Artikel
A decomposition heuristic for rotational workforce scheduling
In rotational workforce planning, a schedule is constructed from a sequence of work and rest periods. Each employee starts at a different part of the schedule, and after a certain amount of time, the schedule repeats. The length of the schedule increases with a higher number of employees. At the same time, various constraints on work sequences and days off have to be considered. For a large number of employees, it is difficult to construct a schedule that meets the requirements. It is important to ensure low solution times independently of the problem instance characteristics. In this work, a novel decomposition approach for rotational shift scheduling is proposed. The decomposition exploits the fact that most constraints in rotational workforce scheduling are imposed on the work shift sequence. By considering a fixed set of blocks to cover the demand, the problem complexity can be greatly reduced. Given a fixed set of blocks, we propose a network model that determines whether a feasible sequence of shift blocks exists. The decomposition approach is applied to the problem structure of the Rotating Workforce Scheduling Problem but may be extended to different problem structures. In a computational study, the decomposition approach is compared to a mathematical formulation and previous exact and heuristic approaches. Computational results show that the decomposition approach greatly outperforms previous heuristics on the standard benchmarks.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Scheduling ; ISSN: 1099-1425 ; Volume: 23 ; Year: 2020 ; Issue: 5 ; Pages: 539-554 ; New York, NY: Springer US
- Klassifikation
-
Management
- Thema
-
Staff scheduling
Integer programming
Decomposition
Rotating Workforce Scheduling Problem
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Becker, Tristan
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer US
- (wo)
-
New York, NY
- (wann)
-
2020
- DOI
-
doi:10.1007/s10951-020-00659-2
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Becker, Tristan
- Springer US
Entstanden
- 2020
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