A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption

Abstract

Abstract Due to increased environmental impacts and their important role in human life, reduction of impacts made by human has attracted more attention, recently. Green supply chains are among the most effective issues related to environmental impacts and increased number of studies in this area verifies this opinion. Transportation fleets transfer products between supply chain's centers and are one of the important factors which increase environmental impacts. Transportation fleets which transfer products between supply chain's centers are one of the important factors which increase environmental impacts while transferring products between centers and waiting in loading queue. Decreasing environmental impacts which are created by transportation fleets, from this point of view, is not investigated comprehensively in forward and reverse logistic supply chains. In order to deal with this gap, in this article a green supply chain with forward and reverse logistic consideration is designed and queuing system is used to optimize the transportation and waiting time of transportation fleets' network. This optimization model will lead to the reduction in environmental impacts. Our network consists of supplier, production system, distribution center, repair center, recycling center, disposal center, and collection center. Returned products from customers are collected in the collection center and transferred to other centers based on their type. Transportation fleets in the network are assumed to be customers of loading system in each center where each of these loading systems has a multi-server queuing system with finite sources. It is assumed that a sufficient number of servers are available in unloading centers, therefore, no queue will exist there. The proposed model will reduce the created environmental impacts and energy consumption of transportation fleets by determining loading, unloading and production rates, which affect waiting and transportation time. A numerical example is discussed for the NLP model in small size and solved with the exact methods. In addition, a meta-heuristics approach is employed to solve the large size of problem. Finally, the sensitivity analysis is performed to investigate the effects of change in parameters on model's decision variables and objective function.