To develop an integrated model for green supply chain

Abstract

Supply chain management is a set of techniques used for effective and efficient integration of suppliers, manufacturers, warehouses and dealers in such a way that system costs to be minimized and goods service needs to be realized with the correct number in the right place and at the right time. Since the important role of three factors of localization, routing and assignment is not covered in the survival of a supply chain life, therefore, integration of these factors will result in an effective supply chain. This research aims to study the issue of supply chain network design including the localization of facilities, allocation flow among facilities and routing decisions. The issue is to determine the number, location and capacity levels of distribution centers, to allocate customers to distribution centers and distribution centers to suppliers and routing decisions such as determination of the products transport route from distributors to customers and type of transport vehicles so that the total cost of the system to be minimized and customer coverage to be maximized. In addition to reducing costs and increasing quality, improving the environmental performance of the supply chain and decreasing the costs of environmental degradation is also included in the proposed issue.This necessity which is known as a green supply chain is observed by choosing vehicles with lower emissions and reducing transport distances. A. Kazemi et al. J Fundam Appl Sci. 2016, 8(3S), 1340-1365 1341 On the other hand, this research role includes the impact of sharing information through raising and reducing waiting times for carriers. All of the above will be formulated by an integer linear programming model. Given that the mentioned issue is located in the group of problems with hard complexity, this article suggests using multi-objective meta-heuristic algorithms for optimization of the problem and compares the efficiency of the proposed algorithms with each other using several random sample problems.