Sample Average Approximation for Multi-Vehicle Collection-Disassembly Problem under Uncertainty

Muhammad Khoirul Khakim Habibi (1) , Olga Battaïa (2) , Van-Dat Cung (3) , Alexandre Dolgui (4, 5) , Manoj Kumar Tiwari (6)

1 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
2 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
3 G-SCOP_ROSP - Recherche Opérationnelle pour les Systèmes de Production
4 IMT Atlantique - DAPI - Département Automatique, Productique et Informatique
5 LS2N - équipe SLP - Systèmes Logistiques et de Production
6 IIT Kharagpur - Indian Institute of Technology Kharagpur

The implementation of the circular economy is increasingly supported by many governments. It is performed by integrating the activities of reverse supply chain into those of forward supply chain. However, many companies that traditionally focus on the activities of forward supply chain have decided to collaborate with third-party reverse logistics providers to manage the reverse supply chain. This collaboration motivates the work presented in this paper to propose better decisions for decision makers in the providers under the fact that integrating decisions of the collection of End-of-Life products and their disassembly process proposes a reverse supply chain with better performance. In this paper, an integrated problem concerning those decisions is presented and formalised. It also deals with the uncertainty of the quality and the quantity of products as well as the demands of the associated components. Two approximate methods are developed to provide the solutions.

Mots clés: Stochastic Programming; Sample Average Approximation; Disassembly; Reverse Supply Chain; Collection; Two-Phase Iterative Heuristic

Domaines: Automatique; Recherche opérationnelle [math.OC]

Muhammad Khoirul Khakim Habibi, Olga Battaïa, Van-Dat Cung, Alexandre Dolgui, Manoj Kumar Tiwari. Sample Average Approximation for Multi-Vehicle Collection-Disassembly Problem under Uncertainty. International Journal of Production Research, 2019, 57 (8), pp.2409-2428. ⟨10.1080/00207543.2018.1519262⟩. ⟨hal-02434343⟩