An assessment of a days off decomposition approach to personnel shift scheduling

This paper studies a two-phase decomposition approach to solving the personnel scheduling problem. The first phase creates a days-off-schedule, indicating working days and days off for each employee. The second phase assigns shifts to the working days in the days-off-schedule. This decomposition is...

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Bibliographic Details
Published in:Annals of operations research 2016-04, Vol.239 (1), p.207-223
Main Authors: van Veldhoven, Sophie, Post, Gerhard, van der Veen, Egbert, Curtois, Tim
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Assessments
Business and Management
Categories
Combinatorics
Decomposition
Decomposition (Mathematics)
Employees
Heuristic
Integer programming
Linear programming
Management
Mathematical analysis
Mathematical programming
Methods
Operations research
Operations Research/Decision Theory
Personnel
Phase shift
Planning
Schedules
Scheduling
Scheduling (Management)
Shift work
Studies
Theory of Computation
Work hours
Workforce planning
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Summary:This paper studies a two-phase decomposition approach to solving the personnel scheduling problem. The first phase creates a days-off-schedule, indicating working days and days off for each employee. The second phase assigns shifts to the working days in the days-off-schedule. This decomposition is motivated by the fact that personnel scheduling constraints are often divided into two categories: one specifies constraints on working days and days off, while the other specifies constraints on shift assignments. To assess the consequences of the decomposition approach, we apply it to public benchmark instances, and compare this to solving the personnel scheduling problem directly. In all steps we use mathematical programming. We also study the extension that includes night shifts in the first phase of the decomposition. We present a detailed results analysis, and analyze the effect of various instance parameters on the decompositions’ results. In general, we observe that the decompositions significantly reduce the computation time, but the quality, though often good, depends strongly on the instance at hand. Our analysis identifies which aspects in the instance can jeopardize the quality.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-014-1674-7