使用Python的Gurobi(优化迭代/模拟)

Question

我建立了一个具有随机随机需求(~N(100,40((的优化模型。我的优化模型本身给了我看起来很有希望的结果。现在，我下一步要做的是通过生成不同的正态分布随机需求来循环(模拟(整个优化问题。但它无法附加我需要的目标值，以便在模拟后得出预期值。

错误代码为：无法检索属性"objVal"任何帮助都将不胜感激！

for i in range(n_samples):
demand = np.random.normal(100, 40, 10)
capacity = np.tile(100, 10)
shortfall = 0 
m = gp.Model("Chaining Network")
Network = {}
Fulfillment = {}
Lostsale = {}
for i in range (nr_supplier):
for j in range (nr_retailer):
Fulfillment[i,j] = m.addVar()
Network[i,j] = m.addVar(vtype = GRB.BINARY)

for j in range (nr_retailer):       
Lostsale[j] = m.addVar()

m.setObjective (gp.quicksum(Lostsale[j] for j in range(nr_retailer)), GRB.MINIMIZE)   

m.addConstr(sum(Network[i,j] for i in range (nr_supplier) for j in range(nr_retailer)) <= maxnet)

for i in range(nr_supplier):
for j in range(nr_retailer):
m.addConstr(Fulfillment[i,j] <= bigM*Network[i,j])

for j in range (nr_retailer):
m.addConstr(sum(Fulfillment[i,j] for i in range(nr_supplier)) + Lostsale[j] >= demand[j] )

for i in range (nr_supplier):
m.addConstr(sum(Fulfillment[i,j] for j in range(nr_retailer)) <= capacity[i] )
for j in range(nr_retailer):
m.addConstr(Lostsale[j] == demand[j] - sum(Fulfillment[i,j] for i in range(nr_supplier)))
for i in range(nr_supplier):
for j in range(nr_retailer):
if i == j:
m.addConstr(Fulfillment[i,j] == min(demand[j], capacity[i]))

m.optimize()
res = m.objVal
shortfall =+ res
estimate = np.mean(shortfall)/n_samples
print(estimate)

Answer 1

您应该检查优化过程的解决方案状态。只有当优化成功终止时，才能查询目标值：

if m.status == GRB.OPTIMAL:
print('Optimal objective: %g' % m.objVal)
elif m.status == GRB.INF_OR_UNBD:
print('Model is infeasible or unbounded')
sys.exit(0)
elif m.status == GRB.INFEASIBLE:
print('Model is infeasible')
sys.exit(0)
elif m.status == GRB.UNBOUNDED:
print('Model is unbounded')
sys.exit(0)
else:
print('Optimization ended with status %d' % m.status)
sys.exit(0)