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Reducing waste in manufacturing operations: bi-objective scheduling on a single-machine with coupled-tasks

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  • Corentin Le Hesran
  • Aayush Agarwal
  • Anne-Laure Ladier
  • Valérie Botta-Genoulaz
  • Valérie Laforest

Abstract

This study addresses a scheduling problem involving a single-machine with coupled-tasks and bi-objective optimisation considering simultaneously inventory and environmental waste. A Mixed Integer Linear Program representing the problem is first developed. Subsequently, a Genetic Algorithm (GA) is presented, followed by numerical experiments on multiple instances. Pareto fronts are determined using the ϵ-constraint and weighted sum methods, and a trade-off point is selected according to a distance criterion. Numerical experiments on both small and large instances show near-optimal results for small instances, and considerably reduced computing times for large ones when using the GA. The results show that a compromise can be found, with a decrease in setup-related waste up to 36% for an increase of inventory of 12%. This will help decision-makers to better consider the environmental aspect when designing schedules, as well as reduce their production environmental impact and waste-management costs.

Suggested Citation

  • Corentin Le Hesran & Aayush Agarwal & Anne-Laure Ladier & Valérie Botta-Genoulaz & Valérie Laforest, 2020. "Reducing waste in manufacturing operations: bi-objective scheduling on a single-machine with coupled-tasks," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7130-7148, December.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:23:p:7130-7148
    DOI: 10.1080/00207543.2019.1693653
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    Cited by:

    1. Nazim Sami & Karim Amrouche & Mourad Boudhar, 2024. "New efficient algorithms for the two-machine no-wait chain-reentrant shop problem," Journal of Combinatorial Optimization, Springer, vol. 47(5), pages 1-29, July.

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