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

Algethami, Haneen; Landa-Silva, Dario

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Authors

Haneen Algethami

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DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation



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). Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem. In 2017 IEEE Congress on Evolutionary Computation (CEC 2017) - Proceedings (1771-1778). https://doi.org/10.1109/CEC.2017.7969516

Conference Name 2017 IEEE Congress on Evolutionary Computation (CEC 2017)
Conference Location Donostia, Spain
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 Mar 29, 2024
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.

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