Research Initiation: Unconstrained Optimization in DiscreteEvent Simulation

研究启动：离散事件仿真中的无约束优化

• 批准号: 9210679
• 负责人: Sigrun Andradottir
• 金额: $ 10万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-15 至 1996-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210679&HistoricalAwards=false
• 关键词: Research; Initiation; Unconstrained; Optimization; DiscreteEvent

In many real world systems, they are of such complexity that the functional relationship between the parameters of the system and its performance is not well understood. Simulation is often the only tool that can be used to evaluate the performance of the system. Standard mathematical programming techniques cannot be used to optimize the performance of the system. This project will conduct research aimed at developing efficient methods for optimizing the performance of stochastic system with respect to continuous decision variables. The project is concerned with three issues: 1) under what conditions do several new stochastic approximation algorithms converge, 2) when are these conditions satisfied in simulation, and 3) how do stochastic approximation algorithms compare empirically?

在许多现实世界系统中，它们具有如此复杂，以至于系统的参数及其性能之间的功能关系尚不清楚。 仿真通常是唯一可用于评估系统性能的工具。 标准数学编程技术不能用于优化系统的性能。 该项目将进行旨在开发有效方法的研究，以优化随机系统在连续决策变量方面的性能。 该项目与三个问题有关：1）在什么条件下，几种新的随机近似算法收敛，2）何时在模拟中满足这些条件，而3）随机近似算法如何以经验比较？