Problem with logical constraint using addGenConstrIndicator()

回答済み

Ludwig Baunach

2024年05月29日 14:40

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Hi, 

 

I want the "finish" variable to be 1 when "beet_flow" is 0. However, when my checking output I get 0 for all values of finish. I should get values of 1 for the given time periods and i's. Below I post a test set to replicate the problem and I post my Python output. I appreciate any help. Thank you!

 

Code:

from gurobipy import Model, GRB

# Define the sets I and T
I = [1, 2]
T = [0, 1, 2, 3, 4]

# Create a new Gurobi model
model = Model("test_model")

# Define variables
beet_flow = model.addVars(I, range(1), range(1), T, vtype=GRB.CONTINUOUS, name="b")
finish = model.addVars(I, T, vtype=GRB.BINARY, name="finish")

# Set artificial beet values
model.addConstr(beet_flow[1, 0, 0, 0] == 1500)
model.addConstr(beet_flow[1, 0, 0, 1] == 500)
model.addConstr(beet_flow[1, 0, 0, 2] == 0)
model.addConstr(beet_flow[1, 0, 0, 3] == 0)
model.addConstr(beet_flow[1, 0, 0, 4] == 0)

model.addConstr(beet_flow[2, 0, 0, 0] == 1500)
model.addConstr(beet_flow[2, 0, 0, 1] == 500)
model.addConstr(beet_flow[2, 0, 0, 2] == 200)
model.addConstr(beet_flow[2, 0, 0, 3] == 0)
model.addConstr(beet_flow[2, 0, 0, 4] == 0)


# Ensure the 'finish' variable is 1 when beets are 0 using indicator constraints
for i in I:
    for t in T:
        model.addGenConstrIndicator(finish[i, t], 1, beet_flow[i, 0, 0, t] == 0)

# Optimize the model
model.optimize()

# Check and print the results
if model.status == GRB.OPTIMAL:
    for i in I:
        for t in T:
            print(f"finish[{i},{t}] = {finish[i, t].x}")
            print(f"Beets [{i}, {t}]: {beet_flow[i,0,0,t].x}")

 

Output:

 

Optimize a model with 10 rows, 30 columns and 10 nonzeros
Model fingerprint: 0xe8ba07b5
Model has 10 general constraints
Variable types: 10 continuous, 20 integer (20 binary)
Coefficient statistics:
  Matrix range     [1e+00, 1e+00]
  Objective range  [0e+00, 0e+00]
  Bounds range     [1e+00, 1e+00]
  RHS range        [2e+02, 2e+03]
  GenCon coe range [1e+00, 1e+00]
Presolve removed 10 rows and 30 columns
Presolve time: 0.00s
Presolve: All rows and columns removed

Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 1 (of 8 available processors)

Solution count 1: 0 

Optimal solution found (tolerance 1.00e-04)
Best objective 0.000000000000e+00, best bound 0.000000000000e+00, gap 0.0000%
finish[1,0] = 0.0
Beets [1, 0]: 1500.0
finish[1,1] = 0.0
Beets [1, 1]: 500.0
finish[1,2] = 0.0
Beets [1, 2]: 0.0
finish[1,3] = 0.0
Beets [1, 3]: 0.0
finish[1,4] = 0.0
Beets [1, 4]: 0.0
finish[2,0] = 0.0
Beets [2, 0]: 1500.0
finish[2,1] = 0.0
Beets [2, 1]: 500.0
finish[2,2] = 0.0
Beets [2, 2]: 200.0
finish[2,3] = 0.0
Beets [2, 3]: 0.0
finish[2,4] = 0.0
Beets [2, 4]: 0.0

Process finished with exit code 0

0

• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2026年11月13日 01:24

  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.
• 

  Marika Karbstein

  • Gurobi Staff

  2024年05月29日 15:34

  Hi Ludwig,

  In your code, you have modeled
  if "finish"=1 then "beet_flow"=0,
  This relation does not restrict "beet_flow" in case "finish" has the value 0. So, you can have both 0.
  You probably want to define the constraint as

  model.addGenConstrIndicator(finish[i, t], 0, beet_flow[i, 0, 0, t] >= 1e-7)

  Here, the finish variable cannot take the value 0 if the beet_flow variable is 0 and must then be 1. Note that strict inequalities are not possible (Why doesn't Gurobi support strict inequality constraints like x < a?). That is why I used >= 1e-7 instead of >0. Furthermore, it is used that the LB of the beet_flow variables is 0.

  Best regards,
  Marika

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• 

  Ludwig Baunach

  2024年05月29日 15:49

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  Hi Marika, 

  thank you very much for your response. It helped me fix the problem. Actually, I needed to add both constraints to create the correct indicator. Without the second other one I would get ones for all values. 

  for i in I:
      for t in T:
          model.addGenConstrIndicator(finish[i, t], 0, beet_flow[i, 0, 0, t] >= 1e-7)
  
  for i in I:
      for t in T:
          model.addGenConstrIndicator(finish[i, t], 1, beet_flow[i, 0, 0, t] <= 1e-7)

  Thank you for your help!

  Best regards,

  Ludwig

  0

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