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Unable to optimize using a trained neural network as a surrogate model




  • Ronald van der Velden
    Gurobi Staff Gurobi Staff


    Interesting problem. If you don't have too many scenarios, you could consider testing multi-scenario feature in Gurobi. Regarding the surrogate model approach - if I understand correctly, the surrogate model only predicts travel time given a set of facilities to open; it assumes that all demand is fulfilled? What constraints in the remaining MIP ensure that demand is fulfilled?

    Kind regards,

  • Xiaochen Chou
    First Comment
    First Question

    Hi Ronald,

    Thank you for the suggestions. 

    The surrogate model predicts the evaluation value of a given set of facilities based on travel costs with added penalties to account for unfulfilled demands. It aims to predict solution quality across various scenarios. The training dataset is generated by solving the assignment problem for the open facilities.

    You are right that then there is no constraints on fulfilling the demands in the remaining MIP, it should be the cause of the issue. As the constraints on demands depend entirely on the surrogate model, insufficient data on rare solutions with few or no open facilities in the training set could be another contributing factor.

    Thank you once more for making it clear. I'll tackle the issue by addressing these two aspects.

    Best regards,




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