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.

Route optimization aims to optimize routes for vehicles withregards to resource usage. Especially when the vehicle needsto visit multiple customers on the route, a route optimizationtool is beneficiary. The purpose of this study is to comparehuman experience with a route optimization program. This isdone by comparing how a truck driver makes his routes to theroute a GIS-tool has calculated and then see which of theroutes was shorter, measured in kilometers. The data for thisstudy was gathered from a big shipping company. In order toachieve the purpose of this study 10 routes were analysed bya GIS program called ArcGIS. The algorithm used by ArcGISin route optimization is tabu search, this type of program wasused because it is based on heuristic methods that is muchfaster than exact methods. Expert systems are based onknowledge from experts that have been accumulated duringmany years of experience. Providing recommendations basedon probability reasoning instead of absolute answer. Thesekind of systems is often used in GIS programs to improveresults and calculation time. The aim of this study wasanalyze if a optimization program finds a better route than theexpert. This study shows an improvement of 60% of theanalyzed routes. To verify the results of this study anhypothesis test was made which gave a level of significanceby more than 85 %. The routes were optimized to a certainextent even before the study was done due to the driveralready being familiar with the routes in question. Because ofthis the results of this study were lower compared to othersimilar studies. Another reason may be that the coordinatesgiven to us did not always correspond perfectly with actuallocation of the stops. / Ruttoptimering avser att optimera rutter för fordon medminsta möjliga resursåtgång. När fordonet ska besöka ettflertal givna platser är ett ruttoptimeringsverktyg förmånligtatt använda. Denna studie syftar till att jämföra den mänskligaerfarenheten mot ett ruttoptimeringsprogram. Detta har gjortsgenom att jämföra hur en lastbilschaufför har kört en rutt mothur ett GIS-verktyg räknat fram den optimerade färdvägen avsamma rutt. Sedan jämfördes om det fanns skillnader ochvilken av rutterna som var kortast, räknat i kilometer. Datahar hämtats från ett stort fraktföretag. För att nå syftet har 10rutter undersökts i programmet ArcGIS Online som använderalgoritmen tabusökning. En kommersiell beräkningsmetodhar använts då det bygger på heuristiska metoder som ärbetydligt snabbare än exakta metoder. Expertsystem byggerpå erfarenhet som experter har samlat på sig genom åren, deger rekommendationer baserade på sannolikhetsresonemangistället för definitiva svar, dessa system sätts ofta in i GIS för att förbättra resultat och beräkningstider i systemen. Studienresulterade i en förbättring på 60 % av rutterna. Målet meddenna undersökning var att visa om ett optimeringsprogramhittar en bättre rutt än experten. För att verifiera resultaten istudien gjordes en hypotesprövning vilket gav ensignifikansnivå på över 85%. Chauffören har kört dessa rutteri flera år vilket gör att rutterna är optimerade i en viss månredan innan studien gjordes. Det har inverkat på resultatetsom gett ett lågt medelvärde av den procentuella skillnaden,jämfört med tidigare undersökningar. En annan faktor kanvara att koordinaterna i datan från företaget inte helt stämdemed den verkliga placeringen av stoppen på rutterna.

<p>Genetic algorithms is a method of optimization that can be used tosolve many different kinds of problems. This thesis focuses ondeveloping a framework for genetic algorithms that is capable ofsolving at least the two problems explored in the work. Otherproblems are supported by allowing user-made extensions.The purpose of this thesis is to explore the possibilities of geneticalgorithms for optimization problems and artificial intelligenceapplications.To test the framework two applications are developed that look attwo distinct problems, both of which aim at demonstrating differentparts. The first problem is the so called Travelling SalesmanProblem. The second problem is a kind of artificial life simulator,where two groups of creatures, designated predator and prey, aretrying to survive.The application for the Travelling Salesman Problem measures theperformance of the framework by solving such problems usingdifferent settings. The creature simulator on the other hand is apractical application of a different aspect of the framework, wherethe results are compared against predefined data. The purpose is tosee whether the framework can be used to create useful data forthe creatures.The work showed how important a detailed design is. When thework began on the demonstration applications, things were noticedthat needed changing inside the framework. This led to redesigningparts of the framework to support the missing details. A conclusionfrom this is that being more thorough in the planning, andconsidering the possible use cases could have helped avoid thissituation.The results from the simulations showed that the framework iscapable of solving the specified problems, but the performance isnot the best. The framework can be used to solve arbitrary problemsby user-created extensions quite easily.</p>

In an ever-changing and demanding world where short assembly and innovation times are indispensable, it is of paramount importance to ensure that the machinery used throughout the whole process of a product are in their best possible condition. This guarantees that the performance of each machine will be optimal, and hence, the process times will be the shortest possible, while the best quality products are obtained. Moreover, having a machine in an impeccable status permits making the necessary changes to it, in order to fulfil the requirements that a more advanced or complex product may have. Maintenance operations and their corresponding trainings have historically been time-consuming, and a vast amount of information has been transmitted from an expert to a newer operator. This means that there has been the need of working with experienced operators to secure that a good service is provided. However, different technologies like augmented reality (AR) have been shown to have a positive impact in the support and monitoring of operators in industrial maintenance operations.The present project gathers information in regard to the framework of AR, with the aim of supporting and monitoring operators in industrial environments. The proposed method consists on the development of an artefact, which would lead to a possible improvement of the already existing solutions. It is believed that the development of an AR application could grant the necessary aid to any operator in maintenance operations. The result of this suggestion is an AR application which superimposes visual information on the physical equipment.

Made available in DSpace on 2014-12-17T14:54:52Z (GMT). No. of bitstreams: 1 FranciscoCLJ.pdf: 1181019 bytes, checksum: b3894e0c93f85d3cf920c7015daef964 (MD5) Previous issue date: 2009-03-20 / Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process / T?cnicas de otimiza??o conhecidas como metaheur?sticas t?m obtido sucesso na resolu??o de problemas classificados como NP - ?rduos. Estes m?todos utilizam abordagens n?o determin?sticas que geram solu??es pr?ximas do ?timo sem, no entanto, garantir a determina??o do ?timo global. Al?m das dificuldades inerentes ? complexidade que caracteriza os problemas NP-?rduos, as metaheur?sticas enfrentam ainda o dilema de explora??o/explota??o, que consiste em escolher entre intensifica??o da busca em uma regi?o espec?fica e a explora??o mais ampla do espa?o de solu??es. Uma forma de orientar tais algoritmos em busca de melhores solu??es ? supri-los de maior conhecimento do problema atrav?s da utiliza??o de um agente inteligente, capaz de reconhecer regi?es promissoras e/ou identificar em que momento dever? diversificar a dire??o de busca, isto pode ser feito atrav?s da aplica??o de Aprendizagem por Refor?o. Neste contexto, este trabalho prop?e o uso de uma t?cnica de Aprendizagem por Refor?o - especificamente o Algoritmo Q-learning - como uma estrat?gia de explora??o/explota??o para as metaheur?sticas GRASP (Greedy Randomized Adaptive Search Procedure) e Algoritmo Gen?tico. Na implementa??o da metaheur?stica GRASP proposta, utilizou-se o Q-learning em substitui??o ao algoritmo guloso-aleat?rio tradicionalmente usado na fase de constru??o. Tal substitui??o teve como objetivo melhorar a qualidade das solu??es iniciais que ser?o utilizadas na fase de busca local do GRASP, e, ao mesmo tempo, suprir esta metaheur?sticas de um mecanismo de mem?ria adaptativa que permita a reutiliza??o de boas decis?es tomadas em itera??es passadas e que evite a repeti??o de decis?es n?o promissoras. No Algoritmo Gen?tico, o algoritmo Q-learning foi utilizado para gerar uma popula??o inicial de alta aptid?o, e ap?s um determinado n?mero de gera??es, caso a taxa de diversidade da popula??o seja menor do que um determinado limite L, ele ? tamb?m utilizado em uma forma alternativa de operador de cruzamento. Outra modifica??o importante no algoritmo gen?tico h?brido ? a proposta de um processo de intera??o mutuamente cooperativa entre o os operadores gen?ticos e o Algoritmo Q-learning. Neste processo interativo/cooperativo o algoritmo Q-learning recebe uma atualiza??o adicional na matriz dos Q-valores com base na solu??o elite da popula??o corrente. Os experimentos computacionais apresentados neste trabalho consistem em comparar os resultados obtidos com a implementa??o de vers?es tradicionais das metaheur?sticas citadas, com aqueles obtidos utilizando os m?todos h?bridos propostos. Ambos os algoritmos foram aplicados com sucesso ao problema do caixeiro viajante sim?trico, que por sua vez, foi modelado como um processo de decis?o de Markov

Genetic algorithms is a method of optimization that can be used tosolve many different kinds of problems. This thesis focuses ondeveloping a framework for genetic algorithms that is capable ofsolving at least the two problems explored in the work. Otherproblems are supported by allowing user-made extensions.The purpose of this thesis is to explore the possibilities of geneticalgorithms for optimization problems and artificial intelligenceapplications.To test the framework two applications are developed that look attwo distinct problems, both of which aim at demonstrating differentparts. The first problem is the so called Travelling SalesmanProblem. The second problem is a kind of artificial life simulator,where two groups of creatures, designated predator and prey, aretrying to survive.The application for the Travelling Salesman Problem measures theperformance of the framework by solving such problems usingdifferent settings. The creature simulator on the other hand is apractical application of a different aspect of the framework, wherethe results are compared against predefined data. The purpose is tosee whether the framework can be used to create useful data forthe creatures.The work showed how important a detailed design is. When thework began on the demonstration applications, things were noticedthat needed changing inside the framework. This led to redesigningparts of the framework to support the missing details. A conclusionfrom this is that being more thorough in the planning, andconsidering the possible use cases could have helped avoid thissituation.The results from the simulations showed that the framework iscapable of solving the specified problems, but the performance isnot the best. The framework can be used to solve arbitrary problemsby user-created extensions quite easily.

Die Tourenbildung beschäftigt sich mit der Konstruktion kostengünstiger Transportrouten zur Belieferung von Verbrauchern. Sie ist eine der weitreichensten Erfolgsgeschichten des Operations Research. Das starke Interesse an diesen Problemen durch Industrie und Forschung liegt zum einen am wirtschaftlichen Potenzial der Tourenbildung und -optimierung, zum anderen macht ihr Reichtum an Struktur sie zu einem faszinierenden Forschungsgebiet. In der vorliegenden Arbeit soll ein Überblick über einige, u. a. auch neuere mathematische Modell- und Lösungsansätze gegeben werden. Auf Grund der hohen Anzahl der Veröffentlichungen auf diesem Gebiet wird nicht zwingend ein Anspruch auf die vollständige Darlegung aller möglichen Problemstellungen im Zusammenhang mit dem TSP sowie dem VRP und deren Lösungsansätze erhoben. An den gegebenen Stellen wird statt dessen auf weiterführende Literatur verwiesen.

Autonomous underwater vehicles have become a popular countermeasure to naval mines. Saab’s AUV62-MR detects, locates and identifies mine-like objects through three phases. By extracting functionality from the AUV62-MR and placing it on a second vehicle, it is suggested that the second and third phases can be performed in parallel. This thesis investigates how to design the second vehicle so that the runtime of the mine reconnaissance process is minimized. A simulation framework is implemented to simulate the second and third phases of the mine reconnaissance process in order to test various design choices. The vehicle design choices in focus are the size and the route planning of the second vehicle. The route-planning algorithms investigated in this thesis are a nearest neighbour algorithm, a simulated annealing algorithm, an alternating algorithm, a genetic algorithm and a proposed Dubins simulated annealing algorithm. The algorithms are evaluated both in a static environment and in the simulation framework. Two different vehicle sizes are investigated, a small and a large, by evaluating their performances in the simulation framework. This thesis takes into account the limited travelling distance of the vehicle and implements a k-means clustering algorithm to help the route planner determine which mine-like objects can be scanned without exceeding the distance limit. The simulation framework is also used to evaluate whether parallel execution of the second and third phases outperforms the current sequential execution. The performance evaluation shows that a major reduction in runtime can be gained by performing the two phases in parallel. The Dubins simulated annealing algorithm on average produces the shortest paths and is considered the preferred route-planning algorithm according to the performance evaluation. It also indicates that a small vehicle size results in a reduced runtime compared to a larger vehicle.

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