Selecting configuration of reverse logistics network using sustainability indicators

Abstract

The network of the reverse logistics (RL) aims to treat and re-inject into the supply chain all that can be valorized from products which are defective, at the end of life, at the end of warranty, at high obsolescence level, etc. The design of that network must take into account many things like the uncertainty about the expected volumes of these products, the forecast of consumer needs customers, producers returns projections, recycling systems, etc. In general, products are not always accompanied with complete data. Those data are often imprecise, hypothetical, inconsistent ... Our literature review is interested mainly in economic viability of an RL organization for a family of products in the context of uncertainty. It shows that almost all research papers do not take into account the incompleteness of the data, do not capitalize on the practices and the historical data of the network and, then, do not consider any indicator of sustainable development (SD). In this paper, we develop a model to select the best reverse logistic network under uncertainty of products returns. This model uses mathematic model and Bayesian network to detect the distribution of used product, integrated in Arena Software to simulate different configurations.