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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2020/2021

Mathematical Economics 2

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Code Completion Credits Range Language
818ME2 Z,ZK 5 2+2 Czech
Lecturer:
Quang Van Tran (guarantor)
Tutor:
Quang Van Tran (guarantor)
Supervisor:
Department of Software Engineering
Synopsis:

The aim of this course is to provide students with a knowledge of the selected models for economics decision. We focus on dynamic programming, queuing theory, solution of linear and nonlinear models.

Requirements:
Syllabus of lectures:

1. Dynamic programming: problem of optimal division of resources, problem of backpack

2. Dynamic programming: storage optimization

3. Dynamic programming: device recovery issues

4. Stochastic models of economic processes: recovery models

5. Mass operation models: introduction, classification, possibilities of use

6. Mass handling models: M/M/1 and their applications

7. Mass handling models: multiplication and death processes, M/M/C and their applications

8. Multi-criteria decision making: basic concepts and methods

9. Basic concepts of problem solving LP: graphical solution, simplex method-principle

10. Possibilities of solving LP problems in Excel

11. Solution of nonlinear problems: basic principles and concepts

12. Solution of nonlinear problems: one-dimensional optimization, golden section method, quadratic interpolation method

13. Solution of nonlinear problems: gradient method with long and short step

Syllabus of tutorials:

The structure of exercises is identical to lectures. Exercises are focused on typical problems from each theme.

Dynamic programming: resource allocation, knapsack problem

Dynamic programming: production scheduling

Dynamic programming: device recovery

Stochastic models: Markov chains

Queuing theory: introduction, classification

Queuing theory: M/M/1 and application

Queuing theory: M/M/C and application

Multi-criteria decision: introduction

Introduction to linear programming: graphical solution, simplex method

Linear programming in Excel

Nonlinear models: introduction

Solution of nonlinear models in 1D

Solution of nonlinear models: gradient method

Study Objective:

Knowledge: The aim of this course is to provide students basic overview of methods for economics decision

Abilities: Students gain ability to select and use appropriate method for their decision making problems.

Study materials:

Mandatory reading:

[1] Taha, H. A.. Operations Research: An Introduction, 10e. London: Pearson, 2017.

[2] Rardin, R. L.. Optimization in Operations Research, 2e. London: Pearson, 2015.

Recommended reading:

[3] Pelikán, J., Chýna, V.. Kvantitativní management. Praha: VŠE, 2011.

[4] Griva, I., Nash, S. G., Sofer, A.. Linear and Nonlinear Optimization, 2e. Philadelphia: Society for Industrial and Applied Mathematics, 2009.

[5] Kořenář, V.. Stochastické procesy. Praha: VŠE, 2010.

Note:
Time-table for winter semester 2020/2021:
Time-table is not available yet
Time-table for summer semester 2020/2021:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-10-22
For updated information see http://bilakniha.cvut.cz/en/predmet24614605.html