Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem

Haneen Algethami, Dario Landa-Silva, Anna Martínez-Gavara

2017

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.

Paper Citation

in Harvard Style

Algethami H., Landa-Silva D. and Martínez-Gavara A. (2017). Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem . In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 416-423. DOI: 10.5220/0006203304160423

in Bibtex Style

@conference{icores17,
author={Haneen Algethami and Dario Landa-Silva and Anna Martínez-Gavara},
title={Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2017},
pages={416-423},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006203304160423},
isbn={978-989-758-218-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Selecting Genetic Operators to Maximise Preference Satisfaction in a Workforce Scheduling and Routing Problem
SN - 978-989-758-218-9
AU - Algethami H.
AU - Landa-Silva D.
AU - Martínez-Gavara A.
PY - 2017
SP - 416
EP - 423
DO - 10.5220/0006203304160423