Version 1
: Received: 11 April 2023 / Approved: 12 April 2023 / Online: 12 April 2023 (04:55:56 CEST)
Version 2
: Received: 19 April 2023 / Approved: 20 April 2023 / Online: 20 April 2023 (05:21:25 CEST)
Version 3
: Received: 21 April 2023 / Approved: 21 April 2023 / Online: 21 April 2023 (09:46:54 CEST)
Version 4
: Received: 30 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (05:20:52 CEST)
How to cite:
Rocha, L. Solution of the Capacitated Lot-Sizing Problem with Remanufacturing (CLSPR) in a General Way with the Help of Simulation and Relaxation. Preprints2023, 2023040242. https://doi.org/10.20944/preprints202304.0242.v4
Rocha, L. Solution of the Capacitated Lot-Sizing Problem with Remanufacturing (CLSPR) in a General Way with the Help of Simulation and Relaxation. Preprints 2023, 2023040242. https://doi.org/10.20944/preprints202304.0242.v4
Rocha, L. Solution of the Capacitated Lot-Sizing Problem with Remanufacturing (CLSPR) in a General Way with the Help of Simulation and Relaxation. Preprints2023, 2023040242. https://doi.org/10.20944/preprints202304.0242.v4
APA Style
Rocha, L. (2024). Solution of the Capacitated Lot-Sizing Problem with Remanufacturing (CLSPR) in a General Way with the Help of Simulation and Relaxation. Preprints. https://doi.org/10.20944/preprints202304.0242.v4
Chicago/Turabian Style
Rocha, L. 2024 "Solution of the Capacitated Lot-Sizing Problem with Remanufacturing (CLSPR) in a General Way with the Help of Simulation and Relaxation" Preprints. https://doi.org/10.20944/preprints202304.0242.v4
Abstract
The capacitated lot-sizing problem with product recovery (CLSP-RM) holds significant importance in reverse logistics but is notoriously complex (NP-hard). In this study, two techniques are introduced to confront this challenge. The first technique entails devising a linear optimization task that eliminates capacity limitations across a wide problem spectrum, yielding a remarkably accurate approximation of the optimal solution. This adaptable approach presents a potent alternative and holds potential for extension to diverse problem categories owing to its versatile nature. The second technique employs a simulation methodology utilizing Halton’s uniform random numbers to address the issue. This randomized production search method sidesteps considerations of production costs, inventory expenditures, and production order when determining production batches. The research’s novelty lies in its application of these techniques to the problem. The suggested methods undergo evaluation via a benchmark dataset of approximately 4200 instances, with comparison against solutions derived through the Gurobi solver. The results underscore the efficacy and resilience of the introduced methodology in tackling the CLSP-RM predicament.
Keywords
simulation based optimization; capacitated lot-sizing problem; heuristics; remanufacturing
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
