Analysis of Reverse Logistics System using COPRAS MCDM Method

Krishna Kumar, M. Ramachandran, Kurinjimalar Ramu, Ashwini Murugan
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引用次数: 11

Abstract

Reverse logistics, which is product recovery, Due to product returns or inventory management or revenue stream; creates a closed loop supply chain. Close-loop supply chain success means manufacturers and depends on the actions of the customers. Now, due to environmental protection laws, Easy to disassemble, reuse and recycle Products must be manufactured by manufacturers. On the other hand, many customers, By delivering goods to collection points Support environmental protection. According to the findings, the total cost for reverse logistics is huge. Total reverse logistics cost and collection points To reduce the high usage rate, Ideal for collection points in reverse logistics Choosing locations is very important. To design a decision-making model for that, Cost/time, legislative factors, environmental impact, Based on criteria such as quality, market etc Quantitative and qualitative assessment is required. Appropriate based on these criteria Performance must be considered to determine reversal. Manufacturing option is in this domain Depends on expert opinion. In this paper, based on COPRAS set theory A multi-criteria decision-making (MCDM) model is proposed. Remanufacturing, Reselling, Repairing, Cannibalization, Refurbishing is alternatives and Market factor (C1), Quality factor (C2), Legislative impact (C3), Environmental impact(C4), Cost/time factor (C4) is taken for evaluations parameters. As a result the Remanufacturing is in 1st rank and Refurbishing are last rank
用COPRAS MCDM方法分析逆向物流系统
逆向物流,即产品回收,由于产品退货或库存管理或收入流;创建一个闭环供应链。闭环供应链的成功意味着制造商,取决于客户的行动。现在,由于环保法规,易于拆卸、再利用和回收的产品必须由制造商制造。另一方面,许多客户,通过送货到集采点支持环保。根据调查结果,逆向物流的总成本是巨大的。为了降低高使用率,理想的逆向物流集散点选址是非常重要的。为了设计一个决策模型,成本/时间,立法因素,环境影响,基于质量,市场等标准,需要进行定量和定性评估。根据这些标准,必须考虑适当的性能来决定反转。制造选项是在这个领域取决于专家的意见。本文基于COPRAS集合理论,提出了一种多准则决策模型。再制造、转售、维修、拆装、翻新是替代方案,评估参数采用市场因素(C1)、质量因素(C2)、立法影响(C3)、环境影响(C4)、成本/时间因素(C4)。因此,再制造排在第一,翻新排在最后
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