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Solve scheduling model using a greedy heurisitc



1 comment

  • Ronald van der Velden
    Gurobi Staff Gurobi Staff

    Hi Lorenz,

    You mentioned "Since my whole model is very complex for large model". Does this mean the model formulation is as above, but runtime increases as you add workers/shifts/days? Can you give an indication of the model dimensions, and the runtime you observe? Are there other complexities that are not shown above (e.g. labor rules related to consecutive shifts for the same worker) which could have an impact?

    If you're willing to sacrifice global optimality, you could consider solving a series of subproblems where each subproblem contains a subset of workers, days and/or shifts. One common approach is rolling horizon. You'd first solve the problem for days 1-7. Then you fix the assignments for day 1, and solve the model for days 2-8 (but include fixed variables for day 1), then for days 3-9 after fixing days 1-2 etcetera. Other alternatives exist, e.g. repeatedly solve for different subsets of employees. You could even start with rolling horizon, then iteratively reoptimize for subsets of days and/or employees. There's many options out there - but ideally you'd first try to solve the full MIP.

    Kind regards,


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