Research on Adaptive Sampling and Stochastic Scheduling

自适应采样与随机调度研究

• 批准号: 8507671
• 负责人: Michael Katehakis
• 金额: $ 3.5万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1985
• 资助国家: 美国
• 起止时间: 1985-09-01 至 1988-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8507671&HistoricalAwards=false
• 关键词: Research; Adaptive; Sampling; Stochastic; Scheduling

Two dynamic allocation problems are considered in this research. The first is the so-called "Multi-Armed Bandit Problem" (MABP), which has applications to scheduling in job shops and in optimal replacement of parts. It has been shown that the general N dimensional MABP can be reduced to N one-dimensional problems involving a single project, the solution of which is the dynamic allocation index value for the current state of the project. The principal investigator proposes a new approach for calculating the dynamic allocation index value and will investigate how this approach can be applied to several problems in sequential statistics and stochastic scheduling. The second part of this research deals with the relationship between closed (no arrivals) and open (arrivals) stochastic scheduling problems. The aim is to understand the role played by the arrival process on the optimal policies.

在这项研究中，考虑了两个动态分配问题。 的 首先是所谓的“多武装匪徒问题”（MABP）， 应用于车间调度和最优替换 零件. 已经表明，一般的N维MABP可以是 简化为涉及单个项目的N个一维问题， 其解是当前的动态分配索引值 项目的状态。 首席研究员提出了一个新的 动态分配指标值的计算方法 研究如何将这种方法应用于几个问题， 顺序统计和随机调度。 的第二部分 本研究探讨了封闭（无到达） 开放（到达）随机调度问题。 目的是 理解到达过程在最佳 施政纲要而