Novel mathematical models for optimal screening and multicriteria scheduling problems

用于优化筛选和多标准调度问题的新颖数学模型

• 批准号: 418663-2012
• 负责人: Erenay, Fatih
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=551539
• 关键词: Novel; mathematical; models; optimal; screening

The proposed research consists of two related lines of research. Our first research stream explores the problem of determining the optimal screening schedule of a system that stochastically deteriorates towards failure. Screenings provide imperfect information about the current deterioration level of the system, based on which optimal preventative actions (i.e., replacement, repair) are determined. This problem is of great importance for various practical applications including disease screening. In particular, the modeling framework developed for this problem can be used in optimizing preventative cancer screening, which may have a significant impact on the society. In this context, we explore novel modeling frameworks for the optimal screening problem based on Markov decision processes and control theory. We will analyze these complex models to prove the existence of certain structural properties. We will use such properties to develop faster solution algorithms to solve this problem. In addition, we develop a solution method for multi-objective optimal screening problems which determine the Pareto-optimal solutions dominating the other feasible solutions. The proposed research will also address the optimal screening problem for a population, in order to determine how limited screening resources should be allocated to the individuals within the population.

拟议的研究包括两个相关的研究方向。我们的第一个研究流探讨的问题是确定一个随机退化到失效的系统的最佳筛选计划。筛选提供了关于系统当前恶化程度的不完全信息，在此基础上确定了最佳预防措施（即更换、维修）。这一问题对于包括疾病筛查在内的各种实际应用具有重要意义。特别是针对该问题开发的建模框架可以用于优化预防性癌症筛查，这可能会对社会产生重大影响。在此背景下，我们探索了基于马尔可夫决策过程和控制理论的最优筛选问题的新建模框架。我们将分析这些复杂的模型来证明某些结构性质的存在。我们将使用这些属性来开发更快的求解算法来解决这个问题。此外，我们还开发了一种多目标最优筛选问题的求解方法，该方法确定帕累托最优解优于其他可行解。拟议的研究还将解决人群的最佳筛查问题，以确定如何将有限的筛查资源分配给人群中的个体。