This thesis deals with the Travelling salesman problem with time windows. The problem is that the travelling salesman must pass through each defined location exactly once and finally return to the original place for the lowest possible price. The time windows in this problem are that each place can only be visited in a given time range, or it can happen that in a certain period of time there will be no path between some places. The thesis deals with an overview of this problem and problems similar to it. It also deals with the description of various methods by which this problem can be solved. As part of this thesis, an application in the Python programming language was also created, which is used to test selected methods for finding solutions. Finally, the given experiments are evaluated and the effectiveness of the given strategies is compared.

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A novel Ant Colony Optimization (ACO) framework for a dynamic environment has been proposed in this study. This algorithm was developed to solve Dynamic Traveling Salesman Problems more efficiently than the current algorithms. Adaptive Large Neighborhood Search based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes in literature to maintain diversity via transferring knowledge to the pheromone trails from previous environments. Numerical results indicate that the proposed algorithm can handle dynamicity in the environment more efficiently compared to other immigrant-based ACOs available in the literature. A real-life case study for wildlife surveillance by unmanned aerial vehicles has also been developed and solved using the proposed algorithm.

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Šiame darbe nagrinėjamas patobulintas tabu paieškos metodas, žinomas kaip iteratyvioji tabu paieška (ITP). Pasiūlytos kai kurios ITP metodo modifikacijos, besiremiančios tam tikromis sprendinių mutavimo (pertvarkymo) procedūromis (inversijos, įterpimai ir kt.), kurios įgalina pagerinti gaunamų sprendinių kokybę. Atlikti išsamūs sudaryto ITP algoritmo ir kitų pasiūlytų modifikacijų eksperimentiniai tyrimai, panaudojant testinius KU pavyzdžius iš KU testinių pavyzdžių bibliotekos TSPLIB. Gauti rezultatai patvirtina pasiūlytų modifikacijų pranašumą kitų ITP variantų atžvilgiu. / In this work, one of the heuristic algorithm – the iterated tabu search and its modifications are discussed. The work is organized as follows. Firstly, some basic definitions and preliminaries are given. Then, the iterated tabu search algoritm and its variants based on special type mutations are considered in more details. The ITS algorithms modifications were tested on the TSP instances from the TSP library TSPLIB. The results of this tests (experiments) are presented as well. The work is completed with the conclusions.

This diploma thesis deals with Ant Colony algorithms and their usage for solving Travelling Salesman Problems and Vehicle Routing Problems. These algorithms are metaheuristics offering new approach to solving NP-hard problems. Work begins with a description of the forementioned tasks including ways to tackle them. Next chapter analyses Ant Colony metaheuristic and its possible usage and variations. The most important part of the thesis is practical and is represented by application Ant Colony Optimization Framework. It is easily extensible application written in Java that is able to solve introduced problems. In conclusion this work presents analysis of solutions on test data.

Nowadays, when great emphasis is put on cost savings, transport optimization is necessary part of every company life in which transportation costs produce significant part. There are optimization methods and possibilities presented in this thesis. In the first chapter there are explained methods such as the travelling salesman problem, the vehicle routing problem, the multiple vehicle routing problem and the split delivery vehicle routing problem and then the reader gets to know the heuristics methods in the chapter two where description of the nearest neighbour method, Clarke-Wright method and split delivery heuristic is mentioned. In the last but one chapter author applies previous methods on concrete distribution arranged by Movianto Česká republika s.r.o. on 5th September, 2013. Based on gained outputs, analysis and comparison of results (including the original distribution) are provided in the fourth chapter. Obtained results of analysis lead to recommendation on how the company should plan its future distribution.

The thesis deals with application of models of routing problems on a real problem, beer distribution in Jesenicko region provided by Viden plus, a.s. The goal of the thesis is to optimize daily distributional routes for two vehicles with different capacities. Total distance has to be minimized respecting time and capacity limits. In case the optimal solution is not found, sub-optimal solution will be accepted. Solutions are calculated with use of the optimization software LINGO and some heuristic methods. Models of travelling salesman problem and vehicle routing problem are used to find the solutions. Results are compared to the current state of distributional routes in conclusion of the thesis.

This master's thesis is focused on evolutionary algorithms. The goal of this thesis is to chooche a proper algorithm which will solve a chosen problem. In this case the chosen algorithm is the genetic algorithm and the chosen problem is the travelling salesman problem. The result of this thesis will be implementation of the algorithm, finding the proper setup and lastly the measurment of the results for various input data.

Various types of transportation issues are a common practice. The issue may be approached mainly as the distribution of products from suppliers to consumers while minimising distribution costs. The difference of real transportation issues predominantly relates to the considered restrictions, such as capacities of vehicles and orders, time windows and other special distribution restrictions. Transportation issues were already defined by F.L. Hitchcock in 1941 and since then, a wide range of stochastic and non- determinist methods providing solutions to transportation issues have been developed. Nevertheless, introducing distribution restrictions in resolving real-life problems makes it difficult for such methods to be applied. This thesis provides a compilation of the well-known determinist methods that may be used to resolve transportation issues. The methods that are appropriate for resolving real issues are discussed in more detail. The solution procedure of the selected method is demonstrated using simple examples and the results are compared with the results of other methods. An analysis of the above methods is used to design and implement new methods to resolve real transportation issues, their results being compared with the methods provided by the commercial software product.