Personalized Multi-Stage Decision Support in Reverse Logistics Management

Abstract

Reverse logistics has gained increasing importance as a profitable and sustainable business strategy. As a reverse logistics chain has strong internal and external linkages, the management of a reverse logistics chain becomes an area of organizational competitive advantage, in particular, with the growth of e-commerce applications. To effectively manage a reverse logistics chain always involves a decision optimization issue in which uncertain information, individual situation, multiple criteria and dynamic environment all need to be considered. This paper addresses the need of supporting reverse logistics managers in selecting an optimal alternative for goods return under their business objectives. Through analyzing the characteristics of reverse logistics chain, this paper proposes a personalized multi-stage decisionsupport model for reverse logistics management. It then presents a personalized fuzzy multi-criteria decision-making approach to assist managers to lead and control the reverse logistics within an uncertain and dynamic system.