This course will introduce students to various aspects of analyzing complex systems using simulation methodologies. The course will address broadly four main forms of simulation of systems:
- Discrete event simulation for solving queuing and inventory problems,
- Multi-agent system simulation involving agent-based simulation for solving complex engineering problems,
- Monte Carlo simulation with applications in ﬁnancial engineering, and
- System Dynamics to model physical and business phenomena.
Case studies with applications to airport facility design, ﬁnancial engineering, healthcare (A&E), and inventory management will be discussed. Implementation of simulation techniques, comparison of competing designs and statistical analysis of output will be conducted using a variety of programming tools including AnyLogic, MS Excel, and R.
On successful completion of this course, students should be able to
- Identify complex problems such as facility design for heavy industry or patient flow design for hospitals, which could be analyzed using simulation tools.
- Describe and Interpret salient features of different simulation methodologies.
- Implement various simulation techniques using programming tools.
- Analyze and critically Evaluate competing systems using simulation tools.
On successful completion of this course, students will be adept in the following.
- Demonstrating critical thinking skills in identifying complex problems from a variety of disciplines that can be precisely formulated for analysis using simulation tools.
- Identifying and Comparing different simulation methodologies, including but not restricted to, Discrete Event simulation, Monte Carlo Simulation, Agent-based Simulation, and Systems Dynamics (simulation).
- Examining and Executing random number generation techniques for discrete and continuous distributions as well as random processes both theoretically as well as using programming tools (R, MS Excel, Python).
- Implementing the aforementioned simulation methodologies (described in (3)) using available software, for example, AnyLogic, MS Excel or R.
- Determining and Analyzing burn-in periods and simulation run times to achieve statistically reliable estimates of simulated system performance.
- Recommending and Ranking competing systems by Designing simulation experiments as well as Employing statistical inference and variance reduction techniques for comparison studies.