A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem

Kenneth N. Reid (1) , Jingpeng Li (1) , Alexander E.I. Brownlee (1) , Mathias Kern (2) , Nadarajen Veerapen (3) , Jerry Swan (1) , Gilbert Owusu (2)

1 University of Stirling
2 BT - British Telecom
3 ORKAD - Operational Research, Knowledge And Data

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.

Mots clés: Evolutionary Ruin and Stochastic Recreate; Metaheuristics; Employee Scheduling; Variable Neighbourhood Search

Domaines: Intelligence artificielle [cs.AI]; Recherche opérationnelle [math.OC]

Kenneth N. Reid, Jingpeng Li, Alexander E.I. Brownlee, Mathias Kern, Nadarajen Veerapen, et al.. A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem. GECCO'19 (2019 Genetic and Evolutionary Computation Conference), ACM, Jul 2019, Prague, Czech Republic. ⟨10.1145/3321707.3321769⟩. ⟨hal-02100461⟩