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Agent-based model for end-of-life product flow analysis

Alexandre Labelle et Jean-Marc Frayret

Article de revue (2018)

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Abstract

This paper presents an agent-based simulation model for end-of-life product flow analysis in recuperation and recycling supply networks that focuses on individual consumer behaviors. The simulation model is applied to a deposit-return program on wine bottles that could be developed in the province of Quebec. Canadian data was used to calibrate and validate the model. A series of experiments was then conducted with three artificial populations to analyse how they would react to several implementation scenarios of this end-of-life product flow strategy. The results suggest that the distance to the nearest depot is an important decision factor, but less predominant than the ownership of a private vehicle and the deposit value. The results also indicate that the use of agent-based modeling combined with the theory of planned behavior (TPB) can produce modular behavior models, that are intuitive and simple, to better understand consumer-behavior-driven supply chains. Such models can be used to give insights to decision-makers and policy-makers about the potential performance of end-of-life product flows strategies and further facilitate efficient resource management.

Mots clés

municipal solid waste; consumer behavior; agent-based modeling; simulation; deposit-return program; end-of-life product flow; recycling

Sujet(s): 1600 Génie industriel > 1600 Génie industriel
1600 Génie industriel > 1603 Logistique
1600 Génie industriel > 1604 Fabrication
Département: Département de mathématiques et de génie industriel
URL de PolyPublie: https://publications.polymtl.ca/3563/
Titre de la revue: Resources (vol. 7, no 3)
Maison d'édition: MDPI
DOI: 10.3390/resources7030042
URL officielle: https://doi.org/10.3390/resources7030042
Date du dépôt: 09 mars 2020 10:50
Dernière modification: 16 avr. 2024 13:00
Citer en APA 7: Labelle, A., & Frayret, J.-M. (2018). Agent-based model for end-of-life product flow analysis. Resources, 7(3), 1-20. https://doi.org/10.3390/resources7030042

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