A Novel Approach for Inventory Classification in Two-Stage Supply Chain System

This research applied Selective Regeneration Particle Swarm Optimization (SRPSO) for two-stage supply chain inventory classification problems. In SRPSO, In order to increase the efficiency, suggestion on parameter settings is made and a mechanism is designed to prevent particles fall into the local optimal. This research is assumed that an inventory classification is performed to properly categorize items into a number of groups. Items in the same group are then jointly replenished. That is, items in the same group have the same order interval at the warehouse and the retailer. The goal of this study is to classify a known set of items into a number of groups and determine the optimal order interval of each classification group to minimize the total relevant cost of the supply chain system. This study tests a real dataset and two article datasets to compare the results to other known classification and non-classification methods. The outcomes fully demonstrate that SRPSO is an efficient, accurate, and robust method for inventory classification in supply chain problems. The SRPSO performs comparatively better than other grouping and non-grouping techniques. Keywords-Particle Swarm Optimization; Selective Regeneration Particle; Inventory Classification