Technology & IT Skills
Fast analytical simulation questions: 15-question practice quiz
This simulation quiz helps you quickly spot gaps in discrete-event modeling, event lists, distributions, and output stats. Work through 15 scenario-based items, then see instant feedback with pointers for further study. Exploring related topics? Brush up mechanics with our statics quiz and review circuits with an electronics quiz.
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1What characterizes a discrete-event simulation in modeling systems?
2What is the primary fun<wbr>ction of an event list in a simulation model?
3Which data structure is most commonly used to manage an event list efficiently?
4What is the purpose of random variate generation in input modeling?
5What does verification in simulation modeling primarily ensure?
6Which probability distribution is most appropriate for modeling time between independent random events in a Poisson process?
7What is the inverse transform method used for in simulation?
8What does statistical analysis of simulation output typically involve?
9What is the most effective strategy for handling simultaneous events in a discrete-event simulation?
10Why is selecting an appropriate probability distribution crucial in simulation input modeling?
11What is the primary goal of analyzing simulation output data statistically?
12Which data structure is considered most efficient for managing the event list in large-scale simulations?
13What is the purpose of incorporating a warm-up period in simulation experiments?
14Which random variate generation method is preferable when the inverse CDF is not available analytically?
15In validation of simulation models, what does the concept of face validity refer to?
Learning Goals
Study Outcomes
- Understand the principles and components of discrete-event simulation.
- Apply event scheduling and management techniques to model complex systems.
- Analyze verification and validation methods for simulation models.
- Interpret input modeling approaches, including probability distributions and random variate generation.
- Evaluate statistical techniques for analyzing simulation output data.
Study Guide
Simulation Additional Reading
Here are some top-notch resources to supercharge your understanding of discrete-event simulation:
- Discrete Event Simulation: It's Easy with SimPy! This paper introduces SimPy, a Python-based discrete event simulation module, through the classic Dining Philosophers Problem, showcasing its practical applications and integration with other Python libraries.
- Discrete-event Simulation: Modeling, Programming, and Analysis Authored by George S. Fishman, this comprehensive book delves into the fundamentals of discrete-event simulation, covering modeling, programming, input-data preparation, and output-data analysis, with illustrative examples throughout.
- Discrete Event Simulation Manual: Manufacturing Applications Brian T. Hughes provides a manual focusing on discrete event simulation in manufacturing, offering tutorials on ProModel software and guiding students through the simulation process with practical examples.
- Discrete Event Simulation Engineering Gerd Wagner's book explains designing discrete event simulations using UML and DPMN, and implementing them with tools like OESjs, Simio, or AnyLogic, emphasizing object-oriented modeling and event scheduling.
- NPTEL Course: Modelling and Simulation of Discrete Event System This course from IIT Roorkee offers video lectures on discrete event system modeling and simulation, covering topics like statistical models, queueing systems, random number generation, and output analysis.
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Updated Feb 19, 2026