Supply Chain Optimization under Risk and Uncertainty using Nondominated Sorting Genetic Algorithm II for Automobile Industry

Abstract

In recent decades, the use of supply chain management is essential for developing new technologies and setting the ground in expanding major global markets to integrate suppliers, manufacturers, warehouses, and stores effectively. In addition, the growing competition in the modern business environment has created an increasing trend of new products and improved product quality for attracting more consumers. However, an increase in the related costs and uncertainty in these innovations requires the development of algorithms to solve optimization problems. Therefore, this study is aimed to implement a multi-product supply chain including raw material suppliers, factories, distributors, and customers to maximize the quality and minimize costs. To this aim, supplier quality, distribution centers, and manufacturing products were considered for the quality model, while warehousing costs, product production, transportation, defective raw materials, and the like were regarded for the cost model. Then, the NSGAII algorithm was used for solving the created optimization problem, and accordingly, the optimal Pareto points were calculated. Based on the results, the proposed model can give the manufacturer the ability to decide on a multi-product and multi-time supply chain by involving cost and quality variables. Thus, the owner can manage the supply chain of the factory effectively.