演讲人：杨超林，上海财经大学信息管理与工程学院副院长，教授

时间：2024年12月4日10:00

地点：同济大厦A楼308教室

讲座摘要:

We address a canonical inventory control problem: multiproduct periodic-review lost-sales inventory system with a warehouse capacity constraint. We study this well-researched problem under the lens of demand learning from censored data. Unlike traditional literature, we do not assume that demand distributions are known a priori. Instead, the decision-maker has access only to observed sales data, while the quantity of lost sales remains unobserved. Existing online learning algorithms bear limitations in providing good-quality solutions to inventory systems offering a large variety of products. We employ and innovate mirror descent with cyclic update techniques to address the challenge of high-dimensionality in product menus. We prove theoretically that our algorithm’s regret bound exhibits a logarithmic dependence on the number of products. This constitutes a significant improvement compared to the square-root regret bound established in existing literature. Based on empirical data, we implemented our methods to assess their practical merit and expose additional managerial insights. Our numerical study confirms that indeed our methodology produces inventory policies superior to existing state-of-the art solutions, especially when managing a large menu of products. Drawing from our numerical observations and theory-informed insights, we provide clear guidelines for practical implementation, along with fine-tuning recommendations.

演讲嘉宾简介:

杨超林，上海财经大学信息管理与工程学院副院长，教授，博士生导师。主要从事供应链管理、库存管理、数据驱动的运营管理等方面的研究。研究成果发表在Management Science、Operations Research、Production and Operations Management、Naval Research Logistics等管理科学领域国际期刊。主持国家自然科学基金优秀青年基金、面上基金、青年基金等科研项目。研究成果曾获POMS-HK国际会议最佳学生论文奖二等奖和Service Systems and Service Management国际会议最佳论文奖。为国内多家零售、物流、制造企业（包括京东、顺丰科技、华为等）提供库存管理与优化咨询服务。指导研究生获得首届阿里云基础设施供应链大赛亚军。