Fuzzy Possibilistic Modeling for Closed Loop Recycling Collection Networks: Fid-Bau Portal

Fuzzy Possibilistic Modeling for Closed Loop Recycling Collection Networks

Vahdani, Behnam / Razmi, Jafar / Tavakkoli-Moghaddam, Reza

Abstract Nowadays, in the competitive market, commercial companies due to their economic problems and also restrictions imposed by international organizations to comply with environmental regulations are making noticeable efforts to reduce the level of their wastes in their manufacturing systems and consequently the level of waste in consumers’ products. In harmony with this issue, one of the most effective ways which has successfully been used and proven to be economically profitable is to take products lifecycles into consideration. Consideration of products lifecycle has made supply chain practitioners to investigate the reverse logistics activities in addition to forward logistics activities. Hence, corporations, in order to reduce their cost on the one hand and boosting their efficiency on the other hand, were obligated to employ closed loop supply chain models to concurrently benefit from its economical and environmental advantages. Therefore, in this paper, a mixed nonlinear facility location–allocation model is proposed for recycling collection centers. The considered closed loop logistics model consists of multiple echelons, multiple suppliers, multiple collection centers, multiple time period and also multiple facilities. In real-life problems parameters like demand, cost, capacity, distances, and quantities of returned products are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mixed nonlinear programming model, one of the most effective technique in the literature is used. Additionally, in order to demonstrate the behavior of the parameters employed in the model, a comprehensive sensitivity analysis is conducted. Computational results demonstrate that the proposed model can show favorable efficiency in solving supply chain problems.