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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computers & industrial engineering Ročník 190; s. 110036
Hlavní autoři:	Saeedi, Mehran, Parhazeh, Sina, Tavakkoli-Moghaddam, Reza, Khalili-Fard, Alireza
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2024
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	[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