Inventory classification by multiple objective particles swarm optimization

Inventory classification is one of important techniques in inventory control context. Managers have to classify inventories because of their variety and high volume. So a stream of research has been to attempt to find methods that increase the management control by determining the number of inventory classes. In this paper the multiple objective particle swarm optimization algorithm has been used. This algorithm has been presented by Chi-Yang Tsai and Szu-Wei Yeh in 2008. Multiple objective particle swarm optimization algorithm is an evolutionary algorithm that enables the management to optimize multiple objectives simultaneously. Minimizing costs of inventory holding and ordering and maximizing inventory turnover ratios are this model’s objectives. We write the software program of this model and then test it on a sample of 100 items. Results show that this algorithm can decrease costs of holding & ordering and also increase the inventory turnover ratios significantly.