A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling

Abstract

a b s t r a c tDue to the implementation of government legislation, social responsibility, environmental concern, economic benefits and customer awareness the industries are under a great pressure not only to provide environmentally friendly products but also to take back the product after its use. The issue in reverse logistics is to take back the used products, either under warranty or at the end of use or at the end of lease, so that the products or its parts are appropriately disposed, recycled, reused or remanufactured. In order to overcome this issue, it is necessary to setup a logistics network for arising goods flow from end users to manufacturers. In this study, the optimum usage of secondary lead recovered from the spent lead-acid batteries for producing new battery is presented. The disposal in surface or sewage water or land of liquid content of the lead-acid batteries is strictly restricted. Because of the need for environmental protection and the lack of considerable lead resources, the spent batteries treatment and lead recovery are becoming crucial now-a-days. The objective of this paper is to develop a multi echelon, multi period, multi product closed loop supply chain network model for product returns and the decisions are made regarding material procurement, production, distribution, recycling and disposal. The proposed heuristics based genetic algorithm (GA) is applied as a solution methodology to solve mixed integer linear programming model (MILP). Finally the computational results obtained through GA are compared with the solutions obtained by GAMS optimization software. The solution reveals that the proposed methodology performs very well in terms of both quality of solutions obtained and computational time.

An Hybrid Discrete Particle Swarm Optimization Algorithm For Solving A Sustainable Remanufacturing Suply Chain Problem

“…All test examples presented in the next sections are solved on a Pentium PC 2.1 MHz, and 1Gb memory. Since there is no benchmark model available in the literature for the proposed model, we generated test problems from available data set of well known related supply chain problems [38][39][40]. These are data sets corresponding to networks of up to 10x10x15.…”

Modeling risk and uncertainty in designing reverse logistics problem

“…A tiny amount of genetic information is changed by imposing mutation in each child. This process must continue until termination condition is satisfied (Kannan et al, 2010).…”

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

“…It caused GA has been applied in closed-loop supply chain in so many researches and fields. Kannan et al [68] developed a close-loop supply chain for battery recycling and used GA and GAMS as solving approach. They compared results obtained by GAMS and GA and concluded the GA and GAMS results are close together for small size problems and when the problem would be equal as real case GAMS need more computational time.…”