| A RANKING AND SELECTION
PROJECT: EXPERIENCES FROM A UNIVERSITY-INDUSTRY COLLABORATION
|
||
|---|---|---|
| David
Goldsman School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, U.S.A. |
Tracy
Opicka School of Industrial Engineering Purdue University West Lafayette, IN 47907, U.S.A. | |
| Barry L. Nelson Department of Industrial Engineering & Management Sciences Northwestern University Evanston, IL 60208, U.S.A. |
Alan B. Pritsker 9032 E. Cedar Waxwing Drive Sun Lakes, AZ 85248, U.S.A. | |
| ABSTRACT | ||
| We describe the experiences and results from a long-term collaboration between two universities and Pritsker Corporation on a grant funded by the National Science Foundation. The goal of the joint work is to make state-of-the-art research in the area of ranking and selection available to practicing engineers and management scientists. | ||
| Return to
Wintersim |
||
| SIMULATION OPTIMIZATION
METHODOLOGIES |
||
|---|---|---|
| Farhad
Azadivar Department of Industrial and Manufacturing Systems Engineering Kansas State University Manhattan, KS 66506, U.S.A. |
||
| ABSTRACT | ||
| Simulation models can be used as the objective function and/or constraint functions in optimizing stochastic complex systems. This tutorial is not meant to be an exhaustive literature search on simulation optimization techniques. It does not concentrate on explaining well-known general optimization and mathematical programming techniques either. Its emphasis is mostly on issues that are specific to simulation optimization. Even though a lot of effort has been spent to provide a reasonable overview of the field, still there are methods and techniques that have not been covered and valuable works that may not have been mentioned. | ||
| Return to
Wintersim |
||
| STOCHASTIC OPTIMIZATION
AND THE SIMULTANEOUS PERTURBATION METHOD
|
||
|---|---|---|
| James C.
Spall The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel, Maryland 20723-6099, U.S.A. |
||
| ABSTRACT | ||
| Multivariate stochastic optimization plays a major role in the analysis and control of many real-world systems. In almost all large-scale practical optimization problems, it is necessary to use a mathematical algorithm that iteratively seeks out the solution because an analytical (closed-form) solution is rarely available. In the above spirit, the "simultaneous perturbation stochastic approximation (SPSA)" method for difficult multivariate optimization problems has been developed. SPSA has recently attracted considerable international attention in areas such as statistical parameter estimation, feedback control, simulation-based optimization, signal and image processing, and experimental design. The essential feature of SPSA¾which accounts for its power and relative ease of implementation¾is the underlying gradient approximation that requires only two measurements of the objective function regardless of the dimension of the optimization problem. This feature allows for a significant decrease in the cost of optimization, especially in problems with a large number of variables to be optimized. | ||
| Return to
Wintersim |
||
| ADVANCED INPUT MODELING
FOR SIMULATION EXPERIMENTATION |
||
|---|---|---|
| Bruce
Schmeiser School of Industrial Engineering Purdue University West Lafayette, IN 47907-1287, U.S.A. |
||
| ABSTRACT | ||
| We discuss ideas useful to simulation practitioners when specifying the probability models used to represent stochastic behavior. Emphasis is on situations in which the classical simple models are inadequate. After discussing some general modeling issues, we consider univariate distributions, nonnormal random vectors and time series, and nonhomogeneous Poisson processes. | ||
| Return to
Wintersim |
||
| INPUT MODELS FOR
SYNTHETIC OPTIMIZATION PROBLEMS |
||
|---|---|---|
| Charles H.
Reilly Department of Industrial Engineering and Management Systems University of Central Florida P.O. Box 162450 Orlando, Florida 32816, U.S.A. |
||
| ABSTRACT | ||
| In this paper, we describe and discuss alternative input models for the coefficients in synthetic optimization problems. Synthetic, or randomly generated, problems are often used in computational studies to establish the efficacy of solution methods or to facilitate comparative evaluations of solution methods. The selection of an input model for the coefficients in synthetic optimization problems is important because such a selection may affect the outcome of a computational study. Understanding how an assumed input model affects the characteristics of test problems can assist researchers in their efforts to accurately quantify and interpret the performance of solution methods. | ||
| Return to
Wintersim |
||
| PARALLEL AND DISTRIBUTED
SIMULATION |
||
|---|---|---|
| Richard M.
Fujimoto College of Computing Georgia Institute of Technology Atlanta, GA 3033, USA |
||
| ABSTRACT | ||
| This tutorial gives an introduction to parallel and distributed simulation systems. Issues concerning the execution of discrete-event simulations on parallel and distributed computers either to reduce model execution time or to create geographically distributed virtual environments are covered. The emphasis of this tutorial is on the algorithms and techniques that are used in the underlying simulation executive to execute simulations on parallel and distributed computing platforms. | ||
| Return to
Wintersim |
||
| SIMULATION IN AN
OBJECT-ORIENTED WORLD |
||
|---|---|---|
| Jeffrey A.
Joines Stephen D. Roberts Department of Industrial Engineering Campus Box 7906 North Carolina State University Raleigh, NC 27695-7906, U.S.A. |
||
| ABSTRACT | ||
| An object-oriented simulation (OOS) consists of a set of objects that interact with each other over time. This pa-per provides a presentation of OOS design elements by contrasting OOS with its procedural counterparts. The elements of component technology is addressed along with the important issue of composition (components) versus inheritance that distinguishes object-based from object-oriented languages. | ||
| Return to
Wintersim |
||
| SIMULATION: TECHNOLOGIES
IN THE NEW MILLENNIUM |
||
|---|---|---|
| Wayne J.
Davis General Engineering University of Illinois at Urbana-Champaign Urbana, Illinois 61801 |
||
| ABSTRACT | ||
| This paper first addresses future simulations needs from a generic point of view and then from the viewpoint of major stakeholders within the simulation community. These stakeholders include the simulation software developer/ vendor, the corporate end user, the government end-user, the researcher and the educator. We then describe or outline the set of capabilities that will be needed to design and manage future systems, and also the limitations of the current simulation tools in meeting these needs. Finally, we conjecture about the kind of simulation-based design and planning capabilities that might exist in future manufacturing systems. | ||
| Return to
Wintersim |
||
| VERIFICATION AND
VALIDATION: WHAT IMPACT SHOULD PROJECT SIZE AND COMPLEXITY HAVE ON ATTENDANT V&V ACTIVITIES AND SUPPORTING INFRASTRUCTURE? |
||
|---|---|---|
| Panel
Presentation |
||
| Co-Chair and
Moderator James D. Arthur The Department of Computer Science Virginia Tech Blacksburg, VA 24061, USA |
Co-Chair Panel Member
and Respondent Robert G. Sargent The Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY 13244, USA | |
| Panel Members and Respondents |
||
| James B.
Dabney AverStar, Inc. 100 Hercules, Suite 300 Houston, Texas 77058, USA |
Averill M.
Law Averill M. Law & Associates PO Box 40996 Tucson, AZ 85717, USA |
John D. (Jack)
Morrison PO Box 1663, MS F602 Los Alamos, NM 87545, USA |
| ABSTRACT | ||
The size and complexity of Modeling and
Simulation (M&S) application continue to grow at a significant rate.
The focus of this panel is to examine the impact that such growth should
be having on attendant Verification and Validation (V&V) activities.
Two prominent considerations guiding the panel discussion are:
| ||
| Return to
Wintersim |
||