Green reverse logistics is one of the most important issues in green supply chain management. Reverse logistics is referred to as the process of logistics management involved in planning, managing, and controlling the flow of wastes for either reuse or final disposal of wastes. However, waste recycling often includes hazardous characteristics. Hazardous-waste reverse logistics may be useful for solving waste-induced environmental pollution problems that accompany high-technology industrial development. Reverse logistics network design for waste recycling encompasses decisions on reverse logistics network shape, the topological relationships among reverse logistics centers, and reverse distribution programming. This study developed a series of models to design a network structure of waste reverse logistics and to determine distribution flow for waste recycling.
In the first part of this study, the reverse logistics network shape is designed and formed into a network structure by applying grey clustering. This study defines transportation cost index and risk index. The topological relationships and locations among reverse logistics centers are selected. Using grey clustering, the recycling plants, disassembling plants, recycling companies and the final treatment plants are chosen and determined. In the second part of the study, on the basis of the designed network shape (structure), this study proposes a mathematical programming model to determine the optimal distribution flows on all of the links forming the designed reverse logistics network for waste recycling. The main objective function is to minimize the total cost of reverse logistics as well as the total risk. The total cost includes transportation cost, operating cost and disposal cost. On the other hand, the total risk includes enroute holding risk and the site stock risk. Furthermore, the model is also subjected to flow conservation, capacity of facilities, limit amount of facilities, the environmental regulations, and non-negative constraints. Finally, a case study with a waste computer recycling is provided to illustrate the results and the application of the models. Sensitivity analysis is also discussed. The results of the case study verify that the models are practicable, and also provide higher flexibility on decision-making for reverse logistics services providers.
This study demonstrates how grey clustering and mathematical programming might be applied to the reverse logistics network design problems and discusses many issues in waste recycling reverse logistics. In addition, it is envisaged that the results of this study may shed light on strategic and operational planning related for waste reverse logistics service providers.