Computationally and statistically efficient model fitting techniques

A significant challenge in fitting metamodels of large-scale simulations with sufficient accuracy is in the computational time required for rigorous statistical validation. This paper addresses the statistical computation issues associated with the Bootstrap and modified PRESS statistic, which yield key metrics for error measurements in metamodelling validation. Experimentation is performed on different programming languages, namely, MATLAB, R, and Python, and implemented on different computing architectures including traditional multicore personal computers and high-power clusters with parallel computing capabilities. This study yields insight into the effect that programming languages and computing architecture have on the computational time for simulation metamodel validation. The experimentation is performed across two scenarios with varying complexity.     

分数年龄假设的新方法：Kriging模型

在生存函数的计算中,生命表只提供了其在整数年龄上的值。当计算非整数年龄上的生存函数时就需要进行分数年龄假设。经典的分数年龄假设在数学上容易处理,但却容易导致死力函数不连续,更重要的是无法保证其在分数年龄上估计的精确性。分数年龄假设本质上是一种插值技术。本研究尝试将一种插值性能优越的Kriging模型引入到分数年龄假设中,对整数年龄上的生存函数进行插值,并基于良好拟合的生存函数进一步构建死力函数及平均余命函数。基于Kriging模型的分数年龄假设的有效性通过了Makeham法则下的生存函数的验证,其结果表明,Kriging模型的插值性能远胜过经典的分数年龄假设模型。     

An Adaptive Exploration-Exploitation Algorithm for Constructing Metamodels in Random Simulation Using a Novel Sequential Experimental Design

In this article, we propose a novel algorithm for sequential design of metamodels in random simulation, which combines the exploration capability of most one-shot space-filling designs with the exploitation feature of common sequential designs. The algorithm continuously maintains a balance between the exploration and the exploitation search throughout the search process in a sequential and adaptive manner. The numerical results indicate that the proposed approach is superior to one of the existing well-known sequential designs in terms of both the computational efficiency and speed in generating efficient experimental designs.     

A Simulation Metamodel for Response-Time Planning*

Field service management continues to be a major challenge for many service organizations as companies are required to provide more service with less resources. Especially in information intense environments, short response times for service calls are essential to avoid disruptions to a business office or production facility. Managers must regularly assess their manpower needs, and ensure that their allocation and operational decisions lead to the best service at the lowest cost. Xerox Corporation provides copiers, duplicators, and printing systems to an international market. Xerox operates a service network consisting of over 30,000 trained service personnel, each allocated to service regions by the service planning staff. Response-time planning involves many challenging problems: requirements planning at the national level, allocation planning at the district level, and operational planning at the team level. Customer service is critical and is commonly measured by response time—the time that elapses from when a service call is placed to when a service engineer begins service. This paper discusses how Xerox Corporation has used simulation models and metamodels to improve response-time planning and field service operations.