Multi-time Scale Attention Network for WEEE reverse logistics return prediction

Zhang, Jia; Gao, Min; Zhao, Liang; Hu, Jiaqi; Gao, Jinyong; Deng, Meiling; Chen, Wan; Yang, Linda

doi:10.1016/j.eswa.2022.118610

Cited by 4 publications

(1 citation statement)

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“…Yu et al [47] established a logistics distribution function and used a statistical model to predict and analyze the future trend of the number of ELVs in China. Zhang et al [48] proposed a multi-time scale attention network model to predict the recycling of waste electrical and electronic equipment.…”

Section: Reverse Logistics Network Designmentioning

confidence: 99%

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

He,

Li,

Lin

et al. 2024

WEVJ

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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.

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Section: Reverse Logistics Network Designmentioning

confidence: 99%