Optimization of multi-echelon reverse supply chain network using genetic algorithm

Abstract

Remanufacturing products has been more popular in recent years among businesses as a result of environmental concerns, and the close loop supply chain (CLSC) network has been used to optimise the reverse logistic system. The objective of the current study is to identify the ideal CLSC network, which consists of several producers, remanufacturers, intermediary centres, and customer centres. The consideration of a multi-product, multi-echelon, closed loop supply chain network (CLSC) model for returns products, in which choices about the procurement of materials and their production, distribution, recycling, and disposal play a significant part, is necessary to meet the work’s objectives. In order to resolve the issue more cheaply, a mixed-integer linear programming (MILP) approach is used. Genetic algorithm (GA) is applied as a solution approach in this.