Inventory control problem using Metaheuristic Techniques: Application to Two Warehouse Inventory Model

Abstract

Inventory management necessitates striking a balance between the benefits of having stockpiles of items, which allow you to meet client requests, and the expenses of carrying inventory. Methods for managing inventories are essential, and by using these methods, most businesses may greatly cut their expenditures related to the flow of goods. In today's marketplaces, firms must efficiently manage their supply chains to preserve market contributions and increase profits. Inventory control in the most effective manner is a critical component of supply chain control. When dealing with inventory control concerns, strategic decisions must be taken, such as the ordering times and the product order quantities. These choices can either reduce overall costs or increase total earnings. Because of the considerable costs correlated with preserving inventory and the fact that it degrades over time, it is generally not economically practical for a company to keep a large inventory on its business premises for an extended time. On the other hand, keeping a limited inventory is not profitable because it increases the risk of running out of stock at the point of sale during periods of high demand for the commodities being sold. According to the outcomes, excellent inventory management is necessary to ensure that the firm is conducted in the most cost-effective manner possible. This study's author used the Multi-objective Particle Swarm Optimization (MOPSO) and the Firefly algorithms to adhere to the EOQ inventory model. As part of his evaluation of inventory costs, the author of this paper performed a comparison analysis. As a result, the author determined that the APSO algorithm outperformed the Firefly approach in performance.