WSC 2009 Final Abstracts

Better Simulation Metamodeling: The Why, What, and How of Stochastic Kriging
Jeremy Staum (Northwestern University)

Abstract:
Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.

Input Modeling for Hospital Simulation Models Using Electronic Messages
Renata Konrad (Worcester Polytechnic Institute) and Mark Lawley (Purdue University)

Abstract:
Health care organizations function in a complex, non-integrated setting, yet the coordination of information, tasks, and equipment across multiple units is essential for productive operations. A variety of simulation models of hospitals exist; however, few reflect resource sharing across multiple departments. Furthermore few models capture the inherent hetero-geneity of a hospital�s patient mix which plays a crucial role in determining how care is delivered and resources allocated. Patient flow paths can be used as input data to provide systematic insight into resource allocation processes and medical care within a hospital. To date, flow path approaches to studying hospital operations have been hindered by lack of a comprehensive data source. This tutorial describes how electronic communication exchanges between hospital departments are used to create an input model for hospital simulations.

Revenue Management: Models and Methods (Reprint from Proceedings of Winter Simulation Conference 2008)
Kalyan Talluri (ICREA and University of Pompeu Fabra), Garrett van Ryzin (Columbia University), Itir Karaesmen (University of Maryland) and Gustavo Vulcano (New York University)

Abstract:
Revenue management is the collection of strategies and tactics firms use to scientifically manage demand for their products and services. The practice has grown from its origins in airlines to its status today as a mainstream business practice in a wide range of industry areas, including hospitality, energy, fashion retail, and manufacturing. This article provides an introduction to this increasingly important subfield of operations research, with an emphasis on use of simulation. Some of the contents are based on excerpts from the book The Theory and Practice of Revenue Management (Talluri and van Ryzin 2004a), written by the first two authors of this article.

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.

An Introduction to Opensimulator and Virtual Environment Agent-based M&S Applications
Paul Fishwick (University of Florida)

Abstract:
An “agent” in a computer simulation is an object with a dynamic model driving its actions. There are different classifications for agents, for example: autonomous, intelligent, and software. A cell within a cellular automaton might be considered an agent with the complete environment being a multi-agent system. An object containing an artificial intelligence could also be considered an agent. Our purpose is to introduce the “personal” aspect of agents through first-person perspective by becoming one of the agents in the simulation. When a level of presence on the part of the human’s relationship to the agent is incorporated in this fashion, we must incorporate methods found typically within multi-user virtual environments. This tutorial is centered on one particular open-source, multi-user, virtual environment system called OpenSimulator (or OpenSim). We introduce OpenSim to allow the reader an opportunity for understanding how this software is used within the context of agent-based computer simulations.

Introduction to Modeling and Generating Probabilistic Input Processes for Simulation
Michael E. Kuhl (Rochester Institute of Technology), Julie S. Ivy (North Carolina State University), Emily K. Lada (SAS Institute Inc.), Natalie M. Steiger (University of Maine), Mary Ann Wagner (SAIC) and James R. Wilson (North Carolina State University)

Abstract:
Techniques are presented for modeling, fitting, and generating many of the univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Also discussed are nonparametric and semiparametric techniques for modeling, fitting, and generating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. The applications range from pharmaceutical manufacturing and medical decision analysis to smart-materials research and healthcare systems analysis. Many of the references include live hyperlinks providing online access to the referenced material.

Commercial-Off-The-Shelf Simulation Package Interoperability: Issues and Futures
Simon JE Taylor (Brunel University), Stephen J Turner (Nanyang Technological University), Steffen Strassburger (Technical University of Ilmenau), Navonil Mustafee (Brunel University) and Ke Pan (Nanyang Technological University)

Abstract:
Commercial-Off-The-Shelf Simulation Packages (CSPs) are widely used in industry to simulate discrete-event models. Interoperability of CSPs requires the use of distributed simulation techniques. Literature presents us with many examples of achieving CSP interoperability using bespoke solutions. However, for the wider adoption of CSP-based distributed simulation it is essential that, first and foremost, a standard for CSP interoperability be created, and secondly, these standards are adhered to by the CSP vendors. This advanced tutorial is on an emerging standard relating to CSP interoperability. It gives an overview of this standard and presents case studies that implement some of the proposed standards. Furthermore, interoperability is discussed in relation to large and complex models developed using CSPs that require large amount of computing resources. It is hoped that this tutorial will inform the simulation community of the issues associated with CSP interoperability, the importance of these standards and its future.

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

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 ExtendSim is described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including discussion of examples covering AutoMod, SLX, and ExtendSim, and also SIMAN (Arena), Pro-Model, and GPSS/H.

Simulation Optimization Using Metamodels
Russell R. Barton (The Pennsylvania State University)

Abstract:
Many iterative optimization methods are designed to be used in conjunction with deterministic objective functions. These optimization methods can be difficult to apply to an objective generated by a discrete-event simulation, due to the stochastic nature of the response(s) and the potentially extensive run times. A metamodel aids simulation optimization by providing a deterministic objective with run times that are generally much shorter than the original discrete-event simulation. Polynomial metamodels generally provide only local approximations, and so a series of metamodels must be fit as the optimization progresses. Other classes of metamodels can provide global fit; fitting can be done either by constructing the global model once at the start of the optimization, or by using the optimization results to identify additional discrete-event runs to refine the global model. This tutorial surveys both local and global metamodel-based optimization methods.