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An alternative to the black box: Strategy learning

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  • Simon Taub
  • Oleg S Pianykh

Abstract

In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are “black box” algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limiting their practical use. In this work, we propose an alternative approach: using machine learning to generate optimal, comprehensible strategies which can be understood and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain near-optimal results.

Suggested Citation

  • Simon Taub & Oleg S Pianykh, 2022. "An alternative to the black box: Strategy learning," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-15, March.
  • Handle: RePEc:plo:pone00:0264485
    DOI: 10.1371/journal.pone.0264485
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    References listed on IDEAS

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    1. Easton, Fred F. & Mansour, Nashat, 1999. "A distributed genetic algorithm for deterministic and stochastic labor scheduling problems," European Journal of Operational Research, Elsevier, vol. 118(3), pages 505-523, November.
    2. Bürgy, Reinhard & Michon-Lacaze, Hélène & Desaulniers, Guy, 2019. "Employee scheduling with short demand perturbations and extensible shifts," Omega, Elsevier, vol. 89(C), pages 177-192.
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