A Soft Computing Framework Using Genetic Algorithms for Two-Warehouse Inventory Systems

This paper presents a novel soft computing framework that applies Genetic Algorithms (GAs) to optimize inventory management in a two-warehouse system. The two-warehouse inventory system is a common problem in supply chain management, where goods are stored in two distinct locations: a central warehouse and a local warehouse. The objective is to minimize the total cost, which includes ordering, holding, and transportation costs while satisfying the demand over time. Genetic Algorithms, a heuristic optimization technique, are employed to find the optimal solution for inventory replenishment policies under uncertain conditions. The proposed framework is tested on several benchmark problems, and results show that it can effectively improve decision-making by providing near-optimal solutions in comparison with traditional methods. This approach offers a practical solution for inventory control in real-world applications, considering both fixed and variable costs in a two-warehouse setting.