问题描述
我建立了具有随机随机需求(〜N(100,40))的优化模型。我的优化模型本身给了我看似有希望的结果。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。但是它无法附加我需要在仿真后得出期望值的目标值。
错误代码为:无法检索属性'objVal' 任何帮助将不胜感激!
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)
解决方法
您应该检查优化过程的解决方案状态。如果优化成功终止,则只能查询目标值:
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