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Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times

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  • Parmentier, Axel
  • T’Kindt, Vincent

Abstract

In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning and scheduling theory. These heuristics use a dedicated predictor to transform an instance of the hard problem into an instance of a simpler one solved to optimality. The obtained schedule is then transposed to the original problem. We introduce a structured learning approach which enables to fit the predictor using a database of instances with their optimal solution. Computational experiments show that the proposed learning based heuristics are competitive with state-of-the-art heuristics, notably on large instances for which they provide the best results.

Suggested Citation

  • Parmentier, Axel & T’Kindt, Vincent, 2023. "Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1032-1041.
    • Handle: RePEc:eee:ejores:v:305:y:2023:i:3:p:1032-1041
    DOI: 10.1016/j.ejor.2022.06.040
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    References listed on IDEAS

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    1. F Della Croce & V T'kindt, 2002. "A Recovering Beam Search algorithm for the one-machine dynamic total completion time scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1275-1280, November.
    2. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
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