Possible to define objective parameters in constraints?

回答済み

Tiril Amundsen

2022年04月28日 08:50

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Hi, I have a problem defining some parameters that I want to use in my objective function. I want to define the parameter used in the objective function in the constraints, but it only returns that the parameter u is not defined. An example of what i want to do:

max u11*x1 + u12*x2 + u21*x1 + u22*x2

subject to:

u[i,j] = ....

+ more constraints

 

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• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2025年11月16日 23:38

  This post is more than three 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.
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  Jonasz Staszek

  • Community Moderator

  2022年04月28日 13:21

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  Hi Tiril!

  Could you share a minimal reproducible example of the code which throws the error? We will then be able to give you better advice.

  Best regards
  Jonasz

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  Tiril Amundsen

  2022年05月01日 17:53

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  import pandas as pd
  import gurobipy as gp
  from gurobipy import *
  import numpy as np
  from gurobipy import GRB
  import matplotlib.pyplot as plt
  import sys
  
  ## Sets ##
  criteria = [0, 1, 2, 3, 4]
  decisionM = [0, 1]
  alternative = [0, 1]
  
  ## Parameters ##
  
  weight = np.array( [[0.03, 0.09, 0.28, 0.14, 0.46], [0.39, 0.15, 0.08, 0.06, 0.32]])
  
  V1 = np.array([[59800000, 68, 0.5, 0.5, 40], [65622000, 165, 0.58, 0.5, 76]])
  
  V2 = np.array([[59800000, 68, 1, 1, 1100], [65622000, 165, 0.75, 0.5, 450]])
  
  V1_min = []
  for c in criteria:
      if V1[0,c]<V1[1,c]:
          V1_min.append(V1[0,c])
      else:
          V1_min.append(V1[1,c])
          
  
  V2_min = []
  for c in criteria:
      if V2[0,c]<V2[1,c]:
          V2_min.append(V1[0,c])
      else:
          V2_min.append(V2[1,c])            
  
  
  ## Model ##
  m = gp.Model('Weighted-sum-method')
  
  #Decision variable
  x = m.addVars(alternative, decisionM, vtype=GRB.BINARY, name='x')
  
  # Defining U1
  for c in criteria:
      for a in alternative:
          m.addConstr(
                  (U1[a,c] == V1_min[c]/V1[a,c]), 'Utility1')
      
  # Defining U2            
  for c in criteria:
      for a in alternative:
          m.addConstr(
                  (U2[a,c] == V2_min[c]/V2[a,c]), 'Utility1')
             
  # Only one alternative can be chosen by each decision maker
  for a in alternative: 
      m.addConstr(
              (sum(x[a,d] for d in decisionM) == 1), 'onealternative')
      
  # Objective function
  obj = m.setObjective(sum(x[a,0]*weight[0,c]*U1[a,c] for a in alternative 
                           for c in criteria) + sum(x[a,1]*weight[1,c]*U2[a,c] 
                               for a in alternative for c in criteria), 
                                  GRB.MAXIMIZE)
      
  m.optimize()

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  Jonasz Staszek

  • Community Moderator

  2022年05月02日 06:51

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  Hi Tiril,

  1. What are \(U1, U2\)? Are they variables? If so, you need to define them as such upfront, for example by:

  U1 = m.addVars(alternative, criteria, vtype="C", name="u1")
  U2 = m.addVars(alternative, criteria, vtype="C", name="u2")

  just after the model definition. Adding these lines solved the problem on my machine.

  2. On a side note, you may want to give all constraints different names. Your code names both U1 and U2 constraints 'Utility1'.

  Best regards
  Jonasz

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  Tiril Amundsen

  2022年05月08日 11:35

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  Thank you, it worked!

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