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The traveling salesman problem (TSP) consists of finding the shortest way between cities, which passes through all cities and returns to the starting point, given the distance between cities. The Vehicle Routing Problem (VRP) is the issue of defining the assumptions and limitations in mapping routes for vehicles performing certain operational activities. It is a major problem in logistics transportation. In specific areas of business, where transportation can be perceived as added value to the product, it is estimated that its optimization can lower costs up to 25% in total. The economic benefits for more open markets are a key point for VRP. This paper discusses the metaheuristics usage for solving the vehicle routing problem with special attention toward Genetic Algorithms (GAs). Metaheuristic algorithms are selected to solve the vehicle routing problem, where GA is implemented as our primary metaheuristic algorithm. GA belongs to the evolutionary algorithm (EA) family, which works on a “survival of the fittest” mechanism. This paper presents the idea of implementing different genetic operators, modified for usage with the VRP, and performs experiments to determine the best combination of genetic operators for solving the VRP and to find optimal solutions for large-scale real-life examples of the VRP.

Natural language processing has been the subject of numerous studies in the last decade. These have focused on the various stages of text processing, from text preparation to vectorization to final text comprehension. The goal of vector space modeling is to project words in a language corpus into a vector space in such a way that words that are similar in meaning are close to each other. Currently, there are two commonly used approaches to the topic of vectorization. The first focuses on creating word vectors taking into account the entire linguistic context, while the second focuses on creating document vectors in the context of the linguistic corpus of the analyzed texts. The paper presents the comparison of different existing text vectorization methods in natural language processing, especially in Text Mining. The comparison of text vectorization methods is possible by checking the accuracy of classification; we used the methods NBC and k-NN, as they are some of the simplest methods. They were used for the classification in order to avoid the influence of the choice of the method itself on the final result. The conducted experiments provide a basis for further research for better automatic text analysis.

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