A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

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 th...

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Autores principales:	Daniel Rippel, Fatemeh Abasian Foroushani, Michael Lütjen, Michael Freitag
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	offshore installations crew scheduling mixed-integer linear programming model predictive control Technology T
Acceso en línea:	https://doaj.org/article/e5f9162c3cf146fda73ce3eee8a180ab
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id	oai:doaj.org-article:e5f9162c3cf146fda73ce3eee8a180ab
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spelling	oai:doaj.org-article:e5f9162c3cf146fda73ce3eee8a180ab2021-11-11T15:47:09ZA Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions10.3390/en142169631996-1073https://doaj.org/article/e5f9162c3cf146fda73ce3eee8a180ab2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6963https://doaj.org/toc/1996-1073In 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.Daniel RippelFatemeh Abasian ForoushaniMichael LütjenMichael FreitagMDPI AGarticleoffshore installationscrew schedulingmixed-integer linear programmingmodel predictive controlTechnologyTENEnergies, Vol 14, Iss 6963, p 6963 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	offshore installations crew scheduling mixed-integer linear programming model predictive control Technology T
spellingShingle	offshore installations crew scheduling mixed-integer linear programming model predictive control Technology T Daniel Rippel Fatemeh Abasian Foroushani Michael Lütjen Michael Freitag A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
description	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.
format	article
author	Daniel Rippel Fatemeh Abasian Foroushani Michael Lütjen Michael Freitag
author_facet	Daniel Rippel Fatemeh Abasian Foroushani Michael Lütjen Michael Freitag
author_sort	Daniel Rippel
title	A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
title_short	A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
title_full	A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
title_fullStr	A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
title_full_unstemmed	A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions
title_sort	crew scheduling model to incrementally optimize workforce assignments for offshore wind farm constructions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e5f9162c3cf146fda73ce3eee8a180ab
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A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

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