MILP solution taking too long, while the incumbent obj value is at the optimal level from early on
Using GUROBI with python.
When solving a MILP, I notice that the incumbent is very early (25sec) at the optimal point but the best bound is so slow to fall (maximization problem) that it takes ages (2000sec+) for reaching the optimal solution. This holds for varying input datasets so I can deduct that it is a general trend.
Any suggestions for changing the parameters that could make it happen faster? I have tried to change lots of them (cuts, MILPfocus etc.) and also use the model.tune() function.

Are your variables are expression bounded? maybe the solver is taking too much time discarding unrealistic values, e.g. for PWL functions, before applying sos constraints
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Thank you very much for your response.
I guess you mean continuous variables that are used (auxiliary) in bigM expressions ? By saying expression bounded do you mean something different than just bounded?
Well I did that and saw a certain amount of improvement and thank you for that. But still the trend remains there, again the incumbent is at the optimal level a lot earlier than the finalization of the run. Any more suggestions please?
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