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Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Author

Listed:
  • Rafael Dawid

    (Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • David McMillan

    (Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK)

  • Matthew Revie

    (Department of Management Science, University of Strathclyde, Glasgow G1 1XW, UK)

Abstract

This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool’s capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

Suggested Citation

  • Rafael Dawid & David McMillan & Matthew Revie, 2018. "Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty," Energies, MDPI, vol. 11(9), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2190-:d:165033
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    References listed on IDEAS

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    1. Hongtao Lei & Gilbert Laporte & Bo Guo, 2012. "A generalized variable neighborhood search heuristic for the capacitated vehicle routing problem with stochastic service times," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 99-118, April.
    2. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    3. Andrés Gómez & Ricardo Mariño & Raha Akhavan-Tabatabaei & Andrés L. Medaglia & Jorge E. Mendoza, 2016. "On Modeling Stochastic Travel and Service Times in Vehicle Routing," Transportation Science, INFORMS, vol. 50(2), pages 627-641, May.
    4. R A Russell & T L Urban, 2008. "Vehicle routing with soft time windows and Erlang travel times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1220-1228, September.
    5. Li, Xiangyong & Tian, Peng & Leung, Stephen C.H., 2010. "Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm," International Journal of Production Economics, Elsevier, vol. 125(1), pages 137-145, May.
    6. Allahviranloo, Mahdieh & Chow, Joseph Y.J. & Recker, Will W., 2014. "Selective vehicle routing problems under uncertainty without recourse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 68-88.
    7. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    8. Krishna Chepuri & Tito Homem-de-Mello, 2005. "Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 153-181, February.
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    Cited by:

    1. Andrzej Łebkowski, 2020. "Analysis of the Use of Electric Drive Systems for Crew Transfer Vessels Servicing Offshore Wind Farms," Energies, MDPI, vol. 13(6), pages 1-23, March.
    2. de Simón-Martín, Miguel & Ciria-Garcés, Tomás & Rosales-Asensio, Enrique & González-Martínez, Alberto, 2022. "Multi-dimensional barrier identification for wind farm repowering in Spain through an expert judgment approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.

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