DDDAS-SMRP: Robustness and Performance in Data-Driven Revenue Management

DDDAS-SMRP：数据驱动收入管理的稳健性和性能

• 批准号: 0540143
• 负责人: Aurelie Thiele
• 金额: --
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540143&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Robustness; Performance; Data

This grant provides funding for the development and analysis of data-driven models of uncertainty in revenue management, which will incorporate the decision-maker's risk preferences and dynamically integrate experimental measurements into the computational approach. A first set of models will focus on quantity-based revenue management, where the demand distribution is independent of the decision-maker's actions. A second set of models will apply the data-driven paradigm to price-based revenue management, where the demand distribution, and hence the data, is affected by the decision variables. Specifically, this research project will investigate the following three questions: (i) How should additional data be incorporated into the algorithm to yield robust solutions, especially when part of the historical data might now be obsolete? (ii) How can the optimization module steer the data collection process, not only in terms of measurement frequency, but also with respect to the quantities monitored? (iii) How can the wealth of available data be manipulated in an efficient manner, so that the added clarity about the state of the system does not come at the expense of computational tractability? The analysis will involve techniques from the fields of financial risk management, control theory, robust regression and mathematical programming, as well as large-scale simulations to assess the robustness and performance of the approach. If successful, this research will provide a comprehensive mathematical model of revenue management under uncertainty that will be more tightly connected to the available data without overly depending on any one assumption about demand. It will allow practitioners to take into account randomness in a more intuitive manner, and hence will promote the use of state-of-the-art optimization models and computational techniques in industry. Moreover, the models and algorithms developed in this work will offer a new approach to decision-making under uncertainty of interest to the whole operations research community and with applications to a wide array of domains.

这笔赠款为开发和分析收入管理中不确定性的数据驱动模型提供资金，该模型将纳入决策者的风险偏好，并将实验测量动态地纳入计算方法。第一组模型将侧重于基于数量的收入管理，其中需求分布独立于决策者的行动。第二组模型将数据驱动的模式应用于基于价格的收入管理，其中需求分布以及数据受到决策变量的影响。具体地说，该研究项目将调查以下三个问题：(I)如何将额外的数据合并到算法中以产生健壮的解决方案，特别是在部分历史数据现在可能过时的情况下？(2)优化模块如何不仅在测量频率方面，而且在监测数量方面指导数据收集进程？(3)如何才能以有效的方式处理丰富的现有数据，从而不以牺牲计算处理能力为代价增加对系统状态的清晰度？分析将涉及金融风险管理、控制理论、稳健回归和数学规划等领域的技术，以及评估该方法的稳健性和性能的大规模模拟。如果成功，这项研究将为不确定性下的收入管理提供一个全面的数学模型，该模型将与现有数据更紧密地联系在一起，而不会过度依赖关于需求的任何一个假设。它将允许从业者以更直观的方式考虑随机性，因此将促进最先进的优化模型和计算技术在工业中的使用。此外，这项工作中开发的模型和算法将为整个运筹界感兴趣的不确定情况下的决策提供一种新的方法，并将应用于广泛的领域。