2022/04/28 by Soh Kumabe, Shinya Shiroshita, Kumabe, Soh +5
Business, Management and Accounting · Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Blockchain Technology Applications and Security #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scheduling and Optimization Algorithms #Supply Chain and Inventory Management
paper · pdf · doi:10.48550/arxiv.2204.13378
openalex publication_date 2022/04/28 · openalex created_date 2022/05/01 · openalex updated_date 2026/07/28
Inventory management in warehouses directly affects profits made by manufacturers. Particularly, large manufacturers produce a very large variety of products that are handled by a significantly large number of retailers. In such a case, the computational complexity of classical inventory management algorithms is inordinately large. In recent years, learning-based approaches have become popular for addressing such problems. However, previous studies have not been managed systems where both the number of products and retailers are large. This study proposes a reinforcement learning-based warehouse inventory management algorithm that can be used for supply chain systems where both the number of products and retailers are large. To solve the computational problem of handling large systems, we provide a means of approximate simulation of the system in the training phase. Our experiments on both real and artificial data demonstrate that our algorithm with approximated simulation can successfully handle large supply chain networks.