A genetic algorithm using priority based encoding for product recovery network design

Abstract

This study considers the problem of designing reverse logistics (RL) network for end-of-life products recovery. Product recovery refers obtaining as much of the economic and ecological values as possible from used products by reducing waste generated by disposing. Drivers of product recovery are generally categorized as economic benefits, legislation and social responsibility. To manage product recovery operations efficiently RL network design is a significant problem that plans all RL operations as collection, classification, disassembly, refurbishing, processing, recycling and disposal. In this study a genetic algorithm (GA) is designed for network design problem. The aim of the algorithm is to determine network structure with maximum profit. In GA, priority based encoding is adopted for representing chromosomes. The proposed solution approach is applied to a case from automotive industry: end-of-life vehicles RL network design problem. Obtained results show that, the proposed algorithm is an efficient method for designing RL network and can be adapted to any other industries.