Designing a Reverse Logistics Network for End-of-Life Vehicles in an Uncertain Environment

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) m...

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Bibliographic Details
Published in:World electric vehicle journal 2024-04, Vol.15 (4), p.140
Main Authors: He, Meiling, Li, Qipeng, Lin, Tianhe, Fan, Jiangyang, Wu, Xiaohui, Han, Xun
Format: Article
Language:English
Subjects:
Algorithms
Confidence intervals
Construction
Costs
End of life
end-of-life vehicles
Forecasting
grey–Markov combination model
Integer programming
Linear programming
Machine tools
Machining
Machining centres
Mixed integer
Nonlinear programming
Planning
Profits
Recycling
Recycling centers
Remanufacturing
Reverse logistics
reverse logistics network
Sensitivity analysis
Site selection
uncertainty
Used goods
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Summary:The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj15040140