doc. Ing. Jan Roupec, Ph.D.
E-mail:   roupec@fme.vutbr.cz  WWW:   http://www.zam.fme.vutbr.cz/~jroupec/ Dept.:   Institute of Automation and Computer Science Dept. of Computer Networks Position:   Associate Professor Room:   A4/702 Dept.:   Dean's Office ICT Office Position:   Technician Room:   A4/702

Supervised courses:

• Algorithms and Programming Techniques (VCP-K)
• Algorithms and Programming Techniques (VCP)
• C and C++ Programming Languages (VCJ-A)
• Computer Communication (VPK-A)
• Computer Communication (VPK-AK)
• Computer Networks and IoT (VPN-A)
• Computer Networks and IoT (VPN)
• Computer Networks and IoT (VPN-K)
• Hardware and Microprocessor Technique (VHT)
• Hardware and Microprocessor Technique (VHT-K)
• Object-Oriented Programming (VOB)
• Object-Oriented Programming (VOB-K)
• Power Systems Automation (VAS)
• Power Systems Automation (VAS-K)
• Selected Chapters from Programming (0VP)

Publications:

• POPELA, P.; HRABEC, D.; KŮDELA, J.; ŠOMPLÁK, R.; PAVLAS, M.; ROUPEC, J.; NOVOTNÝ, J.:
  Waste processing facility location problem by stochastic programming: Models and solutions,
  Advances in Intelligent Systems and Computing, pp.167-179, ISBN 9783319978871, (2018), Springer Nature
  conference paper
  akce: MENDEL 2017: 23rd International Conference on Soft Computing, Brno, 20.06.2017-22.06.2017
• HRABEC, D.; POPELA, P.; ROUPEC, J.:
  WS Network Design Problem with Nonlinear Pricing Solved by Hybrid Algorithm,
  Parallel Problem Solving from Nature – PPSN XIV, pp.655-664, ISBN 978-3-319-45823-6, (2016), Springer International Publishing
  conference paper
  akce: 14th International Conference on Parallel Problem Solving from Nature, Edinburg, 17.09.2016-21.09.2016
• HRABEC, D.; POPELA, P.; ROUPEC, J.; JINDRA, P.; NOVOTNÝ, J.:
  Hybrid Algorithm for Wait-and-See Transportation Network Design Problem with Linear Pricing,
  21st International Conference of Soft Computing, MENDEL 2015, pp.183-188, ISBN 978-3-319-19823-1, (2015), VUT
  journal article in Scopus
  akce: 21st International Conference on Soft Computing — MENDEL 2015, Brno University of Technology, 23.06.2015-25.06.2015
• HRABEC, D.; POPELA, P.; ROUPEC, J.; MAZAL, J.; STODOLA, P.:
  Two-Stage Stochastic Programming for Transportation Network Design Problem,
  Mendel 2015: Recent Advances in Soft Computing, pp.17-25, ISBN 978-3-319-19824-8, (2015)
  conference paper
  akce: 21st International Conference on Soft Computing — MENDEL 2015, Brno University of Technology, 23.06.2015-25.06.2015
• POPELA, P.; NOVOTNÝ, J.; ROUPEC, J.; HRABEC, D.; OLSTAD, A.:
  Two-Stage Stochastic Programming for Engineering Problems,
  Engineering Mechanics, Vol.21, (2014), No.5, pp.335-353, ISSN 1802-1484
  journal article - other
• HRABEC, D.; POPELA, P.; ROUPEC, J.; JINDRA, P.; HAUGEN, K.; NOVOTNÝ, J.; OLSTAD, A.:
  Hybrid Algorithm for Wait-and-see Network Design Problem,
  20th International Conference of Soft Computing, MENDEL 2014, pp.97-104, ISBN 978-80-214-4984-8, (2014)
  journal article in Scopus
  akce: 20th International Conference on Soft Computing, MENDEL 2014, Brno University of Technology, 25.06.2014-27.06.2014
• ŠEDA, M.; ROUPEC, J.; ŠEDOVÁ, J.:
  Transportation Problem and Related Tasks with Application in Agriculture,
  International Journal of Applied Mathematics and Informatics, Vol.8, (2014), No.1, pp.26-33, ISSN 2074-1278
  journal article - other
• ROUPEC, J.; POPELA, P.; HRABEC, D.; NOVOTNÝ, J.; HAUGEN, K.; OLSTAD, A.:
  Hybrid Algorithm for Network Design Problem with Uncertain Demands,
  Lecture Notes in Engineering and Computer Science WCECS 2013, pp.554-559, ISBN 978-988-19252-3-7, (2013)
  conference paper
  akce: World Congress on Engineering and Computer Science 2013, San Francisco, 23.10.2013-25.10.2013

List of publications at Portal BUT

Abstracts of most important papers:

• ROUPEC, J.; POPELA, P.:
  The Nested Genetic Agorithms for Distributed Optimization Problems,
  Proceedings of The World Congress on Engineering and Computer Science 2011, pp.480-484, ISBN 978-988-18210-9-6, (2011)
  conference paper
  akce: World Congress on Engineering and Computer Science, San Francisco, 19.10.2011-21.10.2011
  Firstly, we review basic principles of the distributed modeling approach in optimization and present introduction to the formal framework based on the concept of a distributed optimization program. The framework is a general one and may be utilized for various classes of decision problems. The DOPs (distributed optimization programs) are introduced as syntactical entities containing certain optimization elements and based on composition rules. They may describe both basic and advanced mathematical programs (e.g., dynamic, stochastic, multistage, and hierarchical) and also game theory models. In addition, more complicated models can be derived from these building stones and further transformed in the syntactical correct way. Although the introduced descriptions are particularly designed for manipulations of programs structures, semantics for certain DOPs can also be defined. Hence, the next challenge is to search promising solutions in the feasible sets of optimization elements of DOPs. Therefore, several genetic algorithms (GAs) are chosen to search in separate feasible sets and they may also exchange information about different populations for achieved solutions of DOP elements in various ways. The general inspiration comes from decomposition techniques in scenario-based multistage programs, so the name nested GAs is used in our case. The computational results and implementation description are presented for the specific min-max problems that are chosen as elementary prototype instances.
• ROUPEC, J.:
  Advanced Genetic Algorithms for Engineering Design Problems,
  Engineering Mechanics, Vol.17, (2011), No.5/6, pp.407-417, ISSN 1802-1484
  journal article - other
  The study of analogy of the natural evolution and the technical object design dates back more than 50 years. The genetic algorithm (GA) is considered to be a stochastic heuristic (or meta-heuristic) optimisation method. The best use of GA can be found in solving multidimensional optimisation problems, for which analytical solutions are unknown (or extremely complex) and efficient numerical methods are also not known. Genetic algorithms are inspired by adaptive and evolutionary mechanisms of live organisms, but they do not copy the natural process precisely. The paper describes the main terms, principles and original implementation details of GA. The main goal of this paper is to help readers to use proper GAs on the field of technical objects design.
• ŠANDERA, Č.; POPELA, P.; ROUPEC, J.:
  The Worst Case Analysis by Heuristic Algorithms,
  Mendel 2009, pp.109-114, ISBN 978-80-214-3884-2, (2009)
  conference paper
  akce: MENDEL 2009 - 15th International Conference on Soft Computing, Brno University of Technology, 24.06.2009-26.06.2009
  The paper is focused on the extreme set of scenarios in stochastic programming. The special genetic algorithm heuristically finds these worst cases.
• ROUPEC, J.; POPELA, P.:
  Genetic Algorithms for Scenario Generation in Stochastic Programming: Motivation and General Framework,
  Lecture Notes in Electrical Engineering, book series: Advances in Computational Algorithms and Data Analysis, Vol. 14 Ao, S.L., Rieger, B., Chen, S.S. (Eds.)., pp.527-536, ISBN 978-1-4020-8918-3, (2008), Springer
  book chapter
  Stochastic programs have been developed as useful tools for modeling of various application problems. The developed algorithms usually require a solution of large-scale linear and nonlinear programs because the deterministic reformulations of the original stochastic programs are based on empirical or sampling discrete probability distributions, i.e. on so-called scenario sets. The scenario sets are often large, so the reformulated programs must be solved. Therefore, the suitable scenario set generation techniques are required. Hence, randomly selected reduced scenario sets are often employed. Related confidence intervals for the optimal objective function values have been derived and are often presented as tight enough. However, there is also demand for goal-oriented scenario generation to learn more about the extreme cases. Traditional deterministic max-min and min-min techniques are significantly limited by the size of scenario set. Therefore, this text introduces a general framework how to generate and modify suitable scenario sets by using genetic algorithms. As an example, the search of absolute lower and upper bounds by using GA is presented and further enhancements are discussed. The proposed technique is implemented in C++ and GAMS and then tested on real-data examples.
• POPELA, P., ROUPEC, J., OŠMERA, P., MATOUŠEK, R.:
  The Formal Stochastic Framework for Comparison of Genetic Algorithms,
  The 2002 IEEE World Congress on Computational Intelligence, pp.576-581, ISBN 0-7803-7281-6, (2002), IEEE
  conference paper
  akce: The 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, 12.05.2002-17.05.2002
  The paper purpose is to discuss the comparison of GAs. The iterations are considered as random element realizations for GAs formally defined. The quantification of algorithm capabilities inspires the use of statistical methods. The significant difference between various setups is detected by statistical tests for the test problem.