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Matheuristics to optimize refueling and maintenance planning of nuclear power plants

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  • Nicolas Dupin

    (Université Paris-Saclay, CNRS, Laboratoire de recherche en informatique)

  • El-Ghazali Talbi

    (Univ. Lille)

Abstract

Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of 5 years, and few restrictions with time window constraints for the latest maintenance operations. Several constructive matheuristics and a Variable Neighborhood Descent local search are designed. The matheuristics are shown to be accurately effective for medium and large size instances. The matheuristics give also results on the design of MILP formulations and neighborhoods for the problem. Contributions for the operational applications are also discussed. It is shown that the restriction of time windows, which was used to ease computations, induces large over-costs and that this restriction is not required anymore with the capabilities of matheuristics or local searches to solve such size of instances. Our matheuristics can be extended to a bi-objective optimization extension with stability costs, for the monthly re-optimization of the maintenance planning in the real-life application.

Suggested Citation

  • Nicolas Dupin & El-Ghazali Talbi, 2021. "Matheuristics to optimize refueling and maintenance planning of nuclear power plants," Journal of Heuristics, Springer, vol. 27(1), pages 63-105, April.
    • Handle: RePEc:spr:joheur:v:27:y:2021:i:1:d:10.1007_s10732-020-09450-0
    DOI: 10.1007/s10732-020-09450-0
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    References listed on IDEAS

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    1. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    2. Nicolas Dupin, 2017. "Tighter MIP formulations for the discretised unit commitment problem with min-stop ramping constraints," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 149-176, March.
    3. Agnès Gorge & Abdel Lisser & Riadh Zorgati, 2012. "Stochastic nuclear outages semidefinite relaxations," Computational Management Science, Springer, vol. 9(3), pages 363-379, August.
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    Cited by:

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