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

Abstract

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. 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.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

“…An investigation was presented by [4] comparing various genetic operators within two simple GAs, to tackle the subject problem. A more efficient GA was proposed by [5] with tuned parameters and using a customised solution representation to maintain feasibility of solutions.…”

“…The start location and the end location of worker w are D w and D w , respectively. The condition is enforced by constraints (3) and (4). Workers may leave their start location and enter their end location at most once (although the start and end locations may be different) as expressed by constraints ( 5) and ( 6), respectively.…”

“…Genetic Algorithms (GAs) have been shown to be effective approaches to find solutions for problems combining scheduling and routing where exact methods are less effective, e.g. [2,3,4]. A baseline GA was proposed by [5] that identified the best set of operators and parameters for each instance of the same WSRP tackled here.…”