Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem
Algethami, Haneen; Landa-Silva, Dario
DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation
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.
|Publication Date||Jun 5, 2017|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Algethami, H., & Landa-Silva, D. (2017). Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem|
|Keywords||Genetic Algorithms, Adaptive Evolutionary Algorithm,
Workforce Scheduling and Routing
|Related Public URLs||http://www.cs.nott.ac.uk/~pszjds/research/files/dls_cec2017.pdf|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.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.
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Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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