A Decision Model for Reverse Logistics Service Providers in Determining Robust Optimal Processing Quantities of Returned Products

逆物流服務商穩健最佳退回商品處理量之決策模式

10.29977/JCIIE.200909.0008

林我聰(Woo-Tsong Lin)；李惠卿(Huei-Ching Lee)；李亞暉(Ya-Hui Lee)

逆物流服務 ； 決策模式 ； 穩健最佳化 ； reverse logistics service ； decision model ； robust optimization

工業工程學刊

26卷5期（2009 / 09 / 01）

397 - 410

英文

逆物流(reverse logistics)爲將產品從消費者處回收，並將此資源再利用之一連串活動；其相關成本往往比正物流(forward logistics)高，且對於回收之產品，在運送、儲存、處理、管理方面亦無規律通路，較正物流增加了許多的複雜性和不確定性；企業往往將這些活動外包給專業逆物流服務商(reverse logistics service providers)。而逆物流服務商亦有其利潤、成本、相關法規之考量，過去此方面研究多以逆物流服務商之回收處理廠的廠址選擇及設置爲主。本研究提出一決策模式，針對擁有多個處理廠的逆物流服務商，於考慮具不確定性及多時期、多型態的退回商品時，幫助其決定最適再生物料收受訂單數量及個別逆物流處理中心之最適處理量。因應模式中不確定因子，本研究採用以情境爲基礎的穩健最佳化(robust optimization)方法求得模式的穩健解。

Reverse logistics covers a serial of activities in dealing with returned products from consumers, including collecting, reusing and recycling. Implementing reverse logistics is much more complicated and expensive than forward logistics to an enterprise. Meanwhile, the systematic patterns for handling transportation, storage, processing and management processes of these activities are still called for. Consequently, to reduce the reverse logistics cost and focus on its core business, an enterprise prefers outsourcing these activities in this manner. Previous studies focused on the selection of processing facilities and the infrastructure design of reverse logistics distribution channels for third-party reverse logistics service providers. In contrast, this research aims to deal with the issues of reverse logistics from different viewpoint. We propose a decision model for a reverse logistics service provider under the context of uncertain, multi-period, multi-type returned/recycled products and multiple processing facilities environment. The major focus of this model is on determining the robust optimal quantities of customer orders and robust optimal processing quantities of returned products for each processing facility. To deal with the issues of uncertainties, the model applies the scenario-based robust optimization approach. Further information on experiment results and implications can be found in this paper.

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