强化学习
计算机科学
库存控制
存货理论
盈利能力指数
供应链
库存管理
启发式
利润(经济学)
运筹学
循环计数
供应链管理
存货周转率
人工智能
运营管理
业务
营销
数学
经济
微观经济学
财务
作者
Tsukasa Demizu,Yusuke Fukazawa,Hiroshi Morita
标识
DOI:10.1016/j.eswa.2023.120256
摘要
This study addresses the optimal inventory management problem for new smartphone products as an effective example of a supply chain with a short product life cycle. The determination of the optimal inventory level leads to a reduction of lost opportunities and defective inventory, which is an important issue from a profit improvement perspective. Mathematical optimization and reinforcement learning approaches have been proposed for inventory management; however, most of these approaches focus on products that are regularly sold over a long period. Thus, when the target is a new product, it is difficult to optimize inventory control from its day of release due to a lack of sufficient data for learning. To solve this problem, we focus on model-based deep reinforcement learning with high sample efficiency and propose an inventory management method for new products that combines model learning in an offline environment and planning in an online environment. Simulations using real-world historical sales datasets demonstrate that the proposed method outperforms existing methods in terms of profitability, efficiency, and customer satisfaction. In particular, the proposed method improves total rewards and inventory turnover by ¿5% each than the heuristic method while maintaining the same stock-out rate. In addition, the results demonstrate that the proposed method can maintain stable inventory control for multiproduct and multistore supply chains.
科研通智能强力驱动
Strongly Powered by AbleSci AI