Learning Algorithms for Dynamic Inventory and Pricing Optimization Problems

动态库存和定价优化问题的学习算法

• 批准号: 1634676
• 负责人: Xiuli Chao
• 金额: $ 30万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634676&HistoricalAwards=false
• 关键词: Learning; Algorithms; Dynamic; Inventory; Pricing

Learning algorithms aim to solve dynamic optimization problems in which the decision maker has limited or no prior information about either a part of or the entire system structure. Indeed, in many applications, the system is so complex that it may not be possible to lay out an exact theoretical model with all system parameters known in advance. In these settings, the decision maker needs to learn such information during the decision making process, e.g., by extracting information from the collected data, to design algorithms for improved system performance. This view of optimization as a dynamic learning process has become prominent in recent years and has led to some promising results. This research will develop efficient data-driven learning algorithms for dynamic operations optimization problems in supply chain management. It will be accomplished by incorporating and extending ideas and techniques from machine learning and stochastic optimization, and the effectiveness of the algorithms will be measured by regret, defined as its average loss (increment) in profit (cost) per unit time compared with a clairvoyant who has complete information about the underlying system structure. This project involves several disciplines such as manufacturing, computing, operations research, and business analytics, and the multidisciplinary approach will encourage participation from under-represented groups and positively impact graduate and undergraduate education. Efficient data-driven algorithms will be developed for several classes of dynamic operations optimization problems, including multi-product dynamic inventory control with stockout substitutions, multi-product pricing and inventory control under customer choice models, inventory and pricing optimization under changing and seasonal environments, dynamic optimization in competitive environments, dynamic inventory control and pricing with reference point effect, and dynamic joint operations and marketing decision making. The research integrates cutting-edge knowledge and ideas from various areas, such as statistics, game theory, machine learning, operations research, and behavioral sciences, and it will lead to efficient learning algorithms that perform well both theoretically and empirically. With the increasing availability of data in companies, the research from this project will help them better utilize data for intelligent pricing and inventory decisions, and increase revenue and minimize cost.

学习算法旨在解决动态优化问题，其中决策者对系统结构的一部分或整个系统结构的先验信息有限或没有先验信息。事实上，在许多应用中，系统是如此复杂，以至于可能无法在所有系统参数都预先已知的情况下制定精确的理论模型。在这些情况下，决策者需要在决策过程中了解这些信息，例如，通过从收集的数据中提取信息，设计算法以提高系统性能。这种将优化视为动态学习过程的观点近年来变得突出，并取得了一些有希望的结果。本研究将开发有效的数据驱动学习算法，用于供应链管理中的动态操作优化问题。它将通过整合和扩展机器学习和随机优化的思想和技术来实现，算法的有效性将通过后悔来衡量，后悔定义为与拥有完整信息的千里眼相比，单位时间内利润（成本）的平均损失（增量）。该项目涉及制造，计算，运筹学和商业分析等多个学科，多学科方法将鼓励代表性不足的群体参与，并对研究生和本科生教育产生积极影响。将为几类动态运营优化问题开发有效的数据驱动算法，包括缺货替代的多产品动态库存控制，客户选择模型下的多产品定价和库存控制，变化和季节环境下的库存和定价优化，竞争环境下的动态优化，参考点效应下的动态库存控制和定价，动态联合经营和营销决策。该研究整合了来自各个领域的前沿知识和想法，如统计学，博弈论，机器学习，运筹学和行为科学，它将导致有效的学习算法，在理论和经验上都表现良好。随着公司数据可用性的增加，该项目的研究将帮助他们更好地利用数据进行智能定价和库存决策，并增加收入和最大限度地降低成本。