Results 1  10
of
11
Bayesian Analysis For Simulation Input And Output
, 1997
"... The paper summarizes some important results at the intersection of the fields of Bayesian statistics and stochastic simulation. Two statistical analysis issues for stochastic simulation are discussed in further detail from a Bayesian perspective. First, a review of recent work in input distribution ..."
Abstract

Cited by 20 (8 self)
 Add to MetaCart
The paper summarizes some important results at the intersection of the fields of Bayesian statistics and stochastic simulation. Two statistical analysis issues for stochastic simulation are discussed in further detail from a Bayesian perspective. First, a review of recent work in input distribution selection is presented. Then, a new Bayesian formulation for the problem of output analysis for a single system is presented. A key feature is analyzing simulation output as a random variable whose parameters are an unknown function of the simulation's inputs. The distribution of those parameters is inferred from simulation output via Bayesian responsesurface methods. A brief summary of Bayesian inference and decision making is included for reference.
AN ASYMPTOTIC ALLOCATION FOR SIMULTANEOUS SIMULATION EXPERIMENTS
, 1999
"... In this paper, we consider the allocation of a fixed total number of simulation replications among competing design alternatives in order to (i) identify the best simulated design, (ii) intelligently determine the best simulation run lengths for all simulation experiments, and (iii) significantly re ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
In this paper, we consider the allocation of a fixed total number of simulation replications among competing design alternatives in order to (i) identify the best simulated design, (ii) intelligently determine the best simulation run lengths for all simulation experiments, and (iii) significantly reduce the total computation cost. An asymptotically optimal allocation rule for maximizing a lower bound of the probability of correct selection is presented. Moreover, we illustrate the efficiency of our method with a series of generic numerical experiments. The simulation cost is significantly reduced with our sequential approach.
Comparison Of Bayesian And Frequentist Assessments Of Uncertainty For Selecting The Best System
, 1998
"... An important problem in discreteevent stochastic simulation is the selection of the best system from a finite set of alternatives. There are many techniques for ranking and selection and multiple comparisons discussed literature. Most procedures employ classical frequentist approaches, although the ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
An important problem in discreteevent stochastic simulation is the selection of the best system from a finite set of alternatives. There are many techniques for ranking and selection and multiple comparisons discussed literature. Most procedures employ classical frequentist approaches, although there has been recent attention to Bayesian methods. In this paper, we compare Bayesian and frequentist assessments of unknown means of simulation output. First, we present a Bayesian formulation for describing the probability that a system is the best, given prior information and simulation output. This formulation provides a measure of evidence that a given system is best when there are two or more systems, with either independent or common random numbers, with known or unknown variance and covariance for the simulation output, given a Gaussian output assumption. Many, but not all, frequentist assessments are shown to be derivable from assumptions of normality of simulation output when certai...
A SURVEY OF RANKING, SELECTION, AND MULTIPLE COMPARISON PROCEDURES FOR DISCRETEEVENT SIMULATION
, 1999
"... Discreteevent simulation models are often constructed so that an analyst may compare two or more competing design alternatives. This paper presents a survey of the literature for two widelyused statistical methods for selecting the best design from among a finite set of k alternatives: ranking and ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Discreteevent simulation models are often constructed so that an analyst may compare two or more competing design alternatives. This paper presents a survey of the literature for two widelyused statistical methods for selecting the best design from among a finite set of k alternatives: ranking and selection (R&S) and multiple comparison procedures (MCPs). A comprehensive survey of each topic is presented along with a summary of recent unified R&SMCP approaches. In addition, an example of the application of Nelson and Matejcik’s (1995) combined R&SMCP procedure is given.
Proceedings of the 2001 Winter Simulation Conference
"... Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a universitybased education in modeling and simulation to meet the challenges. We examine the factors affecting ..."
Abstract
 Add to MetaCart
Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a universitybased education in modeling and simulation to meet the challenges. We examine the factors affecting the composition of a modeling and simulation course. Based on the anticipated consequences, we propose potential solutions.
Proceedings of the 2002 Winter Simulation Conference
"... A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corre ..."
Abstract
 Add to MetaCart
A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corresponds to this mental model. An understanding of how the model works is required. Simulation models for implementation must be developed step by step, starting with a simple model, the simulation prototype. After this has been explained to the user, a more detailed model can be developed on the basis of feedback from the user. Software for simulation prototyping is discussed, e.g., with regard to the ease with which models and output can be explained and the speed with which small models can be written.
Proceedings of the 2003 Winter Simulation Conference
"... The model used in this report focuses on the analysis of ship waiting statistics and stock fluctuations under different arrival processes. However, the basic outline is the same: central to both models are a jetty and accompanying tankfarm facilities belonging to a new chemical plant in the Po ..."
Abstract
 Add to MetaCart
The model used in this report focuses on the analysis of ship waiting statistics and stock fluctuations under different arrival processes. However, the basic outline is the same: central to both models are a jetty and accompanying tankfarm facilities belonging to a new chemical plant in the Port of Rotterdam. Both the supply of raw materials and the export of finished products occur through ships loading and unloading at the jetty. Since disruptions in the plants production process are very expensive, buffer stock is needed to allow for variations in ship arrivals and overseas exports through large ships. Ports provide jetty facilities for ships to load and unload their cargo. Since ship delays are costly, terminal operators attempt to minimize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This article considers three arrival processes: stockcontrolled, equidistant per ship type, and Poisson. We assess how their deployment in a port simulation model, based on data from a real case study, affects the efficiency of the loading and unloading process. Poisson, which is the chosen arrival process in many clientoriented simulations, actually performs worst in terms of both ship delays and required storage capacity. Stockcontrolled arrivals perform best with regard to ship delays and required storage capacity. In the case study two types of arrival processes were considered. The first type are the socalled stockcontrolled arrivals, i.e., ship arrivals are scheduled in such a way, that a base stock level is maintained in the tanks. Given a base stock level of a raw material or ...
Proceedings of the 1998 Winter Simulation Conference
, 1998
"... In this paper we compare the average performance of one class of lowdiscrepancy quasiMonte Carlo sequences for global optimization. Weiner measure is assumed as the probability prior on all optimized functions. We show how to construct van der Corput sequences and we prove their consistency. Numer ..."
Abstract
 Add to MetaCart
In this paper we compare the average performance of one class of lowdiscrepancy quasiMonte Carlo sequences for global optimization. Weiner measure is assumed as the probability prior on all optimized functions. We show how to construct van der Corput sequences and we prove their consistency. Numerical experimentation shows that the van der Corput sequence in base 2 has a better average performance.
A BAYESIAN APPROACH TO ANALYSIS OF LIMIT STANDARDS
"... Limit standards are probabilistic requirements or benchmarks regarding the proportion of replications conforming or not conforming to a desired threshold. Sample proportions resulting from the analysis of replications are known to be beta distributed. As a result, standard constructs for defining a ..."
Abstract
 Add to MetaCart
Limit standards are probabilistic requirements or benchmarks regarding the proportion of replications conforming or not conforming to a desired threshold. Sample proportions resulting from the analysis of replications are known to be beta distributed. As a result, standard constructs for defining a confidence interval on such a proportion, based on critical points from the normal or Student’s t distribution, are increasingly inaccurate as the mean sample proportion approaches the limits of 0 or 1. We consider the Bayesian relationship between the beta and binomial distributions as the foundation for a sequential methodology in the analysis of limit standards. The benefits of using the beta distribution methodology are variance reduction, and smaller sample size (when compared to other analysis methodologies). 1
PERFORMANCE MEASURES FOR RANKING AND SELECTION PROCEDURES ABSTRACT
"... To efficiently compare Ranking and Selection procedures, we present a threelayer performance evaluation process. The two most popular formulations, namely the Bayes and Indifference Zone formulations, have a common representation analogous to convex risk measures used in quantitative risk managemen ..."
Abstract
 Add to MetaCart
To efficiently compare Ranking and Selection procedures, we present a threelayer performance evaluation process. The two most popular formulations, namely the Bayes and Indifference Zone formulations, have a common representation analogous to convex risk measures used in quantitative risk management. We study decision makers’ acceptance sets via an axiomatic approach and introduce a new performance measure using computational cost. 1