energies
Article
A Crew Scheduling Model to Incrementally Optimize
Workforce Assignments for Offshore Wind Farm Constructions
Daniel Rippel
1,2,
* , Fatemeh Abasian Foroushani
2
, Michael Lütjen
1
and Michael Freitag
1,2
Citation: Rippel, D.; Foroushani,
F.A.; Lütjen, M.; Freitag, M. A Crew
Scheduling Model to Incrementally
Optimize Workforce Assignments for
Offshore Wind Farm Constructions.
Energies 2021, 14, 6963. https://
doi.org/10.3390/en14216963
Received: 14 September 2021
Accepted: 19 October 2021
Published: 22 October 2021
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1
BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20,
28359 Bremen, Germany; ltj@biba.uni-bremen.de (M.L.); fre@biba.uni-bremen.de (M.F.)
2
Faculty of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany;
fa_ab@uni-bremen.de
* Correspondence: rip@biba.uni-bremen.de; Tel.: +49-(0)421-218-50137
Abstract: 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 installa-
tions. 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: offshore installations; crew scheduling; mixed-integer linear programming; model pre-
dictive control
1. Introduction
Wind energy offers an opportunity to satisfy the world’s need for green and sus-
tainable energy. Studies show that the last decade witnessed an exponential increase in
produced wind energy [1]. This increase results from more capable turbines [2] and con-
tinuously increasing concurrent installation and refurbishing projects [3]. Offshore wind
farms can produce higher amounts of energy when compared to their onshore counter-
parts, resulting from the ability to install larger turbines and wind farms and higher wind
exposure at the open sea [4]. The current political landscape implies that these trends will
continue over the following years. In 2020, several countries worldwide reworked their
climate policies, potentially resulting in a worldwide reduction of 47% of global emissions
by 2050, e.g., banning fossil energy generation or fuel [1].
While offshore wind farms provide large amounts of energy, their installation imposes
additional challenges. While higher wind speeds and quickly changing weather condi-
tions at the open sea favor energy generation, they render the installation complicated
and impede installation operations in more than a hundred meters of height. Moreover,
offshore construction sites allow larger farms but render them harder to reach, requiring
precise operations planning using weather forecasts and human experience. Besides these
planning-related challenges, the installation of offshore wind farms requires highly spe-
cialized equipment and certified personnel. For example, installation vessels, so-called
jack-up vessels, usually provide the ability to mount themselves onto the sea bed to sta-
bilizes themselves for crane operations. In various countries, personnel requires special
certification and training for offshore installations in such heights. Several authors in the
Energies 2021, 14, 6963. https://doi.org/10.3390/en14216963 https://www.mdpi.com/journal/energies