Designing a two-stage model for a sustainable closed-loop electric vehicle battery supply chain network: A scenario-based stochastic programming approach

[Display omitted] •Designing a sustainable closed-loop supply chain network for the electric vehicle battery industry.•Considering resilience and safety stock strategies to demand fluctuations in the two-stage model.•Considering sustainability by covering total cost, energy consumption, carbon emiss...

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Veröffentlicht in:	Computers & industrial engineering Jg. 190; S. 110036
Hauptverfasser:	Saeedi, Mehran, Parhazeh, Sina, Tavakkoli-Moghaddam, Reza, Khalili-Fard, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2024
Schlagworte:	Electric vehicle battery Meta-heuristic algorithms Multi-objective optimization Sustainable closed-loop supply chain Two-stage stochastic programming Electric vehicle battery Multi-objective optimization Sustainable closed-loop supply chain Meta-heuristic algorithms Two-stage stochastic programming
ISSN:	0360-8352, 1879-0550
Online-Zugang:	Volltext
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Zusammenfassung:	[Display omitted] •Designing a sustainable closed-loop supply chain network for the electric vehicle battery industry.•Considering resilience and safety stock strategies to demand fluctuations in the two-stage model.•Considering sustainability by covering total cost, energy consumption, carbon emissions, and job creation.•Using a scenario-based stochastic programming approach to tackle uncertainty in the presented model.•Proposing three meta-heuristic algorithms to solve large-sized problems with a case study. Transportation is a fundamental requirement of modern life. Vehicles powered by fossil fuels are highly polluting. This study develops a two-stage stochastic programming model to establish a sustainable closed-loop supply chain for Electric Vehicle (EV) batteries. The model considers economic, environmental, and social criteria, including cost, energy consumption, carbon emissions, and job creation. The ε-constraint method and three multi-objective meta-heuristic algorithms are utilized to solve problems. Implementing this model in a case study of an EV battery supply chain aids managerial decision-making for optimal center establishment, flow determination, and inventory setting. Finally, essential parameters are analyzed, and several important managerial insights are prepared. The results suggest that investing in used battery collection significantly reduces costs and carbon emissions.
ISSN:	0360-8352 1879-0550
DOI:	10.1016/j.cie.2024.110036