WSC 2007 Final Abstracts

Inside Discrete-event Simulation Software: How it Works and Why it Matters
Thomas J. Schriber (University of Michigan) and Daniel T. Brunner (Kiva Systems Incorporated)

Abstract:
This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including coverage of SIMAN (Arena), ProModel, and GPSS/H as well as the other three tools.

Verification and Validation of Simulation Models
Robert G. Sargent (Syracuse University)

Abstract:
In this paper we discuss verification and validation of simulation models. Four different approaches to deciding model validity are described; two different paradigms that relate verification and validation to the model development process are presented; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity are discussed; a way to document results is given; a recommended procedure for model validation is presented; and model accreditation is briefly discussed.

Defense and Homeland Security Applications of Multi-agent Simulations
Thomas W. Lucas and Susan M. Sanchez (Naval Postgraduate School), Felix Martinez (Centro de Estudios Superiores Navales), Lisa R. Sickinger (USS Laboon (DDG-58)) and Jonathan W. Roginski (United States Military Academy)

Abstract:
Department of Defense and Homeland Security analysts are increasingly using multi-agent simulation (MAS) to examine national security issues. This paper summarizes three MAS national security studies conducted at the Naval Postgraduate School. The first example explores equipment and employment options for protecting critical infrastructure. The second case considers non-lethal weapons within the spectrum of force-protection options in a maritime environment. The final application investigates emergency (police, fire, and medical) responses to an urban terrorist attack. There are many potentially influential factors and many sources of uncertainty associated with each of these simulated scenarios. Thus, efficient experimental designs and computing clusters are used to enable us to explore many thousands of computational experiments, while simultaneously varying many factors. The results illustrate how MAS experiments can provide valuable insights into defense and homeland security operations.

Statistical Analysis of Simulation Output: State of the Art
Christos Alexopoulos (Georgia Institute of Technology)

Abstract:
This paper reviews statistical methods for analyzing output data from computer simulations. Specifically, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and methods based on standardized time series.

Recent Advances in Ranking and Selection
Seong-Hee Kim (Georgia Institute of Technology) and Barry L. Nelson (Northwestern University)

Abstract:
This tutorial provides an overview on recent advances made in ranking and selection (R&S) for selecting the best simulated system and discusses challenges that still exist in the field. We focus on indifference-zone R&S procedures that provide a guaranteed probability of correct selection when the best system is at least a user-specified amount better than the other systems.

Real Options Valuation
Barry R. Cobb (Virginia Military Institute) and John M. Charnes (The University of Kansas)

Abstract:
Managerial flexibility has value. The ability of their managers to make smart decisions in the face of volatile market and technological conditions is essential for firms any competitive industry. This advanced tutorial describes the use of Monte Carlo simulation and stochastic optimization for the valuation of real options that arise from the abilities of managers to influence the cash flows of the projects under their control. Option pricing theory supplements discounted cash flow methods of valuation by considering managerial flexibility. Managers' options to take actions that affect real investment projects are comparable to options on the sale or purchase of financial assets. Just as a financial option derives its value from the the price movements of the underlying financial asset, a real option derives its value from the fluctuations of the cash flows generating the value of the investment project.

Regression Models and Experimental Designs: A Tutorial for Simulation Analysts
Jack P. C. Kleijnen (Tilburg University)

Abstract:
This tutorial explains the basics of linear regression metamodels - especially low-order polynomials - and the corresponding statistical designs---namely, fractional factorial designs of resolution III (Plackett-Burman designs), IV (accounting for interactions), V (estimating individual interactions), and Central Composite Designs (CCDs, for second-order polynomial metamodels). This tutorial assumes `white noise', which means that the residuals of the fitted linear regression metamodel are normally, independently, and identically distributed with zero mean. This metamodel requires validation. The tutorial gathers statistical results that are scattered throughout the literature on mathematical statistics, and presents these results in a form that is understandable to simulation analysts.

Making Sure You Tackle the Right Problem: Linking Hard and Soft Methods in Simulation Practice
Michael Pidd (Lancaster University Management School)

Abstract:
As well as knowledge of computing, statistics and modelling, a successful simulation analyst needs to develop ways to understand the important features of an application domain. The last two decades have seen the increasing use of ‘soft’ approaches to help analysts structure the problems they are asked to tackle. The idea is to help ensure that the right problem is tackled. The complementary use of these problem structuring approaches with the technical side of computer simulation offers the prospect of better simulation practice. This advanced tutorial introduces some of these structuring approaches and discusses how they can be used in simulation projects, paying attention to the different ways in which simulation models are used.