Robust Multi-Criteria Optimization With Application to Radiation Therapy

该奖项的研究目标是在输入数据不确定和可能表现出相关性的情况下，显着扩大多准则优化的适用范围。这包括潜在错误数据的收集和分析，相关多专家多准则问题的稳健性研究，以及现有方法的方法验证和对真实数据的结果探索。许多现实世界的应用程序使决策者能够同时实现多个相互矛盾的目标。最常见的方法是最小化目标的总和，每个目标都根据其重要性给予积极的权重。通常，这些权重由一名或多名专家确定，导致相当大的不确定性。这项研究的结果将促进将专家的偏好纳入决策以及决策的可靠性和重复性。这种方法的普遍性使其与现实世界的问题广泛相关，许多问题的目标是自然相关的。在放射治疗中，这种方法使肿瘤学家的选择能够直接告知方法。人类在临床决策中的偏见将被最小化，使最佳的癌症治疗变得可获得、相关并最终有利于患者，而不受医疗设施或地理区域的影响。从教育的角度来看，工科学生和临床医生之间的直接互动将为下一代跨学科研究人员提供一个独特的培训场所。目前，存在错误的多准则优化的两种主要途径是不产生最终决策的帕累托前沿方法和受限于分布可用性的分布稳健方法。该项目侧重于后者的扩展，并受到放射治疗优化应用的推动，近三分之二的癌症病例使用了该应用程序。这个逆问题通常遵循试错法。研究议程提供了对稳健的多标准决策问题中的基本问题的多个层面的更深层次的见解，以及建立与临床实践直接相关的工具和解决方案。该项目如果成功，可以大幅减少决策时间和结果的模糊性，保证高质量的癌症放射治疗不受人类和机构不确定性的限制。

The research goal of this award is to significantly expand the range of applicability of multi-criteria optimization when input data are uncertain and may exhibit correlations. This includes the collection and analysis of potentially erroneous data, the research of robustness in correlated multi-expert multi-criteria problems, and the validation of the methods on available approaches and probing of results on real data. Many real-world applications put a decision maker in the position to simultaneously achieve multiple contradicting goals. The most common approach is to minimize an aggregate of the objectives, each of which are given a positive weight by its importance. Often, these weights are determined by one or more experts, leading to a sizable level of uncertainty. The results from this research will advance the incorporation of experts' preferences into decision- making and the reliability and reproducibility of decisions. The generality of the approach makes it broadly relevant to real-world problems, many with objectives that are naturally correlated. In radiation therapy, the approach enables oncologists' choices to directly inform the method. Human bias in clinical decisions will be minimized, making optimal cancer treatment accessible, relevant, and ultimately beneficial to patients regardless of medical facility or geographic region. From an educational standpoint, the direct interaction between engineering students and clinicians will offer a unique training ground for the next generation of cross-disciplinary researchers.Currently, the two main paths in multi-criteria optimization in the presence of errors are Pareto frontier approaches that do not produce one final decision and distributionally robust approaches that are limited to the availability of the distribution. This project focuses on an extension of the latter and is motivated by the application in radiation therapy optimization, as used in nearly two thirds of all cancer cases. This inverse problem typically follows a trial-and-error procedure. The research agenda provides deeper insights on a number of levels into fundamental questions in robust multi-criteria decision-making problems, as well as to establish tools and solutions directly relevant to clinical practice. This project, if successful, can lead to a drastic reduction of both decision time and ambiguity of the outcome, warranting high-quality cancer radiation treatments not limited by human and institutional uncertainties.