Python Pulp不会添加所有变量约束，并且会忘记目标函数

Question

我在使用模块纸浆时遇到一些问题。 我想创建一个混合整数线性编程问题，并将其写为LP文件。 之后，我用cplex解决了它。

问题在于，当我添加第二个约束时，目标函数变为false（添加了虚拟对象），并且仅第一个约束与决策变量x一起添加。

这是我的代码：希望您能帮帮我！

bay_model = pulp.LpProblem('Bay Problem', pulp.LpMinimize)

y = pulp.LpVariable.dicts(name = "y",indexs = (flight, flight, gates),
                          lowBound = 0, upBound = 1,cat = pulp.LpInteger)

x = pulp.LpVariable.dicts(name = "x",indexs = (flight,gates),lowBound = 0,                             
                          upBound = 1, cat=pulp.LpInteger)




bay_model += pulp.lpSum([x[i][j]*g.distance[j] for i in flight for j in gates])

for i in flight:
     bay_model += pulp.lpSum([x[i][j] for j in gates]) == 1
     print "flight must be assigned" + str(i)

for k in gates:
     bay_model += [y[i][j][k] * f.time_matrix[i][j] for i in flight for j in flight if f.time_matrix[i][j] == 1] <= g.capacity[k]
     bay_model += [(2 * y[i][j][k] - x[i][k] - x[j][k]) for i in flight for j in flight] == 0
     print "time constraint" + str(k)

Answer 1

我不认为列表推导[x [i] for i in ...]可以像这样添加到bay_model中。 如果希望约束保留在列表中的每个元素上，则可以预先定义元素：

for i in flights:
  for j in flights:
    if f.time_matrix[i][j] == 1:
      for k in gates:
        bay_model += y[i][j][k] * f.time_matrix[i][j] <= g.capacity[k]