Systems Simulation
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This site features information about discrete event system modeling and simulation. It includes discussions on descriptive simulation modeling, programming commands, techniques for sensitivity estimation, optimization and goal-seeking by simulation, and what-if analysis.
Advancements in computing power, availability of PC-based modeling and simulation, and efficient computational methodology are allowing leading-edge of prescriptive simulation modeling such as optimization to pursue investigations in systems analysis, design, and control processes that were previously beyond reach of the modelers and decision makers.
Professor Hossein Arsham
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Introduction & Summary
Statistics and Probability for Simulation
Topics in Descriptive Simulation Modeling
Techniques for Sensitivity Estimation
Simulation-based Optimization Techniques
Metamodeling and the Goal seeking Problems
"What-if" Analysis Techniques
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Probability and Statistics Resources
Optimization Resources
Simulation Resources
Introduction & Summary
Statistics and Probability for SimulationStatistics for Correlated Data What Is Central Limit Theorem? What Is a Least Squares Model? ANOVA: Analysis of Variance Exponential Density Function Poisson Process
Goodness-of-Fit for Poisson Uniform Density Function Random Number Generators Test for Random Number Generators Some Useful SPSS Commands References & Further Readings
Topics in Descriptive Simulation ModelingModeling & Simulation Development of Systems Simulation A Classification of Stochastic Processes Simulation Output Data and Stochastic Processes Techniques for the Steady State Simulation Determination of the Warm-up Period Determination of the Desirable Number of Simulation Runs
Determination of Simulation Runs Simulation Software Selection Animation in Systems Simulation SIMSCRIPT II.5 System Dynamics and Discrete Event Simulation What Is Social Simulation? What Is Web-based Simulation? Parallel and Distributed Simulation References & Further Readings
Techniques for Sensitivity EstimationIntroduction Applications of sensitivity information Finite difference approximation Simultaneous perturbation methods Perturbation analysis Score function methods Harmonic analysis Conclusions & Further Readings
Simulation-based Optimization TechniquesIntroduction Deterministic search techniques
Heuristic search technique
Complete enumeration and random choice
Response surface search Pattern search techniques
Conjugate direction search
Steepest ascent (descent)
Tabu search technique
Hooke and Jeeves type techniques
Simplex-based techniques Probabilistic search techniques
Random search
Pure adaptive and hit-and-run search Evolutionary Techniques
Simulated annealing
Genetic techniques
A short comparison
References and Further Readings Stochastic approximation techniques
Kiefer-Wolfowitz type techniques
Robbins-Monro type techniques Gradient surface method Post-solution analysis Rare Event Simulation Conclusions & Further Readings
Metamodeling and the Goal seeking ProblemsIntroduction Metamodeling Goal seeking Problem References and Further Readings
"What-if" Analysis TechniquesIntroduction Likelihood Ratio (LR) Method Exponential Tangential in Expectation Method Taylor Expansion of Response Function Interpolation Techniques Conclusions & Further Readings
Introduction & Summary
Computer system users, administrators, and designers usually have a goal of highest performance at lowest cost. Modeling and simulation of system design trade off is good preparation for design and engineering decisions in real world jobs.
In this Web site we study computer systems modeling and simulation. We need a proper knowledge of both the techniques of simulation modeling and the simulated systems themselves.
The scenario described above is but one situation where computer simulation can be effectively used. In addition to its use as a tool to better understand and optimize performance and/or reliability of systems, simulation is also extensively used to verify the correctness of designs. Most if not all digital integrated circuits manufactured today are first extensively simulated before they are manufactured to identify and correct design errors. Simulation early in the design cycle is important because the cost to repair mistakes increases dramatically the later in the product life cycle that the error is detected. Another important application of simulation is in developing "virtual environments" , e.g., for training. Analogous to the holodeck in the popular science-fiction television program Star Trek, simulations generate dynamic environments with which users can interact "as if they were really there." Such simulations are used extensively today to train military personnel for battlefield situations, at a fraction of the cost of running exercises involving real tanks, aircraft, etc.
Dynamic modeling in organizations is the collective ability to understand the implications of change over time. This skill lies at the heart of successful strategic decision process. The availability of effective visual modeling and simulation enables the analyst and the decision-maker to boost their dynamic decision by rehearsing strategy to avoid hidden pitfalls.
System Simulation is the mimicking of the operation of a real system, such as the day-to-day operation of a bank, or the value of a stock portfolio over a time period, or the running of an assembly line in a factory, or the staff assignment of a hospital or a security company, in a computer. Instead of building extensive mathematical models by experts, the readily available simulation software has made it possible to model and analyze the operation of a real system by non-experts, who are managers but not programmers.
A simulation is the execution of a model, represented by a computer program that gives information about the system being investigated. The simulation approach of analyzing a model is opposed to the analytical approach, where the method of analyzing the system is purely theoretical. As this approach is more reliable, the simulation approach gives more flexibility and convenience. The activities of the model consist of events, which are activated at certain points in time and in this way affect the overall state of the system. The points in time that an event is activated are randomized, so no input from outside the system is required. Events exist autonomously and they are discrete so between the execution of two events nothing happens. The SIMSCRIPT provides a process-based approach of writing a simulation program. With this approach, the components of the program consist of entities, which combine several related events into one process.
In the field of simulation, the concept of "principle of computational equivalence" has beneficial implications for the decision-maker. Simulated experimentation accelerates and replaces effectively the "wait and see" anxieties in discovering new insight and explanations of future behavior of the real system.
Consider the following scenario. You are the designer of a new switch for asynchronous transfer mode (ATM) networks, a new switching technology that has appeared on the marketplace in recent years. In order to help ensure the success of your product in this is a highly competitive field, it is important that you design the switch to yield the highest possible performance while maintaining a reasonable manufacturing cost. How much memory should be built into the switch? Should the memory be associated with incoming communication links to buffer messages as they arrive, or should it be associated with outgoing links to hold messages competing to use the same link? Moreover, what is the best organization of hardware components within the switch? These are but a few of the questions that you must answer in coming up with a design.
With the integration of artificial intelligence, agents and other modeling techniques, simulation has become an effective and appropriate decision support for the managers. By combining the emerging science of complexity with newly popularized simulation technology, the PricewaterhouseCoopers, Emergent Solutions Group builds a software that allows senior management to safely play out "what if" scenarios in artificial worlds. For example, in a consumer retail environment it can be used to find out how the roles of consumers and employees can be simulated to achieve peak performance.
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