A Genetic Algorithm for a Workforce Scheduling and Routing Problem

Algethami, Haneen, Pinheiro, Rodrigo Lankaites and Landa-Silva, Dario (2016) A Genetic Algorithm for a Workforce Scheduling and Routing Problem. In: IEEE Congress on Evolutionary Computation (IEEE CEC 2016), 25-29 July 2016, Vancouver, Canada.

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The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling scheduling and routing constraints while aiming to minimise the total operational cost. This paper presents a Genetic Algorithm (GA) tailored to tackle a set of real-world instances of this problem. The proposed GA uses a customised chromosome representation to maintain the feasibility of solutions. The performance of several genetic operators is investigated in relation to the tailored chromosome representation. This paper also presents a study of parameter settings for the proposed GA in relation to the various problem instances considered. Results show that the proposed GA, which incorporates tailored components, performs very well and is an effective baseline evolutionary algorithm for this difficult problem.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/799547
Additional Information: ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Genetic Algorithms, Indirect Solution Representation, Genetic Operators, Workforce Scheduling and Routing
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
Depositing User: Landa-Silva, Dario
Date Deposited: 13 Sep 2016 10:35
Last Modified: 04 May 2020 17:59
URI: https://eprints.nottingham.ac.uk/id/eprint/35583

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