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Composing Vessel Fleets for Maintenance at Offshore Wind Farms by Solving a Dual-Level Stochastic Programming Problem Using GRASP

Author

Listed:
  • Kamilla Hamre Bolstad

    (Department of Industrial Economics and Technology Management, NTNU, 7491 Trondheim, Norway)

  • Manu Joshi

    (Department of Industrial Economics and Technology Management, NTNU, 7491 Trondheim, Norway)

  • Lars Magnus Hvattum

    (Faculty of Logistics, Molde University College, 6410 Molde, Norway)

  • Magnus Stålhane

    (Department of Industrial Economics and Technology Management, NTNU, 7491 Trondheim, Norway)

Abstract

Background: Dual-level stochastic programming is a technique that allows modelling uncertainty at two different levels, even when the time granularity differs vastly between the levels. In this paper we study the problem of determining the optimal fleet size and mix of vessels performing maintenance operations at offshore wind farms. In this problem the strategic planning spans decades, while operational planning is performed on a day-to-day basis. Since the operational planning level must somehow be taken into account when making strategic plans, and since uncertainty is present at both levels, dual-level stochastic programming is suitable. Methods: We present a heuristic solution method for the problem based on the greedy randomized adaptive search procedure (GRASP). To evaluate the operational costs of a given fleet, a novel fleet deployment heuristic (FDH) is embedded into the GRASP. Results: Computational experiments show that the FDH produces near optimal solutions to the operational day-to-day fleet deployment problem. Comparing the GRASP to exact methods, it produces near optimal solutions for small instances, while significantly improving the primal solutions for larger instances, where the exact methods do not converge. Conclusions: The proposed heuristic is suitable for solving realistic instances, and produces near optimal solution in less than 2 h.

Suggested Citation

  • Kamilla Hamre Bolstad & Manu Joshi & Lars Magnus Hvattum & Magnus Stålhane, 2022. "Composing Vessel Fleets for Maintenance at Offshore Wind Farms by Solving a Dual-Level Stochastic Programming Problem Using GRASP," Logistics, MDPI, vol. 6(1), pages 1-22, January.
    • Handle: RePEc:gam:jlogis:v:6:y:2022:i:1:p:6-:d:721433
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    References listed on IDEAS

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    1. Lars Magnus Hvattum & Arne Løkketangen & Gilbert Laporte, 2009. "Scenario Tree-Based Heuristics for Stochastic Inventory-Routing Problems," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 268-285, May.
    2. Mahdi Yousefi Nejad Attari & Ali Ebadi Torkayesh & Behnam Malmir & Ensiyeh Neyshabouri Jami, 2021. "Robust possibilistic programming for joint order batching and picker routing problem in warehouse management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(14), pages 4434-4452, July.
    3. Marcelo Prais & Celso C. Ribeiro, 2000. "Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 164-176, August.
    4. Pantuso, Giovanni & Fagerholt, Kjetil & Hvattum, Lars Magnus, 2014. "A survey on maritime fleet size and mix problems," European Journal of Operational Research, Elsevier, vol. 235(2), pages 341-349.
    5. Mauricio G.C. Resende & Celso C. Ribeiro, 2010. "Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 283-319, Springer.
    6. Giovanni Pantuso & Kjetil Fagerholt & Stein W. Wallace, 2015. "Solving Hierarchical Stochastic Programs: Application to the Maritime Fleet Renewal Problem," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 89-102, February.
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    Cited by:

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