Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation

There are many reasons for the growing interest in reverse logistics. The most prominent reasons are the growing concern for the environment and cost reduction. Next to environment, consumers demand for clean manufacturing and recycling. Hence, customers and retailers expect original equipment manufacturers to set up a proper reverse logistics system and expect the returned products to be processed and recovered in an environmentally responsible way and another reason is cost reduction. A well-managed reverse logistics programme can provide important cost savings in procurement, disposal, inventory carrying and transportation. In this context, looking at the entire supply chain is the best starting point for solutions. Supply chain management aims at the integration of traditional 'forward' supply chain processes, avoiding local optimisation by emphasising integrality. The main objective of this paper is to design an integrated forward logistics multi-echelon distribution inventory supply chain model (FLMEDIM) and closed loop multi-echelon distribution inventory supply chain model (CLMEDIM) for the built-to-order environment using genetic algorithm and particle swarm optimisation. In this paper, the proposed model is validated by considering two case studies: one for a tyre manufacturer and the other for a plastic goods manufacturer both located in the southern part of India. This paper utilises the multi-echelon distribution inventory supply chain model proposed by Haq and Kannan (2006a) for the FLMEDIM. The software used was written in the Java programming language.