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An optimisation model for scheduling the decommissioning of an offshore wind farm

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  • Chandra Ade Irawan

    (University of Nottingham Ningbo China)

  • Graham Wall

    (University of Portsmouth)

  • Dylan Jones

    (University of Portsmouth)

Abstract

An optimisation model is proposed for scheduling the decommissioning of an offshore wind farm in order to minimise the total cost which is comprised of jack-up vessel, barge (transfer) vessel, inventory, processing and on-land transportation costs. This paper also presents a comprehensive review of the strategic issues relating to the decommissioning process and of scheduling models that have been applied to offshore wind farms. A mathematical model using integer linear programming is developed to determine the optimal schedule considering several constraints such as the availability of vessels and planning delays. As the decommissioning problem is challenging to solve, a matheuristic approach based on the hybridisation of a heuristic approach and an exact method is also proposed to find near optimal solutions for a test set of problems. A set of computational experiments has been carried out to assess the proposed approach.

Suggested Citation

  • Chandra Ade Irawan & Graham Wall & Dylan Jones, 2019. "An optimisation model for scheduling the decommissioning of an offshore wind farm," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 513-548, June.
    • Handle: RePEc:spr:orspec:v:41:y:2019:i:2:d:10.1007_s00291-019-00546-z
    DOI: 10.1007/s00291-019-00546-z
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    References listed on IDEAS

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    1. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2017. "Minimizing transportation and installation costs for turbines in offshore wind farms," Renewable Energy, Elsevier, vol. 101(C), pages 667-679.
    2. Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    3. Stefan Schwerdfeger & Nils Boysen & Dirk Briskorn, 2018. "Just-in-time logistics for far-distant suppliers: scheduling truck departures from an intermediate cross-docking terminal," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 1-21, January.
    4. Barlow, Euan & Tezcaner Öztürk, Diclehan & Revie, Matthew & Akartunalı, Kerem & Day, Alexander H. & Boulougouris, Evangelos, 2018. "A mixed-method optimisation and simulation framework for supporting logistical decisions during offshore wind farm installations," European Journal of Operational Research, Elsevier, vol. 264(3), pages 894-906.
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    Cited by:

    1. Tjaberings, Jorick & Fazi, Stefano & Ursavas, Evrim, 2022. "Evaluating operational strategies for the installation of offshore wind turbine substructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    2. Noor Amila Wan Abdullah Zawawi & Kamaluddeen Usman Danyaro & M. S. Liew & Lim Eu Shawn, 2023. "Environmental Sustainability and Efficiency of Offshore Platform Decommissioning: A Review," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    3. Monalisa Godeiro & Mario González & Dylan Jones & Negar Akbari & Gabriela Nascimento & David Melo & Rafael Vasconcelos & Andressa Santiso & Luana Nogueira & Mariana Almeida & José Toledo, 2024. "Green Port Industry to Support the Offshore Wind Sector: A Proposal Framework," Energies, MDPI, vol. 17(23), pages 1-26, December.
    4. Rippel, Daniel & Peng, Shengrui & Lütjen, Michael & Sczcerbicka, Helena & Freitag, Michael, 2020. "Model transformation framework for scheduling offshore logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 521-552, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. Amorosi, Lavinia & Fischetti, Martina & Paradiso, Rosario & Roberti, Roberto, 2024. "Optimization models for the installation planning of offshore wind farms," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1182-1196.
    6. Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), 2020. "Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 30, number 30.
    7. Rippel, Daniel & Jathe, Nicolas & Lütjen, Michael & Szczerbicka, Helena & Freitag, Michael, 2019. "Integrated domain model for operative offshore installation planning," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 25-54, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Daniel Rippel & Fatemeh Abasian Foroushani & Michael Lütjen & Michael Freitag, 2021. "A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions," Energies, MDPI, vol. 14(21), pages 1-21, October.
    9. Anne P. M. Velenturf, 2021. "A Framework and Baseline for the Integration of a Sustainable Circular Economy in Offshore Wind," Energies, MDPI, vol. 14(17), pages 1-41, September.

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