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Optimised scheduling for weather sensitive offshore construction projects

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  • Kerkhove, L.-P.
  • Vanhoucke, M.

Abstract

The significant lead times and costs associated with materials and equipment in combination with intrinsic and weather related variability render the planning of offshore construction projects highly complex. Moreover, the way in which scarce resources are managed has a profound impact on both the cost and the completion date of a project. Hence, schedule quality is of paramount importance to the profitability of the project. A prerequisite to the creation of good schedules is the accuracy of the procedure used to estimate the project outcome when a given schedule is used. Because of the systematic influence of weather conditions, traditional Monte Carlo simulations fail to produce a reliable estimate of the project outcomes. Hence, the first objective of this research is to improve the accuracy of the project simulation by creating a procedure which includes both uncertainty related to the activities and an integrated model of the weather conditions. The weather component has been designed to create realistically correlated wind- and weather conditions for operationally relevant time intervals. The second objective of this research is to optimise the project planning itself by using both general meta-heuristic optimisation approaches and dedicated heuristics which have been specifically designed for the problem at hand. The performance of these heuristics is judged by the expected net present value of the project. The approach presented in this paper is tested on real data from the construction of an offshore wind farm off the Belgian coast and weather data gathered by the Flanders Marine Institute using measuring poles in the North Sea.

Suggested Citation

  • Kerkhove, L.-P. & Vanhoucke, M., 2017. "Optimised scheduling for weather sensitive offshore construction projects," Omega, Elsevier, vol. 66(PA), pages 58-78.
    • Handle: RePEc:eee:jomega:v:66:y:2017:i:pa:p:58-78
    DOI: 10.1016/j.omega.2016.01.011
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    3. Albert H. Schrotenboer & Evrim Ursavas & Iris F. A. Vis, 2019. "A Branch-and-Price-and-Cut Algorithm for Resource-Constrained Pickup and Delivery Problems," Transportation Science, INFORMS, vol. 53(4), pages 1001-1022, July.
    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.
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    6. Steven J. Schuldt & Mathew R. Nicholson & Yaquarri A. Adams & Justin D. Delorit, 2021. "Weather-Related Construction Delays in a Changing Climate: A Systematic State-of-the-Art Review," Sustainability, MDPI, vol. 13(5), pages 1-25, March.
    7. 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.
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    10. Javad Seif & Mohammad Dehghanimohammadabadi & Andrew Junfang Yu, 2020. "Integrated preventive maintenance and flow shop scheduling under uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 852-887, December.

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