Tractable Markdown Optimization for an E-tailer

电子零售商的易处理降价优化

• 批准号: 1162034
• 负责人: Georgia Perakis
• 金额: $ 23.49万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1162034&HistoricalAwards=false
• 关键词: Tractable; Markdown; Optimization; tailer

The research goal of this award is to design operational models to provide decision support for markdown pricing by e-tailers. The models will include strategic customer behavior in the presence of business rules, and will have the potential to be applied operationally. Existing models for MDO with strategic customers make strong assumptions about customer behavior that are difficult to estimate or validate with the data that can practically be collected. Our research focuses on pricing models that can be estimated with customer visit data. Such data is already being collected by e-tailers through user logins and cookies. Such information would not be practical to collect for brick-and-mortar stores, the traditional context for the MDO problem. The research plan is to first try to understand the impact of limited strategic (i.e. returning but myopic) customers on the optimal prices for an e-tailer in a single-item setting. Next, we will build upon the foundation such MDO models by additionally considering business rules - these are practically important hard constraints that retailers impose on the sequence of prices. Finally, we will generalize our models to the case of multiple items. We will analyze these models first from a theoretical standpoint but also will exploit the relationships between them and test them in practice using real data. This research will employ methodologies from a variety of fields with the long term goal to deepen our understanding on issues in dynamic pricing as they relate to the retail industry and beyond. We will develop an integrated framework, models and methods for the application of stochastic and robust optimization to key pricing problems.If successful this research will fill a gap between theory and practice in the existing research that will transform the pricing processes of e-tailers. It will empower them to benefit from a better understanding of strategic customer behavior. This is vital because e-commerce an increasing segment of retail business. Further, we believe that the applications of this research go beyond the field of pricing. We will share our integrated framework, models and methods, to help both academics and practitioners. From an educational perspective, the results of this project will serve as components in teaching modules at MIT. These include modules in core courses for which the PI already has shared responsibility. This project lends itself ideally to mentoring undergraduate and graduate students in research on tractable practice-based optimization.

本奖项的研究目标是设计运营模型，为电子零售商的降价定价提供决策支持。这些模型将包括存在业务规则的战略客户行为，并将具有可操作性应用的潜力。现有的具有战略客户的MDO模型对客户行为做出了强有力的假设，这些假设很难用实际收集到的数据来估计或验证。我们的研究重点是可以用客户访问数据估计的定价模型。这些数据已经被电子零售商通过用户登录和cookies收集起来。对于实体店（MDO问题的传统背景）来说，收集这样的信息是不切实际的。研究计划是首先试图了解有限的战略（即返回但短视）客户对单一项目设置下电子零售商最优价格的影响。接下来，我们将在MDO模型的基础上构建额外考虑业务规则的MDO模型——这些是零售商对价格序列施加的实际重要的硬约束。最后，我们将把我们的模型推广到多个项目的情况。我们将首先从理论角度分析这些模型，但也将利用它们之间的关系，并在实践中使用真实数据进行测试。这项研究将采用来自不同领域的方法，其长期目标是加深我们对动态定价问题的理解，因为它们与零售业及其他行业有关。我们将开发一个集成的框架、模型和方法，将随机和鲁棒优化应用于关键的定价问题。如果成功，本研究将填补现有研究中理论与实践之间的空白，将改变电子零售商的定价过程。这将使他们能够从更好地理解战略性客户行为中受益。这一点至关重要，因为电子商务在零售业务中所占的比重越来越大。此外，我们认为这项研究的应用范围超出了定价领域。我们将分享我们的综合框架、模型和方法，以帮助学术界和实践者。从教育的角度来看，这个项目的结果将作为麻省理工学院教学模块的组成部分。这些包括核心课程中的模块，PI已经分担了责任。这个项目非常适合指导本科生和研究生进行可处理的基于实践的优化研究。