题 目：Managing with Incomplete Inventory Information
主讲人：Sethi Suresh Pal. (加拿大皇家科学院院士、杰出教授、博士)
时 间：2013年8月26日(星期一)15点
地 点：主楼418会议室
主讲人简介：
    Suresh P. Sethi任教于德克萨斯大学达拉斯分校，现为加拿大皇家科学院院士，Charles & Nancy Davidson运作管理的杰出教授，智能供应网络中心主任(the Center for Intelligent Supply Networks)。Sethi教授在运作管理、营销、工业工程、最优控制等领域做出了杰出贡献，最为人熟知是Sethi advertising model、DNSS Points以及有关最优控制的教科书，如Optimal Control Theory: Applications to Management Science and Economics。Sethi教授在国际主流期刊上发表学术论文264篇，其中Management Science 11篇, Operations Research 17篇, Production and Operations Management 15篇, Manufacturing & Service Operations Management 5篇。Sethi教授现为Production and Operations Management协会主席，同时担任Production and Operations Management期刊（UT Dallas 24种顶级期刊之一）的Department Editor，SIAM Journal on Control and Optimization的责任编辑，Automatica的副主编。现担任Management Science、Operations Research(OR)、IIE Transactions(IIE) 、Manufacturing & Service Operations Management等多种权威期刊的审稿专家。
内容简介：
    A critical assumption in the vast literature on inventory management has been that the current level of inventory is known to the   decision maker. Some of the most celebrated results such as optimality of base-stock have been obtained under this assumption. Yet it is often the case in practice that the decision makers have incomplete or partial information about their inventory levels. The reasons for this are many: Inventory records or cash register information differ from actual inventory because of a variety of factors including transaction errors, theft, spoilage, misplacement, unobserved lost demands, and information delays. As a result, what are usually observed are some events or surrogate measures, called signals, related to the inventory level. At best, these relationships may provide only the distribution of current inventory levels. In the best case, therefore, the relevant state in the inventory control problems is not the current inventory level, but rather its distribution given the observed signals. Thus, the analysis for finding optimal production or ordering policies takes place generally in the space of probability distributions. The purpose of this talk is to review recent developments in the analysis of inventory management problems with incomplete information.