US-Turkey Cooperative Research: International: Bayesian Inventory Management under Censored Demand

0114738 LevineDescription：该奖项是为了表彰一项合作研究项目，该项目涉及土耳其安卡拉比尔肯特大学加州大学戴维斯分校统计系Richard Levine博士、管理系Emre Berk博士和工业工程系Ulku Gurler博士。这些科学家将研究贝叶斯更新方法的发展，以得出统计推断，并在定期审查库存问题中得出最优解。我们的目标是开发现实的贝叶斯模型，用于库存管理应用中的决策：1)库存需求不可观察或部分可观察(销售损失)；2)客户对即时库存和延迟库存的态度不同；3)需求过程随着时间的推移而演变。在库存管理方面，目标是利用先进的统计方法对需求过程中的不确定性进行建模，并结合贝叶斯方法，在操作过程中随着数据的积累动态更新现有信息。范围：该奖项将允许加州大学戴维斯分校的一名助理教授与两名土耳其科学家合作，其中一名科学家也是最近获得博士学位的。该项目可能会增强长期富有成效的合作的潜力。这个话题具有科学价值。其目的是弥合纯粹的工业工程/运筹学方法和普通统计方法之间的差距，前者侧重于给定系统的优化，后者提供从给定数据集估计系统参数的一次性解决方案。这种协作为专家带来了处理这一复杂问题所需的补充专业知识。该项目吸引了美国和土耳其这些领域的活跃研究人员。该项目符合支持互利合作研究的INT标准。

0114738LevineDescription: This award is for support of a collaborative research project involving Dr. Richard Levine, Department of Statistics, University of California at Davis, Davis, California, Dr. Emre Berk, Department of Management, and Dr. Ulku Gurler, Department of Industrial Engineering, both at Bilkent University, Ankara, Turkey. These scientists will study the development of Bayesian updating methods to draw statistical inferences and derive optimal solutions in periodic review inventory problems. The goal is to develop realistic Bayesian models for decision-making in inventory management applications when1) inventory demands are unobservable or partially observed (lost sales); 2) customers differ in their attitudes toward immediate and delayed inventories; 3) demand processes evolve over time. In the context of inventory management, the goal is to be achieved by utilizing advanced statistical methods for modeling the uncertainty in demand processes and incorporating Bayesian methods to update available information dynamically during the course of operation as data accumulates. Scope: This award will allow an Assistant Professor at UC Davis to collaborate with two Turkish scientists, one of whom is also a recent Ph.D. This project is likely to enhance the potential for long-term productive collaboration. The topic is of scientific interest. The purpose is to bridge the gap between the purely industrial engineering/operations research approach that focuses on optimization of a given system and the common statistical approach that provides a one time solution for the estimation of system parameters from a given data set. The collaboration brings experts with needed complementary expertise to deal with this complex problem. The project brings active researchers in these areas in the United States and Turkey. The project meets the INT criteria for support of collaborative research of mutual benefit.