Non-preemptive goal programming in Gurobipy

ユーザーの入力を待っています。

Madhushini Narayana Prasad

2022年02月09日 03:19

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Hi,

I am having the following setup: First I run my optimization model to minimize only the slack penalties. Then, the slack penalties are included as upper bound to optimize the original objective function as the next and final step. For some reason, the overall objective function value from the goal programming is significantly worse than the original MILP model. 

Model.setObjective(slack_obj,GRB.MINIMIZE)

Model.Optimize()

#Retrieve the slack variable values

Slack_Var.UB = Target_Value

Model.reset(0)

Model.setObjective(obj_expr-slack_obj, GRB>MAXIMIZE)

Model.Optimize()

 

While the optimal obj value from original LP model is 1.395, the optimal value in this hierarchical model is 1.411. Am I missing something here? I do not want a warm start for the second model.

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  Simranjit Kaur

  • Gurobi Staff

  2025年11月16日 23:43

  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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  Jaromił Najman

  • Gurobi Staff

  2022年02月14日 08:03

  Could you please share a minimal working example to reproduce the issue? Or at least please share the logs showing the different behavior.

  Are you performing any other changes to the model except for the objective? Do you solve up to a 0% MIPGap? Please note that depending on the numerical properties of your model, tolerance may play a significant role.

  Best regards, 
  Jaromił

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