General information


Subject type: Mandatory

Coordinator: Joan Triadó Aymerich

Trimester: Second term

Credits: 6

Teaching staff: 

Xavier Font Aragonés

Teaching languages


  • English

English: for Theory and Laboratory sessions

Skills


Specific skills
  • CE22: Design and apply models aimed at solving industrial organization problems.

Transversal competences
  • CT1: That the students know a third language, which will preferably be English, at an adequate level orally and in writing and in accordance with the needs of graduates in each degree.

  • CT2: That students have the ability to work as members of an interdisciplinary team either as another member, or performing management tasks in order to contribute to developing projects with pragmatism and a sense of responsibility, assuming commitments taking into account the available resources.

Description


Subject framed in the matter of operative investigation. The course aims to introduce students to the basic concepts, principles and foundations of simulation techniques, game theory, and Markov chains for analysis and decision making in all contexts. Finally, concepts related to business applications are introduced in the context of the digital transformation of the company, such as the big data and the Business intelligence.

 

Contents


 

Content title 1: Analysis of decisions

Dedication:  

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Bayesian theory of decision
  • Decision trees
  • Utility function
  • Analysis of decisions with multiple objectives

Related activities

Active1, Active 3 and Active 4

       

 

Content title 2: Simulation

Dedication:  

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Simulation processes
  • Generation of random numbers
  • Simulation of discrete problems
  • Industrial dynamics
  • Simulation games
  • Digital Twin

Related activities

Active1, Active 3 and Active 4

       

 

Content title 3: Markovian decision-making processes

Dedication:  

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Markov chains
  • Topological and spectral analysis of Markov chains
  • Remuneration in Markov chains
  • Markov chains with remuneration and decision
  • Markovian decision processes

Related activities

Active2, Active 3 and Active 4

       

 

Content title 4: Game theory

Dedication: 

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Introduction
  • Sum-zero games
  • Algebraic or matrix solutions
  • Graphic solutions
  • Linear programming techniques
  • Metagames

Related activities

Active2, Active 3 and Active 4

       

 

Content title 5Big data i Business intelligence

Dedication: 

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Introduction
  • Big data: volume, speed, variety, likelihood
  • Tools of Business intelligence
  • Generation of intelligent control panels
  • Applications: Machine learning for predictive maintenance

Related activities

Active2, Active 3 and Active 4

Evaluation system


 

ACTIVITIES

PES discipline

EXAMS

60% (Pex1 25% + Pex2 35%)

EXERCISES

10%

WORK EXPERIENCE

30%

 

 

 

 

 

 

The final grade is the weighted sum of the grades for the learning activities:

Q = 0.25 Pex1 + 0.35 Pex2 + 0.10 ExiPar + 0.30 Lab

Important: all activities are mandatory. To pass the course, the student must attend (or submit) all the activities!

Remarks on Recovery

The theory part of the subject (Pex1 and Pex2) is recoverable. The other parts are not recoverable. For students attending the resit exam their grade (Pex1 and Pex2) will be the one obtained in this test and their final grade (Q) will be calculated with the formulas detailed above and in no case will it exceed 7.

 

Rules for carrying out the activities

Remarks:

To pass the assessment activities, students must demonstrate the MECES Level - 2:

• (point c) have the ability to collect and interpret data and information on which to base their conclusions, including, where necessary and relevant, reflection on issues of a social, scientific or ethical nature in the field of their field of study

• (point e) know how to communicate to all types of audiences (specialized or not) in a clear and precise way, knowledge, methodologies, ideas, problems and solutions in the field of their field of study;

• (point f) be able to identify their own training needs in their field of study and work or professional environment and to organize their own learning with a high degree of autonomy in all types of contexts

 

For each activity, teachers will be informed of the particular rules and conditions that govern them

The one-to-one activities presuppose the student's commitment to carry them out individually and without any collaboration with other people. All activities in which the student does not comply with this commitment to individuality will be considered suspended (grade 0), regardless of their role (sender or receiver) and without this excluding the possible application of other sanctions in accordance. with the current Disciplinary Regime.

Likewise, the activities to be carried out in groups presuppose the commitment on the part of the students who make it up to carry them out within the group and without any kind of collaboration with other groups or people who are alien (group individuality). All activities in which the group has not respected this commitment regardless of its role (sender or receiver) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime will be considered suspended (rating 0).

In the case of activities that can be done in groups, when in any of them the commitment of group individuality is not respected and / or fraudulent means are used in its accomplishment, the qualification of the activity will be, for all members of the group, of 0 points (Activity Note = 0) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime.

 

Any activity not delivered will be considered a suspended subject

It is optional for teachers to accept or not deliveries outside the deadlines indicated. In the event that these late deliveries are accepted, it is up to the teacher to decide whether to apply a penalty and the amount of this.

REFERENCES


Basic

Hillier, Frederick S .; Lieberman, Gerald J. (2010). Introduction to Operations Research. McGraw-Hill

Jared Dean (2014). Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners. Wiley, ISBN 978-1-118-92069-5.

Complementary

Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling by William J. Stewart; Princeton University Press (July 26, 2009)

Game Theory: An Introduction by Steven Tadelis Princeton University Press; 1st Edition edition January 6, 2013