Many-objective Optimisation for an Integrated Supply Chain Management Problem

Türk, Seda; Özcan, Ender; John, Robert

Abstract

Due to the complexity of the supply chain with multiple conflicting objectives requiring a search for a set of trade-off solutions, there has been a range of studies applying multi-objective methods. In recent years, there has been a growing interest in the area of many-objective (four or more objectives) optimisation which handles difficulties that multi-objective methods are not able to overcome. In this study, we explore formulation of Supply Chain Management (SCM) problem in terms of the possibility of having conflicting objectives. Non-dominated Sorting Genetic Algorithm-III (NSGA-III) is used as a many-objective algorithm. First, to make an effective search and to reach solutions with better quality, parameters of algorithm are tuned. After parameter tuning, we used NSGA-III at its best performance and tested it on twenty four synthetic and real-world problem instances considering three performance metrics, hypervolume, generational distance and inverted generational distance.

Citation

Türk, S., Özcan, E., & John, R. (2021). Many-objective Optimisation for an Integrated Supply Chain Management Problem. In R. Matoušek, & J. Kůdela (Eds.), Recent Advances in Soft Computing and Cybernetics (97-111). Springer Nature. https://doi.org/10.1007/978-3-030-61659-5_9

• Online Publication Date: Feb 6, 2021
• Publication Date: 2021
• Deposit Date: Dec 3, 2024
• Publisher: Springer Nature
• Peer Reviewed: Peer Reviewed
• Pages: 97-111
• Series Title: Studies in Fuzziness and Soft Computing
• Series Number: 403
• Series ISSN: 1434-9922
• Book Title: Recent Advances in Soft Computing and Cybernetics
• ISBN: 9783030616588
• DOI: https://doi.org/10.1007/978-3-030-61659-5_9
• Public URL: https://nottingham-repository.worktribe.com/output/30153122
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-61659-5_9