Dynamic Decision Making with Applications in Healthcare and Airline Revenue Management

医疗保健和航空公司收益管理中应用的动态决策

• 批准号: 2740612
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2740612
• 关键词: Dynamic; Decision; Making; Applications; Healthcare

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.

我博士学位的目标论文是为了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1970年代航空市场的自由化以来，航空公司的目标是设计更好的收入管理系统。航空公司收入管理系统计算给定客户收费的最佳票价。传统的定价方法使用随机收入管理系统来优化其收入。他们在不考虑竞争和容量限制的情况下对需求进行参数预测模型。借助来自航空合作伙伴的数据，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得出有效的初始政策来定价。此外，现有数据用于创建模拟器以提高派生的初始策略的性能。我们计划通过目前在实践中采用的方法来基准针对最先进方法的拟议算法。随后，我们希望运行一项试点计划，以在现实世界中验证这些方法的性能。拟议的算法利用搜索和竞争数据，这将是收入管理文献的新颖补充。在线部署算法以促进利润也将是文献的新颖补充。根据EPSRC的策略，该项目将属于运营研究主题。我们与Sky Airlines合作，以在线方式收集数据并部署算法。对于医疗保健中的AI工具，医生必须信任算法规定的治疗方法。传统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，要么不考虑歧义。在这里，我们旨在开发可解释的动态治疗方案，以在存在歧义时顺序决定对患者开处方的药物。该项目的目标是找到一个解释性的概念，开发可解释的算法，并检查现实世界中离线数据的性能。我们还希望从理论上计算可解释性的成本，并在零时找到条件，以在医疗保健环境中正确的药物处方。这将属于EPSRC指南的医疗保健和运营研究主题。我们正在哈佛大学肯尼迪学校与Sourush Saghafian合作参加该项目。