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

点击翻译按钮获取中文摘要

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.