Instance space analysis for a personnel scheduling problem

This paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the in-depth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming...

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Veröffentlicht in:Annals of mathematics and artificial intelligence Jg. 89; H. 7; S. 617 - 637
Hauptverfasser: Kletzander, Lucas, Musliu, Nysret, Smith-Miles, Kate
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.07.2021
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Artificial Intelligence
Complex Systems
Computer Science
Forecasts and trends
Hardness
Mathematics
Mechanical properties
Phase transitions
Scheduling
Visual observation
68W40
68T20
68T05
Algorithm selection
Instance space
Combinatorial optimization
Personnel scheduling
ISSN:1012-2443, 1573-7470
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Zusammenfassung:This paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the in-depth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming to describe hardness of test instances. We create a new, more diverse set of instances based on an initial instance space analysis that reveals gaps in the instance space, and offers the opportunity to generate additional instances to add diversity to the test suite. The results of three algorithms on our extended instance set reveal insights based on this visual methodology. We observe different regions of strength and weakness in the instance space for each algorithm, as well as a phase transition from feasible to infeasible instances with more challenging instances at the phase transition boundary. This rigorous and insightful approach to analyzing algorithm performance highlights the critical role played by the choice of test instances, and the importance of ensuring diversity and unbiasedness of test instances to support valid conclusions.
Bibliographie:ObjectType-Article-1
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-020-09695-2