Fuzzy optimisation approach to supply chain distribution network for product value recovery

Abstract

Efficient integration of forward and reverse logistics network is significant for optimising the economic and ecological value of a closed-loop supply chain network (CLSCN). There is also a high measure of uncertainty involved in such networks and must be handled appropriately in the decisionmaking process. In this study, we propose a fuzzy optimisation model for improving a CLSCN for handling end-of-life (EOL) and end-of-use (EOU) products. Manufacturer uses fabricated components obtained from the recovery processes and procures new components from external suppliers to assemble new products. Suppliers are evaluated using ANP and are assigned weights. Environmental concerns are also addressed by the model by ensuring that the carbon emission due to transportation does not exceed the permissible carbon cap. To achieve this, hybrid distribution-cum-collection centres (HDC) are grouped into optimal clusters. Products are carried along optimal routes within these clusters using travelling salesman problem (TSP). A fuzzy multi-objective mixed integer linear programming model is proposed which aims at minimising cost of the network and maximising weights of suppliers. A case study of printers is considered to validate the model.