Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem

Algethami, Haneen and Landa-Silva, Dario (2017) Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem. In: 2017 IEEE Congress on Evolutionary Computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain.

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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.

Item Type: Conference or Workshop Item (Paper)
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.
Keywords: Genetic Algorithms, Adaptive Evolutionary Algorithm, 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: 11 Aug 2017 08:14
Last Modified: 13 Oct 2017 01:17
URI: http://eprints.nottingham.ac.uk/id/eprint/41542

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