Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29229
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Reid, Kenneth N
Li, Jingpeng
Brownlee, Alexander
Kern, Mathias
Veerapen, Nadarajen
Swan, Jerry
Owusu, Gilbert
Contact Email: knr1@cs.stir.ac.uk
Title: A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem (Forthcoming)
Citation: Reid KN, Li J, Brownlee A, Kern M, Veerapen N, Swan J & Owusu G (2019) A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem (Forthcoming). In: Proceedings of the Genetic and Evolutionary Computation Conference 2019. GECCO '19: The Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM. https://gecco-2019.sigevo.org/index.html/HomePage; https://doi.org/10.1145/3321707.3321769
Conference Name: GECCO '19: The Genetic and Evolutionary Computation Conference 2019
Conference Dates: 2019-07-13 - 2019-07-17
Conference Location: Prague, Czech Republic
Abstract: Employee scheduling problems are of critical importance to large businesses. These problems are hard to solve due to large numbers of conflicting constraints. While many approaches address a subset of these constraints, there is no single approach for simultaneously addressing all of them. We hybridise 'Evolutionary Ruin & Stochastic Recreate' and 'Variable Neighbourhood Search' metaheuristics to solve a real world instance of the employee scheduling problem to near optimality. We compare this with Simulated Annealing, exploring the algorithm configuration space using the irace software package to ensure fair comparison. The hybrid algorithm generates schedules that reduce unmet demand by over 28% compared to the baseline. All data used, where possible, is either directly from the real world engineer scheduling operation of around 25,000 employees , or synthesised from a related distribution where data is unavailable.
Status: VoR - Version of Record
AM - Accepted Manuscript
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URL: https://gecco-2019.sigevo.org/index.html/HomePage

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