A decomposition heuristic for rotational workforce scheduling

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Scheduling ; ISSN: 1099-1425 ; Volume: 23 ; Year: 2020 ; Issue: 5 ; Pages: 539-554 ; New York, NY: Springer US

Classification and Topics

• Classification: Management
• Subject: Staff scheduling ; Integer programming ; Decomposition ; Rotating Workforce Scheduling Problem

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Becker, Tristan
• Event: Veröffentlichung
• (who): Springer US
• (where): New York, NY
• (when): 2020

Further information

• DOI: doi:10.1007/s10951-020-00659-2
• Last update: 10.03.2025, 11:44 AM CET