WSC 2004 Final Abstracts
Sunday 1:00:00 PM 2:30:00 PM
Advances in Simulation Modeling
Chair: Ming Zhou (Indiana State University)
Forecasting is of prime importance for accuracy in decision making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor forecasting. TES (Transform-Expand-Sample) is a class of stochastic processes to model empirical autocorrelated time series and is used in Monte Carlo simulation. Its merit is to simultaneously capture both the empirical distribution function and the autocorrelation function. The transition structure of TES processes can be utilized to calculate forecasts for future periods. In this paper, we utilize phase-type random variables as the innovation density in TES model fitting methodology, and we investigate the forecasting performance of TES processes compared to traditional auto regressive integrated moving-average models. We find that TES models yield forecasts as accurate as time series models.
Incremental Planar Motion
Tony Dean (Motorola, Inc.)
A cellular engineer typically estimates system performance via simulation. An important input to this simulation is the average busy hour subscriber location distribution. The performance of some system features, such as admission control or carrier, antenna, or beam assignment, requires a dynamic mobility model which matches and maintains that distribution. The author discusses the requirements of such a model and presents easily implementable models satisfying those requirements.
Knowledge Representation for Conceptual Simulation Modeling
Ming Zhou (Indiana State University), Young Jun Son (University of Arizona) and Zhimin Chen (Shenzhen University)
Simulation is a powerful tool that helps decision makers in business and industry to solve difficult and complex problems, reduce cost, improve quality and productivity, and shorten time-to-market. However the technology is still underutilized in many applications due to several reasons. In this study we address these issues using a knowledge engineering approach, i.e. develop efficient and robust models and formats to capture, represent and organize the knowledge for developing conceptual simulation models that can be generalized and interfaced with different appli-cations and implementation tools. The research fits into a larger project effort that aims to create a sustained research program on knowledge-based simulation.
An Experimental Study on Forecasting Using TES Processes
Abdullah S. Karaman and Tayfur Altiok (Rutgers University)
Sunday 3:00:00 PM 4:30:00 PM
Modeling Methodologies for Specific Applications
Chair: Ariel Landau (IBM)
The development of simulation models can be time consuming and highly dependant on system data being widely available. When using simulation modeling to analyze fu-ture systems, system data may not be available for the system under study and simulation results are often needed within a short time frame to support early system design efforts. This paper presents a parametric estimation/generic simulation integrated environment developed to facilitate the rapid development of valid simulation models for the Orbital Space Vehicle ground processing operations.
A Manager-Friendly Platform for Simulation Modeling and Analysis of Call Center Queueing Systems
Robert Saltzman and Vijay Mehrotra (San Francisco State University)
Call center operational performance is measured largely through queue times and customer abandonment rates, and thus managers have an acute need to understand how both management policies and stochastic factors affect these performance statistics. Simulation is an excellent vehicle for examining these relationships, but a lack of program-ming ability can be a barrier that prevents call center man-agers from making use of such models. To address this problem, we have developed a user-friendly Excel inter-face for a dynamic discrete event simulation model. The underlying model is a general queuing system for which analytical results are often unavailable, and the Excel inter-face enables managers to interactively specify a wide range of system parameters and analyze results, all without ex-posing them to the simulation model’s components. Based on input from call center operations managers, we have also been able to utilize this framework to ask, and answer, some important empirical questions.
A Methodological Framework for Business-Oriented Modeling of IT Infrastructure
Ariel Landau, Segev Wasserkrug, Dagan Gilat, Natalia Razinkov, Aviad Sela, and Sarel Aiber (IBM )
The creation of IT simulation models for uses such as capacity planning and optimization is becoming more and more widespread. Traditionally, the creation of such models required deep modeling and/or programming expertise, thus severely limiting their extensive use. Moreover, many modern intelligent tools now require simulation models in order to carry out their function. For these tools to be widely deployable, the derivation of simulation models must be made possible without requiring excessive technical knowledge. Hence we introduce a general methodology that enables an almost automatic deployment of IT simulation models, based on three fundamental principles: Modeling only at the required level of detail; modeling standard components using pre-prepared models; and automatically deriving the application-specific model details. The technical details underlying this approach are presented. In addition, a case study, showing the application of this methodology to an eCommerce site, demonstrates the applicability of this approach.
An Integrated Estimation and Modeling Environment for the Design of the Orbital Space Plane
Dayana Cope, Mansooreh Mollaghasemi, and Assem Kaylani (Productivity Apex Inc), Alex J. Ruiz-Torres (Polytechnic University of Puerto Rico), Martin J. Steele (NASA) and Marcella L. Cowen (Blue Frog Technologies Inc.)