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我在使用模块纸浆时遇到了一些问题。我想创建一个混合整数线性规划问题并将其写为 LP 文件。在此之后，我用 cplex 解决它。

问题是，当我添加第二个约束时，目标函数变为 false（添加了虚拟），并且仅添加了第一个约束，仅包含决策变量 x。

这是我的代码：我希望你能帮助我！

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)
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1 回答 1

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我认为列表推导 [x[i] for i in ...] 不能像这样添加到 bay_model 中。如果您希望约束保留列表中的每个元素，您可以预先定义元素：

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]
于 2015-10-08T14:26:38.690 回答