Course detail

Advanced Decision Making in Business

FP-RaeaAAcad. year: 2017/2018

The content of the subject is to make students familiar with the methods of analyses and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) by the way of explanation of the principles of these theories and their resulting applications in managerial practice.

Language of instruction

English

Number of ECTS credits

6

Learning outcomes of the course unit

The student obtains the knowledge of theory of fuzzy logic, artificial neural network, genetic algorithms and theory of chaos. He/she will be able to understand of the use of the above mentioned methods in business and management, to encourage the use of the above mentioned methods in practice, to analyze critically the business and economic processes by the methods of fuzzy logic, artificial neural network, genetic algorithms and theory of chaos.

Prerequisites

The basic knowledge of mathematics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The exam will be granted in case of handing in the final assignment, that will be processed in Excel file solving decision making problem with the help of fuzzy logic. The exam will be classified according ECTS. The way of implementation is in the form of written form pointed within the range 0-20 points. A-20-19;B18-17;C16-15;D14-13;E12-;F10-0.

Course curriculum

1. Introduction
2-3. Fuzzy logic (FL): To be familiar with the basic notions and fuzzy logic rules, creation of models. The presentation of cases of application in decision making processes e.g. managerial and investment decision making, prediction, etc.
4-5. Artificial neural networks (ANN): To be familiar with the basic notions in the area of artificial neural networks, presentation of the notation perceptron, multilayer neural network and their parameters. The applications cover investment decision making, estimations of the price of products, real properties, evaluation of value of client, etc.
6-7. Genetic algorithms (GA): To be familiar with the principles of genetics, the analogy between nature and math description that enables the solution of decision making of problems. The use in the area of optimization of wide spectrum of problems is mentioned - the optimization of investment strategy, production control, cutting plans, curve fitting, the solution of traveling salesman, cluster analyses, etc.
8-9. The theory of chaos: The theory deals with the possibilities of better description of economic phenomena than the classical methods do. The notion chaos, order and fractal are clarified, the use of this is mentioned.
10-11. The use of FL, ANN and GA in the field of prediction, stock market, data mining, risk management, decision making etc. To be familiar with the problems and the way of the use of mentioned methods.
12-13. Simulation: The presentation of the notion system and its identification and simulation. The description of the use of FL, ANN and GA during the process of simulation of decision making processes in enterprise sphere.

Work placements

Not applicable.

Aims

The aim of the course is to get acquainted with some advanced and non-standard methods of analysis and simulation techniques in economy and finance by the method of explanation of these theories, to become familiar with these theories and their use.

Specification of controlled education, way of implementation and compensation for absences

Control of results of independent written projects.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

DOSTÁL, P.: Advanced Decision making in Business and Public Services, Akademické nakla (EN)
DOSTÁL, P. Advanced Economic Analyses. Brno: CERM Akademické nakladatelství, 2008. 80 p. ISBN: 978-80-214-3564-3) (EN)
DOSTÁL, P., SOJKA, Z. Finacial Risk Management, UTB Zlín, 2005,60p, ISBN 80-7318-343-9. (EN)

Recommended reading

ALIEV, A., ALIEV, R. Soft Computing and Its Applications, World Scientific Publishing Ltd, UK2002, 444p., ISBN 981-02-4700-1. (EN)
ALTROCK, C. Fuzzy Logic & Neurofuzzy – Applications in (EN)
BOSE, K., LIANG, P. Neural Network, Fundamental with Graphs, Algorithm and Applications, Mc Graw-Hill, USA, 1996, 478s., ISBN 0-07-114064-6. (EN)
DAVIS, L. Handbook of Genetic Algorithms, Int. Thomson Com. Press, USA, 1991, 385p., ISBN 1-850-32825-0. (EN)

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1.Introductory
2.Fuzzy logic-theory
3.Fuzzy logic-Excel
4.Fuzzy logic-Excel - Assignment
5.Fuzzy logic-+Matlab
6.NN-theory
7.NN-applications,
8.GA-theory
9.GA-practice
10.Theory of chaos
11.Presentation of assignment
12.Written test
13.Evaluation

Exercise

26 hours, compulsory

Teacher / Lecturer