Title :

A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem

Author(s) :

Reid, Kenneth [Auteur]
University of Stirling
Li, Jingpeng [Auteur]
University of Stirling
Brownlee, Alexander E.I. [Auteur]
University of Stirling
Kern, Mathias [Auteur]
Veerapen, Nadarajen [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Swan, Jerry [Auteur]
University of Stirling
Owusu, Gilbert [Auteur]

Conference title :

GECCO'19 (2019 Genetic and Evolutionary Computation Conference)

Conference organizers(s) :

ACM

City :

Prague

Country :

République tchèque

Start date of the conference :

2019-07-13

Book title :

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Journal title :

Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Publication date :

2019-07

English keyword(s) :

Evolutionary Ruin and Stochastic Recreate
Metaheuristics
Employee Scheduling
Variable Neighbourhood Search

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Computer Science [cs]/Operations Research [math.OC]

English abstract : [en]

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, ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL