New hybrid algorithm for supply chain optimization

Abstract

Optimization is the process of obtaining the best solutions to specific problems. In the literature, those problems have been optimized through a plethora of algorithms. However, these algorithms have many advantages but also disadvantages. In this article, a New Hybrid Algorithm for Supply Chain Optimization, NHA-SCO has been proposed in order to improve the benefits of objective function convergence. For the analysis of the results, three assembly companies have been utilized as case studies. These companies present their supply chains, i.e., networks where products flow from their raw material to the final products delivered to clients. These supply chains must satisfy different objectives, such as maximize benefits and service level and minimize scrap. For the evaluation of results, NHA-SCO has been compared to other well-known optimization algorithms. In the presented case studies, the NHA-SCO algorithm performs faster, or it converges in fewer iterations, obtaining similar or even better results than the other algorithms tested.