A Gravitational Search Algorithm Approach for Optimizing Closed-Loop Logistics Network

Abstract

Since late in the 20th century, various heuristic and metaheuristic optimization methods have been developed to obtain superior results and optimize models more efficiently. Some have been inspired by natural events and swarm behaviors. In this chapter, the authors illustrate empirical applications of the gravitational search algorithm (GSA) as a new optimization algorithm based on the law of gravity and mass interactions to optimize closed-loop logistics network. To achieve these aims, the need for a green supply chain will be discussed, and the related drivers and pressures motivate us to develop a mathematical model to optimize total cost in a closed-loop logistic for gathering automobile alternators at the end of their life cycle. Finally, optimizing total costs in a logistic network is solved using GSA in MATLAB software. To express GSA capabilities, a genetic algorithm (GA), as a common and standard metaheuristic algorithm, is compared. The obtained results confirm GSA’s performance and its ability to solve complicated network problems in closed-loop supply chain and logistics.

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

“…The superior solution and convergence rate cause GSA have been interested by researchers to be supply chain problems. In 2012, Sadrnia et al [56] used GSA as a new solving technique to find optimal solutions in recycling automotive alternators close-loop logistics in end-of-life. They assumed all material and product flows between facilities in closed-loop supply chain such as suppliers, manufacturer, distributors, retailers, collection centers, and recycling centers as object with some respective masses.…”