Checking date: 12/07/2020


Course: 2020/2021

Quantitative models and methods in management II
(15725)
Study: Bachelor in Industrial Technologies Engineering (256)


Coordinating teacher: GARCIA GUTIERREZ, ISABEL

Department assigned to the subject: Department of Mechanical Engineering

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
No previous courses are required.
Competences and skills that will be acquired and learning results. Further information on this link
Knowledge of the fundamentals of quantitative methods applied to complex problems in industrial management. Ability to apply the most suitable technique in each case. Results interpretation and value of the result in the context of the real situation. Define simulation models for the study of alternative cofiguration in industrial management. Identify the classical non polynomial problems whose resolution require the use of heuristic methods. Know the fundamentals and dynamic of heuristic search algorithms and of nature-inspired algorithms.
Description of contents: programme
Productive and logistic system analysis by means of discrete event simulation. Design of simulation models. Simulation model implementation. Result analysis and configuration comparison in simulation. Introduction to NP problems. Review of the classical NP problems: the salesman problem, the knapsack problem, task programming with limited resources. Heuristic search algorithms.
Learning activities and methodology
Lectures, exercises, practical sessions, cases and assignments to be carried out by the students and discussed during the sessions, autonomous student work, individual and group toutoring. Practical sessions in the laboratory. Participation in the partial and final evaluation sessions.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Basic Bibliography
  • Adenso Díaz. Optimización heurística y redes neuronales. Paraninfo. 1996 (Capítulos 1 y 3)
  • Goldberg. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. 1989 (Capítulos 1 y 2)
  • Haupt and Haupt. Practical genetic algorithms. John Wiley & Sons. 1998
  • Law and Kelton. Simulation modeling and analysis . McGraw-Hill. 2007
  • Rich and Knight. Inteligencia artificial. McGraw-Hill. 1994 (Capítulos 2 y 3)
  • Taha, H.. Investigación de operaciones. Pearson. 2004

The course syllabus and the academic weekly planning may change due academic events or other reasons.