This diploma thesis deals with an issue of tour-routes and an optimization of routes for travel agencies, namely the Rialto travel agency from the city Přerov. Two tours, offered by this travel agency, are optimized and three optimization methods are used - the nearest neighbour metho, the Vogel approximation Method and the savings method. The first part of this thesis describes the theoretical issue of topic - the legislation concerning the travel agencies, transportation logistics, the usage of operations research in transport logistics and travelling salesman problem issue and methods used to solve the travelling salesman problem. In the second part the Rialto travel agency is briefly described, then the tours are described and these tours are optimized. The last part contents the analysis of obtained results, the results are evaluated and suggestions considering the economic, temporal and legislative (rest and working hours of drivers) aspects are made.

Objective of this thesis is optimalization of route for inspectory technician. There were described traveling Salesman problem, vehicle Routing problem and it's modifications. Problem was solved by this three heuristics: nearest neighbour algorithm, savings method and insert method.

This thesis is specialized in instance based learning algorithms. Main goal is to create an application for educational purposes. There are instance based learning algorithms (IBL), nearest neighbor algorithms and kd-trees described theoretically in this thesis. Practical part is about making of tutorial application. Application can generate data, classified them with nearest neighbor algorithm and is able of IB1, IB2 and IB3 algorithm testing.

Voter decision-making about which political party to support in the elections is a very complex process. One of the key factors influencing our voting behaviour is the effect of particular candidates in terms of their local activities. Many Czech and also foreign studies show that candidates gain significantly higher preferences in the municipality of their residence and its surroundings. Within the analyzed parliamentary elections, this is reflected not only by the territorial concentration of preferential votes, but also by higher local electoral support of the political parties or movements represented by these candidates. This diploma thesis examines, among other things, differences in the strength of the described effect (technically called the friends and neighbours effect) between candidates from different political parties and in different regions of Czechia. Quantitative research has shown greater local strength and spatial extent of friends and neighbours effect for leading candidates from non-metropolitan areas who have experiences from local or regional politics, such as mayors or regional councilors. In the 2017 parliamentary elections, the friends and neighbours effect mostly influenced the spatial patterns of electoral support of the KDU-ČSL and the STAN movement and on the other...

The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.

Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.

This work describes static and dynamic problems with one messenger or multiple number of messengers and suggests a possibility of solving such problems with modified heuristic methods. To solve messenger problem, modified nearest neighbor heuristic, modified insertion heuristic and modified exchange heuristic are used. The main contribution of this work are applications, developed in MS Excel, programmed with Visual Basic for Application, that can solve static and dynamic problems with one messenger or multiple number of messengers and that could be beneficial for companies that do business in messenger services.

This diploma thesis deals with static and dynamic capacitated messenger problem and its solving with heuristic algorithms. Different variations of the capacitated messenger problem were considered, with a single messenger or multiple messengers, with one depot or multiple depots in case of multiple messengers. Limited time for route realization was another modification that was considered. Modified nearest neighbour method, modified insertion method and modified exchange method were used to solve the problem. The main contribution of the thesis is deriving heuristics for described types of messenger problem and programming the algorithms in VBA (Visual Basic for Applications) in MS Excel. The results of computational experiments indicate that modified nearest neighbour method leads to better outcomes in static multiple messenger problems with a single depot, while modified insertion method is associated with lower values of objective function in static multiple messenger problem with multiple depots. Modified exchange method improves original solutions. Modified insertion method was approved for solving dynamic multiple messenger problems.