Python Pulp does not add all variables constraints and forgets objective function

Question

I am running in some problems using the module pulp. I want to create an mixed integer linear programming problem and write it as a LP file. After this I solve it with cplex.

The problem is that when I add the second constraints, the objective function becomes false(dummy is added) and only the first constraint is added with only decision variable x.

This is my code: I hope you can help me out!

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

I don't think list comprehensions [x[i] for i in ...] can be added to bay_model like this. If you want the constraint to hold over each element in the list you can define the elements beforehand:

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]