Stochastic Kriging: Modeling and Controlling Uncertainty in Simulation

PI：PI_NAME Staum，Jeremy 机构：西北大学标题：随机克里格法：模拟中的建模和控制不确定性“随机克里格：模拟中的建模和控制不确定性“研究旨在通过增加算法来改进依赖于随机系统的模拟来支持决策的算法。计算效率，并能够更可靠的统计推断的质量建议的决定。 两种算法被认为是。 一类是探索和优化随机系统的性能作为其设计的函数。 另一种是用于评估由于系统参数的不确定性而产生的风险，例如为客户提供服务所需的平均时间，或者涉及资产价格或贷款违约等金融情景。 主要的研究目标是通过设计有效的运行模拟计划来改进算法，这些计划可以支持对给定的各种设计中的任何一种的系统性能的准确预测。改进算法以探索和优化随机系统的性能作为其设计的函数，可以提供决策支持。制造商立即获得他们希望探索的系统设计性能的准确估计。 这将使模拟能够更好地支持有关制造和服务系统投资的决策。 更好的风险评估算法将鼓励更好的决策和风险管理实践。 目前，由于系统参数或金融情景的不确定性而产生的风险，通常用比模拟更不准确或更不具启发性的方法来处理，因为模拟被认为太笨拙或太慢，无法处理这些风险评估问题。

PI: PI_NAME Staum, Jeremy INSTITUTION: Northwestern UniversityTITLE: Stochastic Kriging: Modeling and Controlling Uncertainty in Simulation "Stochastic Kriging: Modeling and Controlling Uncertainty in Simulation"The research aims to improve algorithms that rely on simulation of stochastic systems to support decision-making by increasing the algorithms? computational efficiency and enabling more reliable statistical inference about the quality of proposed decisions. Two kinds of algorithms are considered. One kind is for the exploration and optimization of the performance of a stochastic system as a function of its design. The other kind is for assessing risks due to uncertainty about system parameters, e.g. the average time required to service a customer, or about financial scenarios, involving e.g. asset prices or loan defaults. The primary research objective is to improve the algorithms by devising efficient plans for running simulations that can support accurate predictions of system performance given any of a wide range of designs.Improvements to algorithms for the exploration and optimization of the performance of a stochastic system as a function of its design could make it possible to provide decision-makers instantly with accurate estimates of the performance of system designs they wish to explore. This would enable simulation to better support decisions about investment in manufacturing and service systems. Better algorithms for risk assessment would encourage better decision-making and risk management practices. At present, risks due to uncertainty about system parameters or about financial scenarios are often treated with methods less accurate or less illuminating than simulation, because simulation is thought to be too unwieldy or slow to handle those risk assessment problems.