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Systems Modelling and Simulation

Code: 101743
Credits: 6
2026/2027
Degree programme Type Course
Aeronautical Management OB 3

Contact lecturer

Name :
Miquel Àngel Piera Eroles
Email :
miquelangel.piera@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

There are s no previous hard subject requirements, although it is recommendable a minimum base of statistics and basic knowledge of programming in Python.

 

Objectives

The subject Systems Modelling and Simulation could be taught in different degrees, because what is intended is that students learn to perform a simulation model of any system in order to have more knowledge about it, and make the best possible decisions to improve the performance of it. In the case of airports, there are three major subsystems: airlines, users and airport infrastructures. Getting models including part of the three subsystems would greatly help decision making within an airport.

The objectives of the subject are specified in:

  1. Be able to develop a conceptual model of any system using modeling formalism called Petri Nets and Colored Petri Nets.
  2. Be able to develop a simulation model in pseudocode and its implementation in any known programming language, and also in some simulation software.
  3. Be able to apply the basic statistical tools necessary for the development of a complete simulation model.
  4. Know how to use the simulation model to identify and solve possible problems that may occur in the system.

 

Learning outcomes

  • CM17 (Prepare opinions or reports based on optimisation models, simulation and quantitative techniques for decision-making processes, systematising, documenting and reflecting the process and mechanisms used.) Prepare opinions or reports based on optimisation models, simulation and quantitative techniques for decision-making processes, systematising, documenting and reflecting the process and mechanisms used.
  • CM18 (Propose decisions based on available and contrasted data and analysis tools in the context of the aeronautical industry that are in line with regulatory and ethical frameworks, and the objectives of the organisation. (CT04)) Propose decisions based on available and contrasted data and analysis tools in the context of the aeronautical industry that are in line with regulatory and ethical frameworks, and the objectives of the organisation. (CT04)
  • CM19 (Communicate data analysis results clearly and effectively to technical and non-technical audiences.) Communicate data analysis results clearly and effectively to technical and non-technical audiences.
  • SM26 (Develop the conceptual model of aeronautical systems using the Petri net formalism, to represent and analyse dynamic processes and improve performance indicators in the aeronautical sector.) Develop the conceptual model of aeronautical systems using the Petri net formalism, to represent and analyse dynamic processes and improve performance indicators in the aeronautical sector.
  • SM29 (Apply a simulation model based on Petri nets, through the use of specific software, to identify and solve problems in the management of resources and operations in the aeronautical sector.) Apply a simulation model based on Petri nets, through the use of specific software, to identify and solve problems in the management of resources and operations in the aeronautical sector.

Contents

 


Topic 1: Introduction to Digital Simulation


Introduction to the development of a simulation project:



  •     Definitions and basic concepts

  •     Stages of a simulation project

  •     Simulation tools

  •     Application fields



Topic 2: Modeling of Discrete Event Systems


Development of Discrete Event Systems models using Petri Nets:



  •     Definitions and basic concepts

  •     Dynamics of a Petri Net

  •     Modeling Bottom-Up

  •     Analysis of Petri Nets


 


Topic 3: Statistical Models for Simulation


Basic statistics for the simulation:



  •     Introduction

  •     Identification of the statistical properties of a sample

  •     Most used distribution functions

  •     Generation of random numbers

  •     Dependence between random variables

  •     Hypothesis test (Chi-Square-Test)


 


Topic 4: Simulation of Discrete Event Systems



  •     Elements of a simulator

  •     Management policies for the variable time (pseudocode)

  •     Simulation environments (software)

  •     Design of experiments


Topic 5: Resource Management



  •     Introduction to resource management.

  •     Experimental techniques:


    • Evaluation of bottlenecks.

    • Little's law.

    • Algorithms to minimize variance.



 

Learning activities and methodology

Title Hours ECTS Learning outcomes
Problem classes 12 0.48 CM17, CM19
Practices 24 0.96 SM26, SM29
Personal study 23 0.92 CM18, SM26
Preparation of Problems 20 0.8 CM17, CM18, SM26
Practical classes 12 0.48 CM19, SM29
Practices 6 0.24 CM19, SM29
Problems 15 0.6 CM17, CM19
Theory classes 26 1.04 SM26
Assessment 4.5 0.18 SM26

The teaching methodology used in this subject is based on the resolution of problems and the participation of students in it. The subject is focused in a very practical way and it is essential that students participate in the activities, since it is the best way to learn. The master classes of the subject are reduced to the essentials to be able to have the basic knowledge to solve the proposed problems. Students can use AI tools to implement models.

The course could be divided into different activities:

  1.     Master classes: typical lectures, including student participation through questions and / or small exercises.
  2.     Problems: realization of problems in the classroom and its correction.
  3.     Practices: learning of a simulation environment and the realization of simulation models of diverse systems. The practices will be done in groups of 3 students.
  4.     Exams: Students will present a simulation model implemented in pyhton and a final exam. 

 

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Submission and presentation of 4 simulation models 20% 2 0.08 CM17, CM18, CM19, SM26
Practices 40% 3 0.12 SM29
Final exam 40% 2.5 0.1 SM26

This subject does not consider the single assessment system.

See assessment in Catalan or Spanish.

 

Bibliography

Robust Modelling and Simulation: Integration of SIMIO with Coloured Petri Net / Idalia Flores De La Mota, Antoni Guasch, Miguel Mujica Mota, Miquel Angel Piera.

Modelado y simulación: aplicación a procesos logísticos de fabricación y servicios / Antoni Guasch ... [et al.]

Petri nets: a tool for design and management of manufacturing systems / Jean-Marie Proth and Xiaolan Xie Proth, Jean –Marie.

Coloured petri nets: modelling and validation of concurrent systems / Kurt Jensen, Lars M. Kristensen Jensen, Kurt.

Cómo mejorar la logística de su empresa mediante la simulación: Miquel Àngel Piera ... [et al.]

Simio & simulation: modeling, analysis, applications / W. David Kelton, Jeffrey S. Smith, David T. Sturrock

Simulation modeling with SIMIO: a workbook / Jeffrey Allen Joines ; Stephen Dean Roberts. Joines, Jeffrey A.

 

Software

Python

MS Excel

R-Simmer

SIMIO

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 11 Catalan second semester afternoon
(PAUL) Classroom practices 11 Catalan second semester afternoon
(PAUL) Classroom practices 12 Catalan second semester afternoon
(PLAB) Practical laboratories 21 Spanish second semester afternoon
(PLAB) Practical laboratories 22 Spanish second semester afternoon