOLUTION CALCULEE")
elif status == OptimizationStatus.INFEASIBLE or status == OptimizationStatus.INT_INFEASIBLE:
print("Status de la résolution: IRREALISABLE")
elif status == OptimizationStatus.UNBOUNDED:
print("Status de la résolution: NON BORNE")
if model.num_solutions>0:
print("Valeur de la fonction objectif de la solution calculée : ", model.objective_value)
print("Écart % avec la relaxation linéaire : ", 100*(model.objective_value - model_relax.objective_value)/model_relax.objective_value, "%")
print("Mois de production :")
for i in range(nbPeriodes):
print(int(y[i].x), end=' ')
print()
print("Quantités produites :")
for i in range(nbPeriodes):
res = xsum(x[i][j] * demandes[j] for j in range(nbPeriodes))
print(int(res.x), end=' ')
print()
print("Quantités stockées :")
for i in range(nbPeriodes):
res_stock = xsum(x[k][j] * demandes[j] for k in range(i+1) for j in range(nbPeriodes)) - sum(demandes[j] for j in range(i+1))
print(int(res_stock.x), end=' ')
print()
print("Nombre de noeuds : ", node_count)
print("Temps de résolution: ", runtime, "s")
else:
print("Pas de solution calculée")
# Ecris les résultats pour une instance dans le fichier results_1.txt
def lot_sizing_resolve_to_file(datafileName):
nbPeriodes, demandes, couts, cfixes, cstock = get_data(datafileName)
model_relax, status_relax, runtime_relax, node_count_relax, y_relax, x_relax = resolve_modele("cont", nbPeriodes, demandes, couts, cfixes, cstock)
model, status, runtime, node_count, y, x = resolve_modele("int", nbPeriodes, demandes, couts, cfixes, cstock)
file = open("results_2.txt", "a")
file.write("---------- " + datafileName + " ----------\n")
file.write("Valeur de la relaxation linéaire calculée : " + str(model_relax.objective_value) + "\n")
if status == OptimizationStatus.OPTIMAL:
file.write("Status de la résolution: OPTIMAL\n")
elif status == OptimizationStatus.FEASIBLE:
file.write("Status de la résolution: TEMPS LIMITE et SOLUTION REALISABLE CALCULEE\n")
elif status == OptimizationStatus.NO_SOLUTION_FOUND:
file.write("Status de la résolution: TEMPS LIMITE et AUCUNE SOLUTION CALCULEE\n")
elif status == OptimizationStatus.INFEASIBLE or status == OptimizationStatus.INT_INFEASIBLE:
file.write("Status de la résolution: IRREALISABLE\n")
elif status == OptimizationStatus.UNBOUNDED:
file.write("Status de la résolution: NON BORNE\n")
if model.num_solutions>0:
file.write("Valeur de la fonction objectif de la solution calculée : " + str(model.objective_value) + "\n")
file.write("Écart % avec la relaxation linéaire : " + str(100*(model.objective_value - model_relax.objective_value)/model_relax.objective_value) + "%\n")
file.write("Mois de production :\n")
for i in range(nbPeriodes):
file.write(str(int(y[i].x)) + " ")
file.write("\n")
file.write("Quantités produites :\n")
for i in range(nbPeriodes):
res = xsum(x[i][j] * demandes[j] for j in range(nbPeriodes))
file.write(str(int(res.x)) + " ")
file.write("\n")
file.write("Quantités stockées :\n")
for i in range(nbPeriodes):
res_stock = xsum(x[k][j] * demandes[j] for k in range(i+1) for j in range(nbPeriodes)) - sum(demandes[j] for j in range(i+1))
file.write(str(int(res_stock.x)) + " ")
file.write("\n")
file.write("Nombre de noeuds : " + str(node_count) + "\n")
file.write("Temps de résolution: " + str(runtime) + "s\n")
else:
file.write("Pas de solution calculée\n")
file.write("\n")
file.close()
# Rempli le fichier results_2.txt des résultats de toutes les instances
def results_file():
file = open("results_2.txt", "w")
file.close
lot_sizing_resolve_to_file('Instances_ULS/Instance21.1.txt')
for i in range(1,11):
lot_sizing_resolve_to_file('Instances_ULS/Instance60.' + str(i) + '.txt')
for i in range(1,11):
lot_sizing_resolve_to_file('Instances_ULS/Instance90.' + str(i) + '.txt')
for i in range(1,11):
lot_sizing_resolve_to_file('Instances_ULS/Instance120.' + str(i) + '.txt')
lot_sizing_resolve_to_file('Instances_ULS/Toy_Instance.txt')
lot_sizing_resolve("Instances_ULS/Instance21.1.txt")
#results_file() #Attention, supprime le fichier results_2.txt avant de réécrire dedans