Assessment of Supply Chain Performance in an Assembly Company: Evaluation of Evolutionary Algorithms

In current globalized markets, companies no longer compete with each other. They now compete with the supply chains (SC) to which they belong. SC optimization allows efficient and effective management of resources. In many cases, optimization goals can conflict with one another. Therefore, the purpose of this work was to evaluate SC performance by comparing three optimization algorithms in a case study with multiple objectives. Two objectives are maximizing profit and maximizing the level of customer service. Also, the modeled problem considers multiple products and periods for two security inventory scenarios (maximum and minimum inventory levels). Evolutionary algorithms were compared: NSGA-II, MOPSO, and MOMA. The NSGA-II algorithm obtained the best result. With a minimum inventory level, NSGA-II presented 97.87% service level and the best benefit. Results show the importance of SC management and its optimization as well as some relevant variables to be considered.