Exact Methods for Extended Rotating Workforce Scheduling Problems

Kletzander, Lucas; Musliu, Nysret; Gärtner, Johannes; Krennwallner, Thomas; Schafhauser, Werner

doi:10.1609/icaps.v29i1.3518

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…The second domain deals with optimization variants of Rotating Workforce Scheduling (Kletzander et al 2019), where the goal is the assignment of shifts to employees according to rules for consecutive assignments. In case of this domain, LAST-RL could not provide improved results compared to GIHH and L-GIHH.…”

Section: Evaluation On Real-life Scheduling Domainsmentioning

confidence: 99%

Large-State Reinforcement Learning for Hyper-Heuristics

Kletzander

Musliu

2023

AAAI

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Hyper-heuristics are a domain-independent problem solving approach where the main task is to select effective chains of problem-specific low-level heuristics on the fly for an unseen instance. This task can be seen as a reinforcement learning problem, however, the information available to the hyper-heuristic is very limited, usually leading to very limited state representations. In this work, for the first time we use the trajectory of solution changes for a larger set of features for reinforcement learning in the novel hyper-heuristic LAST-RL (Large-State Reinforcement Learning). Further, we introduce a probability distribution for the exploration case in our epsilon-greedy policy that is based on the idea of Iterated Local Search to increase the chance to sample good chains of low-level heuristics. The benefit of the collaboration of our novel components is shown on the academic benchmark of the Cross Domain Heuristic Challenge 2011 consisting of six different problem domains. Our approach can provide state-of-the-art results on this benchmark where it outperforms recent hyper-heuristics based on reinforcement learning, and also demonstrates high performance on a benchmark of complex real-life personnel scheduling domains.

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Section: Evaluation On Real-life Scheduling Domainsmentioning

confidence: 99%

Large-State Reinforcement Learning for Hyper-Heuristics

Kletzander

Musliu

2023

AAAI

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Instance space analysis for a personnel scheduling problem

Kletzander

Musliu

Smith‐Miles

2020

Ann Math Artif Intell

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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.

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Exact Methods for Extended Rotating Workforce Scheduling Problems

Cited by 2 publications

References 16 publications

Large-State Reinforcement Learning for Hyper-Heuristics

Large-State Reinforcement Learning for Hyper-Heuristics

Instance space analysis for a personnel scheduling problem

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