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Simulation (SIM)

Credits Dept.
7.5 (6.0 ECTS) EIO


Person in charge:  (-)

General goals

In those situations in which they have to deal with uncertainty, the limits of some of the treatments designed for determinist environments are made manifest, and the need appears for a different methodology for the numerical manipulation of these models: simulation. This subject provides students with the tools they need for:
- Building complex models for simulation.
- Using standard simulation languages for studying models.
- Analysing entry data.
- Designing experiments and analysing the results.
- Visualisation.
- Distributed architectures.
- Aspects related to output.
It aims to give students an in-depth view of advanced features of simulation software and makes special emphasis on integrated developing environments for simulation projects and on their application in specific areas such as production, logistics, services and others more closely linked to computing. The subject is of an eminently practical nature and oriented towards simulation applications.

Specific goals


  1. Understand the various kinds of simulation and their relationship with various scientific and technological fields.
  2. The dominant methodologies in the simulation field and the languages and development environments stemming from them.
  3. The fundamental concepts required to understand the random component of problems, how randomness can be modelled using simulations, the importance of dynamic aspects in complex systems (and compared with more static-oriented technologies.
  4. Learn how to generate random variables using the latest methods of generating random numbers and variables.
  5. Learn how to statistically analyze the results of a simulation and evaluate the quality of the performance estimates obtained.
  6. Understand the strategic importance of data and information in such organisations
  7. Learn of the problems relating to the verification and validation of simulation models.
  8. Understand the problems and importance of standardisation, accreditation, and certification of simulation projects, and trends in the simulation field around the world.
  9. Concepts concerning the level of detail in models and its relationship with aspects of modelling and model implementation.
  10. Learn the most recent developments in the fields of visualisation with regard to simulations, interaction, and user-friendliness.


  1. Capture the essence of complex systems and achieve a capacity for abstraction permitting the construction of simulation models based on specific objectives.
  2. Transform these models into executable programmes that students can design and construct.
  3. Compare and evaluate design alternatives or system implementation using discrete simulation. Use this information to make professional and business decisions.
  4. Appreciate the business possibilities and opportunities for creating simulation-based products and services.
  5. Learn how to communicate in a business setting when offering solutions in a multidisciplinary context or in crisis situations.


  1. Ability to solve problems through the application of scientific and engineering methods.
  2. Ability to create and use models of reality.
  3. Ability to design and carry out experiments and analyse the results.
  4. Know-how to apply the solution cycle to common scientific and engineering problems: specification, coming with ideas and alternatives, design solution strategies, carrying out the strategy, validation, interpretation and evaluation of results. Ability to analyse the process on completion.
  5. Ability to work in multidisciplinary teams.
  6. Creativity.
  7. Ability to make convincing formal and informal oral presentations.
  8. Ability to adapt: Knowing how to deal with new situations arising from organisational and/or technological changes.
  9. Intellectual curiosity and openness.
  10. Knowledge of the local and global social context. Ability to evaluate the potential impact of an engineering solution.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Introduction
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 0 2,0 0 4,0
Simulation: systems and models. Types of simulation: simulation of discrete systems, continuous simulation, hybrid simulation. Relationship between different technological and economic sectors, especially in the computing, services, manufacturing, and logistics fields.

2. Methodologies for constructing discrete simulation models
T      P      L      Alt    Ext. L Stu    A. time Total 
7,0 0 9,0 0 0 13,0 0 29,0
Introduction to the methodologies employed: Event-Oriented, Process Interaction, Activity Scanning. Specification of models. Design and construction of a simulation environment kernel based on object-oriented programming. Application of general environments to the development of simulation projects. Practical work.

  • Laboratory
    Design and construction of a simulation environment kernel based on object-oriented programming.

3. Simulation data.
T      P      L      Alt    Ext. L Stu    A. time Total 
1,0 0 2,0 0 0 3,0 0 6,0
Analysis of the input data for a simulation. information available. Level of detail. Data credibility criteria.

  • Laboratory
    Analysis of the input data. Visualisation of examples.

4. Monte Carlo methods and the sampling process in simulations.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 1,0 0 2,0 5,0 0 10,0
Generation of random numbers. Congruential generators, composites, Tausworthe generators. Generator tests: theoretical and empirical tests. Methods for generating random samples.

Some well-known distributions and their application to simulation models.

  • Additional laboratory activities:
    Examination of the State-of-the-Art in Internet.

5. Simulation languages using discrete systems
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 2,0 0 0 3,0 0 7,0
Methodology of languages for constructing discrete simulation models. Simulation of networks and queues. Network flows. Transactions. Blocks. Resources.

Process interaction oriented languages.

Fitting languages to models.

Application to practical cases.

6. Introduction to development environments for simulation projects.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 9,0 0 2,0 12,0 0 25,0
Complex simulation project development environments. Introduction to LeanSim, Witness, ARENA, GPSS & Proof Animation. Practical applications.

Introduction to practical work: approach to a simulation project employing one of these development environments

  • Laboratory
    Introduction to and use of the foregoing development environments.
  • Additional laboratory activities:
    Prepared exercises with environments

7. Visualisation and animation in simulations.
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 0 1,0 0 0 4,0 0 9,0

Methodologies and approaches to the problem of representing graphics in simulations.

Visualisation for special applications.

Virtual reality. User interfaces for simulations.

8. Design of experiments and analysis of simulation results.
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 0 2,0 0 0 5,0 0 11,0
Basic concepts and methods. Planning in discrete simulation: Experiment design using simulations.

Factorial designs. Design strategies.

Simulation optimisation. Response surfaces. Meta-models. Analysis of simulation results: Study of the behaviour of the transitory state and the stationary state. Analytical methods: Independent repetitions, average batches, regenerative methods, variance reduction techniques.

9. Verification and validation of simulation models.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 0 3,0 0 5,0
Verification, validation, and accreditation of simulation models. Independent validation, credibility, accreditation, certification, standards.

10. Simulation of continuous processes.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 3,0 0 0 3,0 0 8,0
Introduction to system dynamics. Causal diagrams, Forrester diagrams. Relationship between diagrams and differential equations. Introduction to continuous and hybrid simulation languages. Applications and practical cases.

  • Laboratory
    Practical exercises with a continuous simulation language: VenSIM.

11. New paradigms in simulation.
T      P      L      Alt    Ext. L Stu    A. time Total 
4,0 0 3,0 0 0 2,0 0 9,0
Social simulation. Simulation and intelligent agents. Presentation of SWARM. Simulation and SIG. Use of cellular automata in simulation. Simulation and chaos.
  • Laboratory
    SWARM. Cellular automata.

12. Integration assignment
T      P      L      Alt    Ext. L Stu    A. time Total 
0 0 9,0 0 8,0 5,0 8,0 30,0
Application of concepts studied in the construction of a simulation model inspired by a real system.

  • Laboratory
    Students will basically work in DEIO-FIB's LeanSIM environment for developing complex simulation projects.
  • Additional laboratory activities:
    Study of the programming languages to be used. Search for a real or fictitious system to be simulated using LeanSIM. Activity tutored by course faculty.
  • Other extra activities:
    Working in groups. Co-ordination. Preparing a presentation.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
32,0 0 41,0 0 12,0 60,0 8,0 153,0
Avaluation additional hours 4,0
Total work hours for student 157,0

Docent Methodolgy

The course adopts a practical approach to simulating a complex engineering activity whose nature links many fields of knowledge. It introduces the simulation stage of project development environments and stresses the importance of constructing valid, credible models.

The course combines theory and lab classes, introducing theoretical concepts accompanied by demonstrations. It includes a number of short practical sessions in the lab that will be taken into account in student assessments.

Students will: work on either a real or a hypothetical problem; examine the information available; set work objectives; develop the model in a simulation environment (within the limits imposed by time and resources); and evaluate the results. An oral presentation will then be given by the work group.

Evaluation Methodgy

Final grade: 0.3 * T1+ 0.5 * T2 + 0.2 * Exam.

T1: average grade of the practical work conducted during the lab classes.

T2: grade for modelling work using LeanSim or a tailor-made model.

Final exam.

Basic Bibliography

  • Averill M. Law. Simulation modeling and analysis, McGraw-Hill, 2007.
  • George S. Fishman Discrete-event simulation : modeling, programming and analysis, Springer, 2001.
  • Antoni Guasch ... [et al.] Modelado y simulación : aplicación a procesos logísticos de fabricación y servicios, Edicions UPC, 2003.
  • Fonseca i Casas, Pau Simulació discreta per mitjà de la interacció de processos, Edicions UPC, 2009.

Complementary Bibliography

  • Jerry Banks, [editors] Handbook of simulation : principles, methodology, advances, applications, and practice, John Wiley & sons, 1998.
  • José M. Garrido Object-oriented discrete-event simulation with Java : a practical introduction, Kluwer Academic/Plenum Publishers, 2001.
  • W. David Kelton, Randall P. Sadowski, David T. Sturrock Simulation with Arena, McGraw-Hill Higher Education, 2007.

Web links






Previous capacities

Statistics. Recommended: Queuing networks. Understanding the way organisations work, and the way systems in general and information systems in particular function. Object-oriented programming.


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