LAPSE:2023.17951
Published Article
LAPSE:2023.17951
A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
March 7, 2023
In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.
Keywords
crew scheduling, mixed-integer linear programming, Model Predictive Control, offshore installations
Suggested Citation
Rippel D, Foroushani FA, Lütjen M, Freitag M. A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions. (2023). LAPSE:2023.17951
Author Affiliations
Rippel D: BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany; Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany [ORCID]
Foroushani FA: Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany [ORCID]
Lütjen M: BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany [ORCID]
Freitag M: BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany; Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany [ORCID]
Journal Name
Energies
Volume
14
Issue
21
First Page
6963
Year
2021
Publication Date
2021-10-22
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14216963, Publication Type: Journal Article
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LAPSE:2023.17951
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doi:10.3390/en14216963
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Mar 7, 2023
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