PYOMO，组合集

Question

我希望你在PYOMO LP方面提供帮助。我不知道我做错了什么，所以任何反馈都会有帮助：

我的数据集如下所示：

#The demand for each order
demand= {782912: 808, 782913: 3188, 782914: 2331, 782915: 847, 782916: 2163,789954:5643}

#The cost per unit produced for each order based in which factory chosen
total cost= { (782912, 'PLANT16'): 0.46, (782913, 'PLANT16'): 0.46, (782914, 'PLANT16'): 0.46, (782915, 'PLANT16'): 0.46, (782916, 'PLANT16'): 0.46}, (789954,'PLANT05'):0.90,(789954,'PLANT07'):0.91,(789954,'PLANT08'):1.13,(789954,'PLANT10'):0.12}

#The capacity of each factory
supply= {'PLANT05': 531,'PLANT07': 841,'PLANT08': 1107,'PLANT10': 981,'PLANT16': 2313}

#定义模型

model=pyo.ConcreteModel()
#Sets
model.i=pyo.Set(initialize=demand.keys())#orders
model.j=pyo.Set(initialize=supply.keys()) #factories
model.select_combos = pyo.Set(within = model.i * model.j, initialize = total_costs_per_unit.keys())
#Parameters

model.p=pyo.Param(model.i,model.j,initialize=total_costs_per_unit) # here goes the cost dictionary
p=model.p

model.d=pyo.Param(model.i,initialize=demand)
d=model.d

model.s=pyo.Param(model.j,initialize=supply)
s=model.s

#Decision variable
model.x=pyo.Var(model.i,model.j,within=pyo.NonNegativeReals)
x=model.x

#Objective function
def obj_rul(model):

  return sum(p[i,j]*x[i,j]  for i,j in model.select_combos)
#warning , not all combinations of i,j exist in my model.p, as they would not be valid solutions for the problem
model.Obj=pyo.Objective(rule=obj_rul,sense=pyo.minimize)

#Constraints

def Const1(model,i):
  return sum(x[i,j] for j in model.j)>=d[i]
model.condemand=pyo.Constraint(model.i,rule=Const1)

def Const5(model,j):
  return sum(x[i,j] for i in model.i)<=s[j]
model.consupply=pyo.Constraint(model.j,rule=Const5)

#Solving
Solver=SolverFactory('glpk')

results=Solver.solve(model)

print(results)
print('Obj funct=',model.Obj())
for i in model.i:
  for j in model.j:
    print('for order',i," from plant ",j, " is sent ",x[i,j]())

我收到的错误消息是：

ERROR:pyomo.core:evaluating object as numeric value: x[782912,PLANT16]
    (object: <class 'pyomo.core.base.var._GeneralVarData'>)
No value for uninitialized NumericValue object x[782912,PLANT16]

总之，我认为如果我有‘订单’和‘植物’的所有组合，它就不会有任何问题。因为现在我不知道如何解决这个问题。

Answer 1

你在正确的轨道上，你的怀疑是正确的。

因为模型中根本没有使用i, j的某些组合，所以它们永远不会被初始化，当您试图打印它们的值时，您会遇到问题。在您的模型中还有其他问题处理相同的事情。

所以你有几个选择。

您有一个“隐含的”约束，如果您没有生产成本，那么它不能在该工厂，所以您可以添加到您的数据，并在“生产上限”为每一个项目-工厂组合.不是真正的乐趣。

或者你可以为“非法”物品的生产投入很大的成本，这样解决者就不会去挑选它们。

但是您已经完成了部分工作，我认为这是最好的解决方案，就是创建一组i, j的“合法组合”。你只是没有把它带到终点线。您可以/应该使用这个子集作为自然遵循它的事物的基础，比如x和p。看看我的密码。您只需要小心只使用合法的i, j组合的总和，这很容易通过筛选它们来完成，我在更新的约束中显示了这一点。

注意:我把你所有的能力乘以10倍，因为最初的模型是不可行的。是的，这是运行的，您应该能够根据需要修改它。

代码

import pyomo.environ as pyo

#The demand for each order
demand= {782912: 808, 782913: 3188, 782914: 2331, 782915: 847, 782916: 2163,789954:5643}

#The cost per unit produced for each order based in which factory chosen
production_cost= {  (782912, 'PLANT16'): 0.46, 
                    (782913, 'PLANT16'): 0.46, 
                    (782914, 'PLANT16'): 0.46, 
                    (782915, 'PLANT16'): 0.46, 
                    (782916, 'PLANT16'): 0.46, 
                    (789954, 'PLANT05'): 0.90,
                    (789954, 'PLANT07'): 0.91,
                    (789954, 'PLANT08'): 1.13,
                    (789954, 'PLANT10'): 0.12}

#The capacity of each factory
supply= {'PLANT05': 5310,'PLANT07': 8410,'PLANT08': 11070,'PLANT10': 9810,'PLANT16': 23130}

model=pyo.ConcreteModel()

#Sets
model.i=pyo.Set(initialize=demand.keys()) #orders
model.j=pyo.Set(initialize=supply.keys()) #factories
model.select_combos = pyo.Set(within = model.i * model.j, initialize = production_cost.keys())

#Parameters
model.p=pyo.Param(model.select_combos, initialize=production_cost) # here goes the cost dictionary
# p=model.p  <-- I wouldn't rename things...confusing

model.d=pyo.Param(model.i,initialize=demand)
# d=model.d

model.s=pyo.Param(model.j,initialize=supply)
# s=model.s

#Decision variable
model.x=pyo.Var(model.select_combos, within=pyo.NonNegativeReals)
# x=model.x

#Objective function
# def obj_rul(model):
#   return sum(p[i,j]*x[i,j]  for i,j in model.select_combos)
#warning , not all combinations of i,j exist in my model.p, as they would not be valid solutions for the problem

# your objective is a simple sum() expression, so you don't need the complication of a rule-function combo...
model.Obj=pyo.Objective(expr=sum(model.p[i,j]*model.x[i,j] for i,j in model.select_combos),sense=pyo.minimize)

#Constraints

def Const1(model,i):
  return sum(model.x[i,j] for j in model.j if (i,j) in model.select_combos)>=model.d[i]
model.condemand=pyo.Constraint(model.i,rule=Const1)

def Const5(model,j):
  return sum(model.x[i,j] for i in model.i if (i,j) in model.select_combos)<=model.s[j]
model.consupply=pyo.Constraint(model.j,rule=Const5)

#Solving
Solver=pyo.SolverFactory('glpk')

results=Solver.solve(model)

print(results)
#print('Obj funct=',model.Obj())
for i, j in model.select_combos:
  print('for order',i," from plant ",j, " is sent ", pyo.value(model.x[i,j]))