Simulation (SIM)

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.

Knowledges

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.

Abilities

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.

Competences

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.

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.

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.

• 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.

• 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.

1. http://eurosim.tuwien.ac.at/eurosim
   



2. http://www.acm.org/sigsim
   



3. http://www.informs-cs.org
   



4. http://www.acm.org/pubs/tomac
   



5. http://www.scs.org
   

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