Multi-criteria inventory classification problem: An effective artificial immune algorithm

In this paper, we study the problem of multi-criteria ABC inventory classification using an efficient artificial immune algorithm (AIA) to partially alter the traditionally incomprehensive attitude of single objective consideration of inventory control problems. Therefore, we simultaneously endeavor to investigate two different subjects. First, we incorporate various criteria such as annual dollar usage, lead time, criticality, commonality, obsolescence and substitutability into the problem of ABC inventory classification. This method is regarded in lieu of mere consideration of the annual dollar usage criteria in the traditional ABC inventory classification. Second, the proposed AIA delays the algorithm convergence due to its restraining mechanism; meanwhile, it alleviates the problem of premature convergence of existing genetic algorithm to end up with more precise ABC inventory classification. Finally, we draw an analogy between the results obtained from both algorithms applied to a real case study present in the literature. The superiority and effectiveness of our AIA is inferred from all the results obtained in various situations.