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GurobiError: Invalid argument to LinExpr multiplication

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Neda Türkoğlu
  • Gurobi-versary
  • First Question
  • First Comment

Hello I am getting the following error: GurobiError: Invalid argument to LinExpr multiplication

 

here is my code;

from gurobipy import GRB,Model,quicksum #*
import numpy as np # array kullanmak için
import pandas as pd #excel'i taşımak için


def Output(m):  
    # Print the result
    status_code = {1:'LOADED', 2:'OPTIMAL', 3:'INFEASIBLE', 4:'INF_OR_UNBD', 5:'UNBOUNDED'} #this is how a 'dictionary' 
                                                                                            #is defined in Python
    status = m.status
    
    print('The optimization status is ' + status_code[status])
    if status == 2:    
        # Retrieve variables value
        print('Optimal solution:')
        for v in m.getVars():
            print(str(v.varName) + " = " + str(v.x))    
        print('Optimal objective value: ' + str(m.objVal) + "\n")
        
model = Model("CPL_model");

n=5 #number of stations
first_station_set = range(n) # i / i and j # n+1 
#second_station_set = range(5) # j

bigM = 1000
m = 3 #number of vehicles
q = [[80,90,110,120,100,120]] #demand of stations
Q=[200,200,200] #capacity of the vehicle
cost=[[5,3,4,6,7,8]]


# Add Decision Varibales

#m = mdl.addVar(lb = 0, vtype = GRB.INTEGER, name = 'm') # vehicle amount
#q = mdl.addVars(first_station_set, vtype = GRB.INTEGER,name = 'Q') # station request
x = model.addVars(first_station_set, first_station_set, vtype=GRB.BINARY, name=["x_"+str(i+1) + "," +str(j+1) 
                                                                                for i in first_station_set for j in first_station_set]) 

u= model.addVars(first_station_set, lb = 0,vtype = GRB.INTEGER,name = ['u_'+str(i) for i in first_station_set])
#Add constraint

# Constraint 2
model.addConstrs((quicksum(x[i,j] for i in first_station_set) == 1 for j in range(1,n)), name = 'C2')


# Constraint 3
model.addConstrs((quicksum(x[j,i] for i in first_station_set) == 1 for j in range(1,n)), name = 'C3')

# Constraint 4
model.addConstr((quicksum(x[0,j] for j in first_station_set) <= m), name = 'C4')

# Constraint 5
model.addConstr(((quicksum(x[0,j] for j in range(1,n))) == (quicksum(x[j,0] for j in range(1,n)))), name = 'C5')


#BURADA YAZIYORUZ UUUUUUYU
model.addConstrs(((u[i]-u[j] + Q *x[i,j]  <= Q - q[j]) for i in range(n) for j in range(n)), name = 'C11')

#Objective FUnction

model.setObjective((quicksum(cost[i][j] * x[i][j] for i in range(n) for j in range(n))),GRB.MINIMIZE)


model.optimize()

model.write("DellAmico1.lp") # LP dosyası ürettirmek

status = model.status
object_Value = model.objVal
print()
print("model status is: ", status)
print()
print("Objective value is: ", object_Value)

Can you help me with this error please. Thanks in advance.

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2 comments

Date Votes
  • Official comment
    Simranjit Kaur
    • Gurobi Staff
    This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
  • Jaromił Najman
    • Gurobi Staff

    The error occurs because you are trying to multiply (and add) a list of values with an optimization variable while constructing a constraint.

    model.addConstrs(((u[i]-u[j] + Q *x[i,j]  <= Q - q[j]) for i in range(n) for j in range(n)), name = 'C11')

    Here \(\texttt{Q * x[i,j]}\) and \(\texttt{Q - q[j]}\) are the problem. Since all vehicle capacities are equal, you could just replace \(\texttt{Q}\) by 200.

    Then you try to subtract a list of lists \(\texttt{q}\). I am not sure what you want to achieve here. I would guess that \(\texttt{q}\) should be

    q = [80,90,110,120,100,120]

    but then it still has 1 entry to many (which does not result in an error).

    Then, there is another issue with the list of lists \(\texttt{cost}\)

    cost=[[5,3,4,6,7,8]]

    which you are trying to access via

    cost[i][j] * x[i][j] for i in range(n) for j in range(n)

    This will not work because there is only 1 dimension in the i-axis (and again 6 entries in the j-axis which are 1 too many given your n=5). You could fix it either by adding cost lists for all remaining i entries

    cost=[[5,3,4,6,7,8],[5,3,4,6,7,8],[5,3,4,6,7,8],[5,3,4,6,7,8],[5,3,4,6,7,8]]

    or using only one dimensional costs

    cost=[5,3,4,6,7,8]

    and adjusting the objective

    model.setObjective((quicksum(cost[i] * x[i][j] for i in range(n) for j in range(n))),GRB.MINIMIZE)

    Additionally, \(\texttt{x[i][j]}\) should read \(\texttt{x[i,j]}\)

    model.setObjective((quicksum(cost[i] * x[i,j] for i in range(n) for j in range(n))),GRB.MINIMIZE)

    With all the above fixes, you can execute the optimization. The model you generated is infeasible. Please have a look at How do I determine why my model is infeasible? for more information. A first start would be to analyze the IIS computed by Gurobi

    model.optimize()

    model.write("DellAmico1.lp") # LP dosyası ürettirmek

    if model.status == GRB.INFEASIBLE:
      model.computeIIS()
      model.write("iis.ilp")
      exit()

    Best regards, 
    Jaromił

     

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