MV3302 Introduction to Discrete Event Simulation Modeling

This course provides an introduction to Discrete Event Simulation (DES) methodology, modeling, and analysis. Use of DES formalism, such as Event Graph methodology, for design of models. Component-based implementation of event graph models on a platform such as Simkit. Use of simulation components for building models using composition. DES modeling of movement and sensing. Random variate generation. Simple output analysis.


Java programming; or permission of instructor; Basic Probability and Statistics at the level of OA3101 and OA3103

Lecture Hours


Lab Hours


Course Learning Outcomes

  • Students will be able to explain the core principles of Discrete Event Simulation (DES), including the significance of events, the state of the system, the event list, and time advancement mechanisms.
  • Students will gain a comprehensive understanding of Event Graph Methodology. They will be able to construct Event Graphs to represent complex systems accurately.
  • Students will acquire hands-on experience with Simkit, learning to navigate its environment, utilize its libraries, and implement simulation models. They will become proficient in coding simulations in Java using the Simkit framework.
  • Students will be able to apply Discrete Event Simulation and Event Graph Methodology to model, simulate, and analyze complex systems in various domains such as manufacturing, healthcare, logistics, and service operations.
  • Students will be able to apply Discrete Event Simulation and Event Graph Methodology to model and simulate entities that interact with each other, including vehicles, aircraft. These models will include capturing simple movement, sensing, and weapons effects.
  • Students will learn to collect, analyze, and interpret simulation output data effectively. They will understand the importance of statistical analysis in validating simulation models and making informed decisions based on simulation results.
  • Students will be able to design and conduct simulation experiments, including determining run lengths, replication strategies, and confidence intervals, to ensure that simulation results are both accurate and reliable.