Decision Optimization

Decision Optimization

Delivers prescriptive analytics capabilities and decision intelligence to improve decision-making.


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  • 1.  Execute same path while rerun optimization when using polishing after time

    Posted 03/11/13 09:56 AM

    Originally posted by: SystemAdmin


    Hi,
    I have a MIP problem that I solve once with the parameter PolishAfterTime.
    I would like to execute exactly the same path and retrieve the same solution each time I resolve this problem.
    Is it possible?

    I solve the problem within 2 phases: 1- branch and cut (for a specific time) 2-PolishAfterNode = 0 and PolishAfterIntSol = 1.
    I tried to solve once, then keep informations on number of integer solutions and number of nodes that I had in the phase 1, then use the PolishAfterNode and/or PolishAfterIntSol
    for the phase 1 of the resolve.
    The path is not the same since Cplex make cuts and other things after the last integer solution found that are not done in the resolve.

    So, is there any way to do it?
    #CPLEXOptimizers
    #DecisionOptimization


  • 2.  Re: Execute same path while rerun optimization when using polishing after time

    Posted 03/13/13 09:30 AM

    Originally posted by: SystemAdmin


    I will do it with callback and I will manage the time in it.
    #CPLEXOptimizers
    #DecisionOptimization


  • 3.  Re: Execute same path while rerun optimization when using polishing after time

    Posted 03/14/13 05:54 AM

    Originally posted by: SystemAdmin


    A decision that depends on wall-clock time will always be non-deterministic.

    The best approach is to use CPLEX 12.5 and use a deterministic time limit. You can run your model a bit, for example with the interactive CPLEX, to measure the ratio of deterministic time to wall-clock time on your hardware. Then, set the deterministic time limit accordingly to roughly match the wall-clock time limit that you are aiming for.

    Note that in CPLEX 12.5 there is also the PolishAfterDettime parameter, which may come in handy for your purposes.
    Tobias
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    #DecisionOptimization