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

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.
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https://hal-emse.ccsd.cnrs.fr/emse-02360718
Contributor : Florent Breuil <>
Submitted on : Wednesday, November 13, 2019 - 8:31:30 AM
Last modification on : Thursday, April 30, 2020 - 10:26:04 AM

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Corentin Le Hesran, Aayush Agarwal, Anne-Laure Ladier, Valerie Botta-Genoulaz, Valérie Laforest. Reducing waste in manufacturing operations: bi-objective scheduling on a single-machine with coupled-tasks. International Journal of Production Research, Taylor & Francis, 2019, ⟨10.1080/00207543.2019.1693653⟩. ⟨emse-02360718⟩

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