342-0630/02 – Unconventional Optimizing Methods (NMOP)

Gurantor departmentInstitute of TransportCredits3
Subject guarantordoc. Ing. Dušan Teichmann, Ph.D.Subject version guarantordoc. Ing. Dušan Teichmann, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2011/2012Year of cancellation2020/2021
Intended for the facultiesFSIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
DOR028 doc. Ing. Michal Dorda, Ph.D.
TEI72 doc. Ing. Dušan Teichmann, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 3+2
Part-time Credit and Examination 12+6

Subject aims expressed by acquired skills and competences

The student will gain knowledge of the principles of methods for modeling and solving optimization problems, whose use in the transportation practice is still widespread. Their use comes into consideration, especially in situations where the conventional optimization methods (graph theory, linear programming, queuing theory, etc.) fail to solve problems, either because of lack of theoretical work in progress addressing the appropriate apparatus or due to large computational complexity of the task solved.

Teaching methods

Lectures
Tutorials

Summary

The course focuses on teaching non-conventional methods for solving optimization problems, which are generally accepted as a substitute for methods that result in optimal solutions. The solution of optimization problems are used mainly in the course of Petri nets and artificial intelligence methods (genetic algorithms, neural networks, swarm intelligence). Brief mention will be given to the principles of advanced heuristics (metaheuristických) methods (Simulated annealing, tabu search).

Compulsory literature:

MARKL, J.. Petri nets. Ostrava: VSB-TU Ostrava. http://www.cs.vsb.cz/markl/pn/index.html DOSTÁL, P.. Advanced methods of analysis and modeling of business and public administration. Brno: Academic Publishing House CERM, 2008. 340 p. ISBN 978-80-7204-605-8 JANÁČEK, J.. Optimization of transport networks. Zilina: University of Žilina, 2006. 2nd edition, 248 p. ISBN 80-8070-586-0

Recommended literature:

MARČEK, D.; MARČEK, M.. Neural networks and their applications. Zilina: University of Žilina, 2006. 223 p. ISBN 80-8070-497-X ZELINKA, I.; OPLATKOVÁ, Z.; ŠEDA, M.; OŠMERA, P.; VČELAŘ, F.. Evolutionary computing - principles and applications. Praha: BEN - technical literature, 2008. 534 p. ISBN 978-80-7300-218-3

Way of continuous check of knowledge in the course of semester

E-learning

Other requirements

The next requirements for students are not defined.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Petri nets Genetic algorithms Neural Networks Metaheuristiky - Simulated Annealing, Tabu Search Examples of applications of unconventional methods to transport jobs from practice

Conditions for subject completion

Full-time form (validity from: 2012/2013 Winter semester, validity until: 2020/2021 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)		Min. number of pointsMax. počet pokusů
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 35  17
        Examination Examination 65  16 3
Mandatory attendence participation:

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2012/2013 (N2301) Mechanical Engineering (2301T003) Transport Equipment and Technology (40) Air Transport P Czech Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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