Quantitative models for reverse logistics: A review

Abstract

This article surveys the recently emerged field of reverse logistics. The management of return flows induced by the various forms of reuse of products and materials in industrial production processes has received growing attention throughout this decade. Many authors have proposed quantitative models taking those changes in the logistics environment into account. However, no general framework has been suggested yet. Therefore the time seems right for a systematic overview of the issues arising in the context of reverse logistics. In this paper we subdivide the field into three main areas, namely distribution planning, inventory control, and production planning. For each of these we discuss the implications of the emerging reuse efforts, review the mathematical models proposed in the literature, and point out the areas in need of further research. Special attention is paid to differences and/or similarities with classical 'forward' logistics methods.

Review of sustainable service-based business models in the Chinese truck sector

“…Remanufacturing is seen as the most economically profitable and environmentally conscious option because it restores the vehicle original function with a quality level equivalent to a new product (Fleischmann et al, 1997;Guide et al, 1999). Therefore a business model including a result-oriented PSS as value proposition and operating with a CLSC could be a promising option to improve the sustainability of the truck sector.…”

Intelligent Products to Support Closed-Loop Reverse Logistics

“…the expended product is transported "back" either to the beginning of a "new lifecycle" or towards its terminal disposal. [22], [23] Both positions are summarised by Fleischmann, et al [24] Here, reverse logistics "encompasses the logistics activities all the way from used products to no longer required by the user products to products again useable in the markets." This approach to reverse logistics closes the loop in the material flow from EOL to BOL.…”

Mean-Variance Analysis for Optimal Operation and Supply Chain Coordination in a Green Supply Chain

“…For the above topics, many previous studies regarding a GSC determined the optimal operations in the GSC so as to maximize the expected profit or minimize the expected cost under the uncertainties (Schenkel et al, 2015). There are some overviews of above topics (Fleischmann et al, 1997;Guide and Wassenhove, 2009;Souza, 2013;Govindan et al, 2013;Stindt and Sahamie, 2014;Bazan et al, 2016: Thiripura Sundari andVijayalakshmi, 2016). However, only considering the expectation for decision-making is insufficient because it ignores the risk attitude of the decision makers (Choi et al, 2008a;Chiu and Choi, 2016).…”