Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozičními metodami, které lze použít k řešení problému v těchto dvou třídách. V práci popisujeme novou metodu pro tvorbu “warm-start” řezů pro metodu zvanou “Generalized Benders Decomposition”, která se používá při řešení dvoustupňových stochastických problémů. Pro třídu úloh s pravděpodobnostním omezením zde uvádíme originální dekompoziční metodu, kterou jsme nazvali “Pool & Discard algoritmus”. Užitečnost popsaných dekompozičních metod je ukázána na několika příkladech a inženýrských aplikacích.

This master’s thesis is about optimal route planning for electric vehicles. The first, theoretical part of this work introduces the issue and describes several heuristic methods that have been used to address optimization tasks. A practical part of the thesis is based around software implementation of the methods described earlier. Finally, several experiments and comparisons of obtained results are carried out.

The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.

The diploma thesis deals with the modeling of selected risks in healthcare. Motivated by the current pandemic situation, it focuses on analysis of risks associated with the vaccination center in Brno. The theoretical part is mainly devoted to the issue of risk management with a focus on risks in healthcare, where the methods that are used in the practical part are defined. Furthermore, the thesis presents selected topics of mathematical programming. Especially, the newsvendor problem is introduced as inspiring case for further modelling. The brief description of the covid-19 pandemic situation later serves as one of the data sources. The practical part deals with the description and risk analysis of the vaccination process using the methods "What If?" and the FMEA method. Appropriate decisions are then proposed for selected risk situations using the GAMS optimization system. Based on the results of the calculations, specific recommendations are proposed.

The master’s thesis outlines the problem of authentication. It describes authentication factors like ownership, knowledge and inherent. There are described properties of each of that. Authentication based on ownership focuses on authenticators - the smartcards. The thesis also describes different types of smartcards (contact, contactless and hybrid smartcards) and refers to their basic properties. Emphasis is placed on the description of contact and contactless smartcard, specifically focusing on .NET smartcards. It describes their internal components such as memory (RAM, ROM, EEPROM), crypto processor etc. Smartcards are also examined in terms of support for cryptographic primitives. The thesis also introduces the cryptographic methods and principles describing symmetric and asymmetric ciphers. Symmetric ciphers are divided in to stream and block ciphers. There is description of asymmetric cipher, digital signature etc. This work also touches on the fundamental principles required for safe programming. Part of this work is also practical implementation (programs). Practical part aims to implement the communication between the user and AC (Access Control) AASR system. The first suite of applications provides encrypted communication between the PC and smartcards. These applications should create on smartcard services that will be able to perform operations on the client side, which are necessary to authenticate in the AASR system. The paper also presents algorithms for working with big numbers - addition, subtraction, multiplication, and Montgomery's algorithm for multiplication. The second application implements the functionality of AC components (Access Control). This functionality is for example – authenticate received token, digital signature authentication, generating random numbers, logging etc.

The master’s thesis incorporates the control system Sinumerik 840D to the most widely used control systems of CNC machine tools. In the first thesis part are analyzed the NC programming methodics, which are relative only to the control system Sinumerik 840D and an essentials necessary to understand the issue in the area of CNC machine tools. Next part of the thesis includes design of the technology and processing of the NC program for machined component (gearbox cover). The NC program is made by ShopMill that is focused on a workshop area. During the program creation is combined a ShopMill and parametric programming, with using of a conditional jump instructions and repetitions. The final part of the thesis includes verification of the selected component production process by simulation and technical-economic evaluation of the designed technology.

The main goal of the master's thesis is to compose a study on cartesian genetic programming with focus on evolution of circuits and to design a concept for visualisation of this evolution. Another goal is to create a program to visualise the circuit evolution in cartesian genetic programming, its generations and chromosomes. The program is capable of visualising the changes between generations and chromosomes and comparing more chromosomes at once. Several user cases had been prepared for the resulting program.

Symbolic regression is the problem of identifying the mathematic description of a hidden system from experimental data. Symbolic regression is closely related to general machine learning. This work deals with symbolic regression and its solution based on the principle of genetic programming and coevolution. Genetic programming is the evolution based machine learning method, which automaticaly generates whole programs in the given programming language. Coevolution of fitness predictors is the optimalization method of the fitness modelling that reduces the fitness evaluation cost and frequency, while maintainig evolutionary progress. This work deals with concept and implementation of the solution of symbolic regression using coevolution of fitness predictors, and its comparison to a solution without coevolution. Experiments were performed using cartesian genetic programming.

Multi-Stage Stochastic Programming with CVaR: Modeling, Algorithms and Robustness RNDr. Václav Kozmík Abstract: We formulate a multi-stage stochastic linear program with three different risk measures based on CVaR and discuss their properties, such as time consistency. The stochastic dual dynamic programming algorithm is described and its draw- backs in the risk-averse setting are demonstrated. We present a new approach to evaluating policies in multi-stage risk-averse programs, which aims to elimi- nate the biggest drawback - lack of a reasonable upper bound estimator. Our approach is based on an importance sampling scheme, which is thoroughly ana- lyzed. A general variance reduction scheme for mean-risk sampling with CVaR is provided. In order to evaluate robustness of the presented models we extend con- tamination technique to the case of large-scale programs, where a precise solution cannot be obtained. Our computational results are based on a simple multi-stage asset allocation model and confirm usefulness of the presented procedures, as well as give additional insights into the behavior of more complex models. Keywords: Multi-stage stochastic programming, stochastic dual dynamic programming, im- portance sampling, contamination, CVaR

1 Web sites offer a huge amount of information. Often it is a page generated from data stored in databases. However, emphasis is placed on the display of information, but not on their machine processing. Part of the thesis is design and implementation of a prototype system to retrieve data from dynamically generated web using programming by demonstration technique. Such a system allows the user to show with mouse to the system how to proceed with gathering information from the website. Based on such a example, the system will derive a procedure to acquire information on similar sites. The implemented system is able to collect user relevant information from similar sites for example in form of a simple table suitable for further machine processing.