Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem

Algethami, Haneen; Landa-Silva, Dario

Abstract

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems.

Citation

Algethami, H., & Landa-Silva, D. (2017, June). Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem. Presented at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), Donostia, Spain

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2017 IEEE Congress on Evolutionary Computation (CEC 2017)
• Start Date: Jun 5, 2017
• End Date: Jun 8, 2017
• Acceptance Date: Mar 16, 2017
• Online Publication Date: Jul 7, 2017
• Publication Date: 2017
• Deposit Date: Aug 11, 2017
• Publicly Available Date: Aug 11, 2017
• Peer Reviewed: Peer Reviewed
• Pages: 1771-1778
• Book Title: 2017 IEEE Congress on Evolutionary Computation (CEC 2017) - Proceedings
• ISBN: 978-1-5090-4602-7
• DOI: https://doi.org/10.1109/CEC.2017.7969516
• Keywords: Genetic Algorithms, Adaptive Evolutionary Algorithm,; Workforce Scheduling and Routing
• Public URL: https://nottingham-repository.worktribe.com/output/864230
• Publisher URL: http://ieeexplore.ieee.org/document/7969516/
• Related Public URLs: http://www.cs.nott.ac.uk/~pszjds/research/files/dls_cec2017.pdf
• Additional Information: Published in: 2017 IEEE Congress on Evolutionary Computation (CEC) : proceedings, 5-8 June 2017, San Sebastian, Spain. IEEE, 2017. ISBN 978-1-5090-4601-0.; doi:10.1109/CEC.2017.7969516.; ; © 2017 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.
• Contract Date: Aug 11, 2017