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Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem.

Ogunsemi, Akinola; McCall, John; Kern, Mathias; Lacroix, Benjamin; Corsar, David; Owusu, Gilbert

Authors

Akinola Ogunsemi

Mathias Kern

Benjamin Lacroix

Gilbert Owusu



Contributors

Jonathan E. Fieldsend
Editor

Abstract

There is a growing literature spanning several research communities that studies multiple optimisation problems whose solutions interact, thereby leading researchers to consider suitable approaches to joint solution. Real-world problems, like supply chain, are systems characterised by such interactions. A decision made at one point of the supply chain could have significant consequence that ripples through linked production and transportation systems. Such interactions would require complex algorithmic designs. This paper, therefore, investigates the linkages between a facility location and permutation flow shop scheduling problems of a distributed manufacturing system with identical factory (FLPPFSP). We formulate a novel mathematical model from a linked optimisation perspective with objectives of minimising facility cost and makespan. We present three algorithmic approaches in tackling FLPPFSP; Non-dominated Sorting Genetic Algorithm for Linked Problem (NSGALP), Multi-Criteria Ranking Genetic Algorithm for Linked Problem (MCRGALP), and Sequential approach. To understand FLPPFSP linkages, we conduct a pre-assessment by randomly generating 10000 solution pairs on all combined problem instances and compute their respective correlation coefficients. Finally, we conduct experiments to compare results obtained by the selected algorithmic methods on 620 combined problem instances. Empirical results demonstrate that NSGALP outperforms the other two methods based on relative hypervolume, hypervolume and epsilon metrics.

Citation

OGUNSEMI, A., MCCALL, J., KERN, M., LACROIX, B., CORSAR, D. and OWUSU, G. 2022. Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem. In Fieldsend, J. (ed.) GECCO'22 companion: proceedings of 2022 Genetic and evolutionary computation conference companion, 9-13 July 2022, Boston, USA, [virtual event]. New York: ACM [online], pages 735-738. Available from: https://doi.org/10.1145/3520304.3529033

Conference Name 2022 Genetic and evolutionary computation conference (GECCO '22)
Conference Location Boston, USA
Start Date Jul 9, 2022
End Date Jul 13, 2022
Acceptance Date Mar 22, 2022
Online Publication Date Jul 19, 2022
Publication Date Jul 31, 2022
Deposit Date Sep 2, 2022
Publicly Available Date Sep 2, 2022
Publisher ACM Association for Computing Machinery
Pages 735-738
Book Title GECCO '22 companion: proceedings of the genetic and evolutionary computation conference
ISBN 9781450392686
DOI https://doi.org/10.1145/3520304.3529033
Keywords Genetic algorithm; Linked optimisation; Multi-criteria decision-making; Scheduling and planning
Public URL https://rgu-repository.worktribe.com/output/1742130

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