Simulation of Manufacturing and Service Systems

Description

Theoretical aspects concerning the performance evaluation of a system by means of Monte Carlo estimation / stochastic simulation. To teach the students the necessary skills to model company situations as discrete event systems (DES), implement those models in DES software, how to run experiments and interpret the results.

Contents

Methodology:
•  Types of simulation
•  Generating random sequences
•  Monte Carlo estimation
•  Discrete event systems: events, agenda, event handlers
•  Variance reduction methods and confidence intervals
•  Ergodicity, stationarity, transition period, regeneration
•  Perfect simulation
•  Simulation-based optimisation methods
•  Demonstration of the above principles in Python

Applications:
•  Use of DES simulation tool FlexSim
•  Collecting simulation data, processing and correct interpretation
•  Conducting case studies: identifying problems and optimising performance