Dynamic Decision Making with Applications in Healthcare and Airline Revenue Management

我博士论文的目标是开发适用于航空公司收入管理和医疗保健的数据驱动的顺序决策算法。自20世纪70年代航空市场自由化以来，航空公司一直致力于设计更好的收入管理系统。航空公司收入管理系统计算为给定客户收取的最优票价。传统的定价方法使用随机收入管理系统来优化收入。他们假设了一个不考虑竞争和产能约束的需求参数预测模型。在航空合作伙伴数据的帮助下，我们可以访问预订、搜索和竞争定价数据。我们希望使用可用的数据来制定有效的初始机票定价政策。此外，现有数据被用来创建模拟器以改进所导出的初始策略的性能。我们计划将拟议的算法与目前实践中使用的方法中最先进的方法进行基准比较。随后，我们希望运行一个试点程序来验证这些方法在现实世界中的性能。所提出的算法利用搜索和竞争数据，这将是收入管理文献中的一个新补充。在线部署算法以推动利润也将是对文献的一种新颖的补充。该项目将属于EPSRC战略下的运筹学主题。我们与天空航空公司合作收集数据并以在线方式部署算法。为了开发医疗保健领域的人工智能工具，医生必须信任算法开出的治疗方案。传统的方法要么使用黑匣子方法进行预测，要么假设过渡的参数模型，要么不考虑模糊性。在这里，我们的目标是开发可解释的动态治疗方案，以便在存在歧义时按顺序决定给患者开什么药。这个项目的目标是找到可解释性的概念，开发可解释性的算法，并检查真实世界离线数据的性能。我们还希望从理论上计算可解释性的成本，并在这一成本为零的情况下找到条件，以便在医疗保健环境中能够正确地开出药物处方。这将属于EPSRC指导方针下的医疗保健和运筹学研究主题。我们正在与哈佛大学肯尼迪学院的Sourush Saghafian合作进行这个项目。

The goal of my Ph.D. thesis is to develop data-driven sequential decision-making algorithms that are applicable in airline revenue management and healthcare. Since the liberalisation of the airline market in the 1970s, airline companies have aimed to design better revenue management systems. An airline revenue management system computes the optimal fare to charge for a given customer. Traditional pricing methodologies used stochastic revenue management systems to optimise their revenue. They assume a parametric forecasting model for the demand without accounting for competition, and capacity constraints. With the help of data from an aviation partner, we have access to bookings, searches, and competition pricing data.We wish to use the available data to derive an effective initial policy to price tickets. Further, the existing data is used to create a simulator to improve the performance of the derived initial policy. We plan to benchmark the proposed algorithms against state-of-the-art methodologies from approaches that are currently employed in practice. Subsequently, we wish to run a pilot program to validate the performance of these methods in a real-world setting.The proposed algorithms utilise the searches and competition data which would be a novel addition to the revenue management literature. Online deployment of the algorithms to drive profit would also be a novel addition to the literature. The project would fall under the operations research theme under EPSRC's strategies. We collaborate with SKY Airlines to collect data and deploy the algorithms in an online fashion.For the development of AI tools in healthcare, doctors must trust the treatment prescribed by an algorithm. Traditional methods either use a black box method for prediction, assume a parametric model for transitions, or don't account for ambiguity. Here we aim to develop interpretable dynamic treatment regimes to sequentially decide the medicine to prescribe to a patient when there is ambiguity present. The goals of this project would be to find a notion of interpretability, develop algorithms that are interpretable, and check the performance of real-world offline data. We wish to also theoretically calculate the cost of interpretability and find conditions when this goes to zero, to enable the right prescription of medication in the healthcare setting. This would belong in the healthcare, and operations research theme under EPSRC guidelines. We are collaborating with Sourush Saghafian from Harvard Kennedy School for this project.