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.

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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