Multi-objective Genetic Algorithm to Reduce Setup Waste in a Single Machine with Coupled-Tasks Scheduling Problem - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

Multi-objective Genetic Algorithm to Reduce Setup Waste in a Single Machine with Coupled-Tasks Scheduling Problem

Abstract

This article studies a single-machine scheduling problem involving coupled-tasks and hard due dates. A genetic algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA) II model is proposed to carry out a bi-objective optimization of both holding cost and setup-related waste generation. Results show that the multi-objective genetic algorithm outperforms the previous approaches regarding both computation time and objective functions, showing that a reduction of setups of 36% is possible at the expense of an 11% increase in inventory with acceptable computation times. It also highlights the importance of multi-objective optimization for decision-making in case of conflicting objective functions.
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Dates and versions

hal-03351276 , version 1 (15-02-2023)

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Corentin Le Hesran, Anne-Laure Ladier, Valérie Botta-Genoulaz. Multi-objective Genetic Algorithm to Reduce Setup Waste in a Single Machine with Coupled-Tasks Scheduling Problem. IFIP International Conference on Advances in Production Management Systems, Sep 2021, Nantes, France. pp.399-408, ⟨10.1007/978-3-030-85874-2_42⟩. ⟨hal-03351276⟩
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