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A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk

Author

Listed:
  • M. Urgo

    (Politecnico di Milano)

  • J. Váncza

    (Hungarian Academy of Sciences
    Budapest University of Technology and Economics)

Abstract

The research in the field of robust scheduling aims at devising schedules which are not sensitive—to a certain extent—to the disruptive effects of unexpected events. Nevertheless, the protection of the schedule from rare events causing heavy losses is still a challenging aim. The paper presents a novel approach for protecting the quality of a schedule by assessing the risk associated to the different scheduling decisions. The approach is applied to a stochastic scheduling problem with a set of jobs to be sequenced on a single machine. The release dates and processing times of the jobs are generally distributed independent random variables, while the due dates are deterministic. A branch-and-bound approach is taken to minimise the value-at-risk of the distribution of the maximum lateness. The viability of the approach is demonstrated through a computational experiment and the application to an industrial problem in the tool making industry.

Suggested Citation

  • M. Urgo & J. Váncza, 2019. "A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 472-496, June.
    • Handle: RePEc:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9316-z
    DOI: 10.1007/s10696-018-9316-z
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    References listed on IDEAS

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    Cited by:

    1. Lei Liu & Marcello Urgo, 2024. "Robust scheduling in a two-machine re-entrant flow shop to minimise the value-at-risk of the makespan: branch-and-bound and heuristic algorithms based on Markovian activity networks and phase-type dis," Annals of Operations Research, Springer, vol. 338(1), pages 741-764, July.
    2. Alfredo S. Ramos & Pablo A. Miranda-Gonzalez & Samuel Nucamendi-Guillén & Elias Olivares-Benitez, 2023. "A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic," Mathematics, MDPI, vol. 11(2), pages 1-25, January.

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