Prerequisites topics
Basic programming, Concurrent registration with Simulation course
Contents:
- Building simulation models of deterministic/ probabilistic; static/ dynamic models using software such as Python/ R/ other custom software.
- Use of general purpose languages such as Python/R/C++ (for Monte Carlo simulation) and/or packages such as Anylogic, Flexsim, etc (for discrete event/ agent based models) to build simulation models
- Implementing Monte Carlo Simulation models, monte carlo integration, random processes, markov chains.
- Discrete event simulation: Simulating Queueing models, single server, tandem queues, various queuing logics and decision logics.
- Conducting simulation experiments, Simulation based optimization.
- Visualizing simulation results, Building 2D and 3D animations.
- Simulation study of large systems such as hospitals, super markets, railway network, airport operations, manufacturing systems using all steps in simulation study, starting with problem conceptualization, data collection, input data analysis, model building, model verification and validation, conduct simulation experiments, make observations and recommendations, write a report.
- Advanced topics: Agent based models, system dynamics models, Introduction to augmented reality based simulation, virtual reality simulations, Interactive simulation, Digital Twins, Gaming models
References
- A. M. Law and W. D. Kelton (2000), Simulation Modeling and Analysis, 3rd Ed., McGraw Hill International - Industrial Engg. Series.
- J. Banks, J. S. Carson, B. L. Nelson and D. M. Nicol (2013), Discrete Event System Simulation,6th Ed., Pearson Education International Series.
- S. Ross (2012) Simulation​, Academic Press.
- U Wilensky and W Rand (2015), An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo, MIT Press
- Dr. Andrei Borshchev, Ilya Grigoryev (2012) The Big Book of Simulation modeling.