WSC 2005

WSC 2005 Final Abstracts

Advanced Tutorials Track

Monday 10:30:00 AM 12:00:00 PM
Optimization of Logistics Simulations

Chair: Markus Klug (ARC Seibersdorf research GmbH)

The Optimizing-Simulator: Merging Simulation and Optimization Using Approximate Dynamic Programming
Warren B. Powell (Princeton University)

There has long been a competition between simulation and optimization in the modeling of problems in transportation and logistics, machine scheduling and similar high-dimensional problems in operations research. Simulation strives to model operations, often using rule-based logic. Optimization strives to find the best possible solution, minimizing costs or maximizing profits. In this tutorial, we show how these two modeling technologies can be brought together, combining the flexibility of simulation with the intelligence of optimization.

Monday 1:30:00 PM 3:00:00 PM
Random Number Generation

Chair: Hongmei Chi (Florida State University)

Fast Random Number Generators Based on Linear Recurrences Modulo 2: Overview and Comparison
Pierre L'Ecuyer and Francois Panneton (Université de Montréal)

Random number generators based on linear recurrences modulo 2 are among the fastest long-period generators currently available. The uniformity and independence of the points they produce, over their entire period length, can be measured by theoretical figures of merit that are easy to compute, and those having good values for these figures of merit are statistically reliable in general. Some of these generators can also provide disjoint streams and substreams efficiently. In this paper, we review the most interesting construction methods for these generators, examine their theoretical and empirical properties, and make comparisons.

Monday 3:30:00 PM 5:00:00 PM
Input Modeling

Chair: Bruce Schmeiser (Purdue University)

Should We Model Dependence and Nonstationarity, and If So How?
Shane G. Henderson (Cornell University)

It can be difficult to develop, fit to data, and generate from, models that capture dependence and/or nonstationarity in simulation inputs. Should we bother? How should we go about it? I will discuss these issues, focusing on three examples: call center arrivals, ambulance travel speeds and wind modeling for America's Cup races.

Tuesday 8:30:00 AM 10:00:00 AM
Verification, Validation, and Accreditation

Chair: Daniel Machado (Grupo de Produção Integrada COPPE/UFRJ)

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

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 accreditation is briefly discussed.

Tuesday 10:30:00 AM 12:00:00 PM
Modeling Call Centers

Chair: Douglas Morrice (The University of Texas at Austin)

Modeling and Simulation of Call Centers
Athanassios N Avramidis and Pierre L'Ecuyer (Université de Montréal)

In this review, we introduce key notions and describe the decision problems commonly encountered in call center management. Main themes are the central role of uncertainty throughout the decision hierarchy and the many operational complexities and relationships between decisions. We make connections to analytical models in the literature, emphasizing insights gained and model limitations. The high operational complexity and the prevalent uncertainty suggest that simulation modeling and simulation-based decision-making could have a central role in the management of call centers. We formulate some common decision problems and point to recently developed simulation-based solution techniques. We review recent work that supports modeling the primitive inputs to a call center and highlight call center modeling difficulties.

Tuesday 1:30:00 PM 3:00:00 PM
Network Traffic Modeling

Chair: Richard Fujimoto (Georgia Institute of Technology)

Advanced Concepts in Large-scale Network Simulation
David M. Nicol and Michael Liljenstam (University of Illinois, Urbana-Champaign) and Jason Liu (Colorado School of Mines)

This tutorial paper reviews existing concepts and future directions in selected areas related to simulation of large-scale networks. It covers specifically topics in traffic modeling, simulation of routing, network emulation, and real-time simulation.

Tuesday 3:30:00 PM 5:00:00 PM
Inside Simulation Software

Chair: Ingolf Ståhl (Stockholm School of Economics)

Inside Discrete-Event Simulation Software: How it Works and Why it Matters
Thomas J. Schriber (The University of Michigan) and Daniel T. Brunner (Systemflow Simulations, Inc.)

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.

Wednesday 8:30:00 AM 10:00:00 AM
Selection of the Best

Chair: Wheyming Song (National Tsing Hua University)

Statistical Selection of the Best System
David Goldsman and Seong-Hee Kim (Georgia Institute of Technology) and Barry L. Nelson (Northwestern University)

This tutorial discusses some statistical procedures for selecting the best of a number of competing systems. The term "best" may refer to that simulated system having, say, the largest expected value or the greatest likelihood of yielding a large observation. We describe various procedures for finding the best, some of which assume that the underlying observations arise from competing normal distributions, and some of which are essentially nonparametric in nature. In each case, we comment on how to apply the above procedures for use in simulations.

Wednesday 10:30:00 AM 12:00:00 PM
Output Analysis

Chair: James Wilson (NC State University)

Review of Advanced Methods for Simulation Output Analysis
Christos Alexopoulos and Seong-Hee Kim (Georgia Institute of Technology)

This paper reviews statistical methods for analyzing output data from computer simulations. First, 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. Second, it reviews recent statistical procedures to find the best system among a set of competing alternatives.