Integrated decision-making in reverse logistics: an optimisation of interacting acquisition, grading and disposition processes
Integrated decision-making in reverse logistics: an optimisation of interacting acquisition, grading and disposition processes
复制标题
DOI：
10.1080/00207543.2019.1659518
复制
发表时间：
2020-10
影响因子：
9.2
通讯作者：
Gernot Lechner;M. Reimann
中科院分区：
文献类型：
--
作者：
Gernot Lechner;M. Reimann
鉴于全球环境和社会挑战，向循环经济过渡被视为可持续发展的一个关键因素。因此，用注重资源再利用的概念取代传统的线性商业模式，包括在产品生命周期结束时丢弃产品，这是至关重要的。逆向物流和闭环供应链被认为是这种转变的关键要素。基于对一家独立后处理企业的案例研究，本文研究了逆向物流中的集成决策问题。我们提出了一个包含相关过程的非线性优化模型，包括废旧产品的获取、用于确定产品质量的分级和后处理处理。要作出的决定涉及积极获取二手产品所花费的努力和再加工产品的数量；这两项决定都受到二手产品的不同状况的影响。对确定性和随机性需求的考虑有助于各种业务案例的表示。对于这两种需求类型，我们以完整战略的形式提供分析见解，这些战略由不同的情景组成，允许在不同条件下做出最佳决策。数值算例通过对相关模型参数进行敏感性分析，补充了对模型的认识。
In view of global environmental and social challenges the transition towards a Circular Economy is considered as a crucial factor for sustainable development. Therefore, the replacement of traditional linear business models involving product discard at the end of product life with concepts focusing on re-use of resources is essential. Reverse Logistics and Closed-loop Supply Chains are seen to be key elements of such a transition. Motivated by findings from a case study of an independent reprocessing company, we address integrated decision-making in Reverse Logistics in this paper. We present a non-linear optimisation model with interrelated processes in terms of acquisition of used products, grading for determination of product quality and reprocessing disposition. The decisions to be made concern the effort spent for active acquisition of used products and the number of reprocessed goods; both decisions are influenced by heterogeneous condition of used products. The consideration of deterministic and stochastic demand facilitates the representation of a variety of business cases. For both demand types we provide analytical insights in the form of complete strategies consisting of different scenarios which allow optimal decision-making under variable conditions. Numerical examples complement insights into the model by conducting a sensitivity analysis of relevant model parameters.