URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=1BK20&collegejaar=2025&taal=enDescription
In this course, you learn to analyse, evaluate and improve the performance of business processes (e.g. throughput time, utilization) by using discrete event simulation. Simulation is an extremely valuable tool in situations where analytical mathematical techniques such as queueing theory are not precise enough, and it’s is one of the most widely used operations research and management science techniques.
The simulation of operational processes belongs to the core competences of the Industrial Engineering engineer whose focus is the (re-) design of
business processes to achieve a desired performance or to improve the actual performance. Simulation models are the backbones of digital twins, which are becoming an increasingly critical tool for organizations to manage and improve their processes effectively. This course mainly consists of project work: in a group, you solve a realistic case, by applying knowledge you gained in previous courses related to process modelling, data modelling, statistics and queuing theory in the context of the simulation study, following a step-wise methodology. The course will also cover
the statistical aspects of simulation: validity and reliability of results, estimating variance reduction and run length, determining simulation parameters and so on.
Objectives
After this course the student is able to:
- Apply the concepts and methodology for a simulation study designed to answer questions about the performance of operational processes.
- Formulate a problem and the assumptions made, based on a case description.
- Design a conceptual model using Petri-nets and UML in order to give a precise specification of the behavior of the operational process that must be analyzed by means of simulation.
- Implement an executable model in SimPN, for a given conceptual model.
- Apply verification and validation of a simulation model using queueing theory.
- Design sound simulation experiments (number of simulation replications, cope with randomness, warm-up period, dependencies) using knowledge on statistics.
- Analyze data from a simulation and to derive statistically valid observations on the average behavior of the system.
- Evaluate the conceptual and executable model of a peer group.
- Work effectively within a group to deliver a well-defined result.
- Write a technical report, where in each chapter the solutions designed for the related step are presented in accordance with professional and scientific norms.