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Tutorials can be proposed in the following three categories:

  •     T1- Introductory tutorials

  •     T2- State of the Art Tutorials

  •     T3- Software and Modelware


The following tutorials will be presented when enough registrants register for them:



Performance Modeling of Quality of Service Architectures in the Internet


New architectures as integrated service architecture (intserv) and differentiated service architecture (diffserv) are introduced to improve the QoS of the internet. The diffserv uses different traffic classes (e.g. video conferences, e-commerce, voice, data, ..) with different priorities. In the first part of the tutorial analytical methods to determine QoS parameters as throughput or delay times in time independent and time dependent priority systems are introduced. These methods are used in the second part of the tutorial to obtain QoS parameters of the "proportional diffserv architecture" of the internet and find an optimal scheduler for this architecture.

AUTHOR's BIOGRAPHY (Gunther Bolch)

Gunther Bolch is Academic Director at the Department of Computer Science 4 (Distributed and Operating Systems) at the University of Erlangen-Nuernberg, Germany. He is researching and lecturing in the area of performance modeling, process control and operating systems. He has written five textbooks and more than 100 publications. He was involved in organizing national and international conferences and is member of the GI, SCS and the special interest group MMB of the GI.



Designing Neuro-Fuzzy Systems - Reviews and Prospects for Applications in Practice


Integrating Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to meet the real world requirements. ANN learns from scratch by adjusting the interconnections between layers. A valuable property of ANN is that of generalization, whereby a trained network is able to provide a correct matching in the form of output data for a set of previously unseen input data. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. With crisp inputs and outputs, FIS implements a nonlinear mapping from its input space to output space by a number of if-then rules. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. We broadly classify the NF systems into three categories namely concurrent model, cooperative model and fully integrated model. We briefly discuss the features of each model and generalize the advantages and deficiencies of each model. We will also attempt to give some insights when to use which model.

This tutorial starts with some basic theoretical aspects of ANN and FIS and we further discuss the step-by-step modeling of different NF architectures. We also demonstrate how NF systems could be used for many practical applications involving prediction, classification tasks etc.

AUTHOR's BIOGRAPHY (Ajith Abraham)

Ajith Abraham is currently a research scholar at Monash University, Australia. His research interests are in optimizing soft computing architectures particularly in the integration of ANN and FIS. In the soft computing arena, he is an active researcher and has over 20 publications in international conference proceedings and journals. Before turning into a full time researcher he was working as a professional engineer and has about 8 years of experience in the Industry. More details at:



What Makes a Simulation Credible? A Risk-Based Approach to Cost-Effective VV&A


What makes a simulation "credible", and what activities contribute to simulation "credibility"? This tutorial begins by identifying and defining three key factors that contribute to simulation credibility, and then correlates these factors to well-known verification and validation (V&V) activities. We then discuss elements that contribute to the quality of V&V results (and hence to their impact on simulation credibility) and criteria by which the quality of V&V results can be evaluated. The concept of "application risk" is introduced as a means to prioritize simulation credibility requirements (and the VV&A activities associated with them).

We then describe an approach to structuring V&V activities in light of requirements for accreditation, followed by suggestions for meaningful documentation products. The tutorial concludes with lessons learned from the VV&A "front", applying these techniques to actual case studies. This tutorial should have broad appeal to anyone interested in how to define and meet requirements for simulation credibility at minimum cost.

AUTHORS' BIOGRAPHIES (Michelle Kilikauskas, David H. Hall)

Ms. Kilikauskas is an Operations Research Analyst with the Joint Accreditation Support Activity (JASA); she is an internationally recognized expert in the practical application of VV&A policy and methodology for models and simulations. She supports many U.S. systems with cost-effective VV&A programs. She also serves on the ITOPS international VV&A working group.

Mr. Hall is a Mathematician, serving as Chairman of the Methodology Subgroup for the Joint Technical Coordinating Group on Aircraft Survivability (JTCG/AS), which is the Joint Service organization that sponsors JASA. He has extensive experience in model and simulation use and VV&A.



Simulation and Optimization - Performance in Industry


Simulation and Optimization in the field of needs planning, production scheduling and distribution as well as transportation is becoming more and more important, especially for companies which are strictly or mainly driven by customer orders. Customers ask for ever shorter delivery times, and delivery reliability has become a critical success factor for many companies, particularly for many SMEs. However, conventional planning tools do not support reliable planning because of the following weaknesses:

Since there are no exact data available on the current state of the production facilities, planning is based on assumptions. These assumptions are based on past experience, and on average values, e.g. of available capacities. This may be sufficient for mass production, but is not adequate when precise delivery dates of a specific product or service are negotiated with a customer.

Simulation tools can model all these constraints, and optimisation techniques can be used to generate beforehand a plan which satisfies the constraints in an optimal way. The extra burden of re-scheduling is taken away from the operator, and production becomes smoother and more predictable.

It is expected that this approach will help increase the reliability of planning, reduce the throughput time and the work in progress. It will also help the enterprises to communicate effectively with customers and agree on realistic delivery times.

The tutorial deals with an intelligent toolset for modelling, simulation and optimization PPSIMOPT and ISSOP of supply chain management in connection with just in time production processes and distributions for user companies, which has currently considerable problems with their capacity utilisation: They have to reject many customer orders because they are often not sure if they have sufficient capacity available to meet the required delivery dates. On the other hand, capacity utilisation is in general only 60%. They expect that better planning and forecasting tools will enable them to take approx. 30% more customer orders, thus reaching a capacity utilisation of approx. 80% on average.


Willy Krug is director of the company DUALIS (Dresden, Germany; see
Prof. W. Krug, DUALIS Dresden, Germany

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Prague Castle


NEW Abstract Submission

February 5-2001






February 25- 2001





Full Paper Submission

April 30-2001






June 6-9, 2001

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