Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem

Algethami, Haneen; Landa-Silva, Dario; Martinez-Gavara, Anna

Abstract

The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including customers and workers preferences while minimising both operational costs and travelling distance. This study seeks to determine effective combinations of genetic operators combined with heuristics that help to find good solutions for this constrained combinatorial optimisation problem. In particular, it aims to identify the best set of operators that help to maximise customers and workers preferences satisfaction. This paper advances the understanding of how to effectively employ different operators within two variants of genetic algorithms to tackle WSRPs. To tackle infeasibility, an initialisation heuristic is used to generate a conflict-free initial plan and a repair heuristic is used to ensure the satisfaction of constraints. Experiments are conducted using three sets of real-world Home Health Care (HHC) planning problem instances.

Citation

Algethami, H., Landa-Silva, D., & Martinez-Gavara, A. (2017). Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem.

• Conference Name: 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017)
• End Date: Feb 25, 2017
• Acceptance Date: Dec 14, 2016
• Publication Date: Feb 23, 2017
• Deposit Date: Mar 27, 2017
• Publicly Available Date: Mar 27, 2017
• Peer Reviewed: Peer Reviewed
• Keywords: Genetic operators, Constraints Satisfaction, Scheduling and Routing Problem, Home Health Care
• Public URL: https://nottingham-repository.worktribe.com/output/844566
• Publisher URL: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006203304160423
• Additional Information: Published in: Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017), SCITEpress, 2017, ISBN 978-989-758-218-9, p. 416-423. DOI:10.5220/0006203304160423.