This thesis deals with the optimization of distribution of products to consumers based on a real case study of a particular company from Opava. For this purpose, a mathematical optimization model is used to illustrate the vehicle routing problem. The study will also offer an explanation on the relevancy of heuristic methods, mainly with respect to their application in solving real life situations analogous to the one surveyed. On the basis of chosen heuristic methods (i.e. the nearest neighbour algorithm and the savings algorithm) and having taken into account the restricting conditions of the company, four algorithms were designed. These four algorithms are programmed in Visual Basic for Applications MS Excel 2007. They are aimed at solving the real problems with the distribution of ordered products that the particular company must deal with. The thesis compares the results provided by an employee of this company, and the results presented by the designed algorithms.

The diploma thesis deals with the use of heuristic methods for solving messenger problems. The first part focuses on the proposed vehicle routing problems, especially on travelling salesman problems and the messenger problems. For individual problems are theoretically discussed mathematical models for variants with one or more vehicles and the expansion of the use of time windows and capacity constraints. The second part introduces heuristic algorithms for the method of nearest neighbour, savings method, insertion method and the exchange method on the travelling salesman problems. Then all of the mentioned algorithms are modified for use in three variants of messenger problem. First, the disposition of one vehicle, the second allows more messengers in the same starting point and the last option is more messengers at different starting points. The last part describes computational experiments and comparison of results provided by different methods. The diploma thesis includes application for solving messenger problems using savings methods programmed in Visual Basic for Application in MS Excel.

At the basic there is a connection between operation research and automotive logistics. The supply chain in this type of industry is hard to coordinate and flexibility is considered as a main key indicator. Companies increase their competitiveness through the optimization of supply chain and try to take control of every flow or a process. This idea goes throughout the departments. The main concept, which is about a connection of whole supply chain with information flow means, that there isn't need of stock and every input is highly available on time and in requested quantity and quality. This thesis describes inbound logistics and introduces some of the methods used for planning vehicle routes. In our situation which is based on car maker data set was chosen a group of a suppliers for the analysis. We used vehicle routing problem with some modifications, heuristics based on the covering problem. Founded solution was compared with the present state.

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 thesis deals with the optimization of distribution of bakery products. Firstly there are the fundamental types of vehicle routing problems and their optimization models introduced. Next part is dedicated to heuristic algorithms. The heuristic methods are introduced in general, then there are the chosen methods described. Later there are two chosen algorithms formulated. First one based on the nearest neighbour method and another one based on the savings algorithm. Both of algorithms were programmed in the Visual Basic of Applications MS Excel 2010. These algorithms were applied for the solution of the real problem dealing with the distribution of goods. The bakery company has provided the data about its customers for this purpose. The last part of this thesis is dedicated to the summary and comparison of the solution of the assigned problem that was gained by the proposed algorithms with the solution that the bakery company has put into practice.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2015-07-17T16:00:19Z No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) / Made available in DSpace on 2015-07-17T16:00:19Z (GMT). No. of bitstreams: 1 Stanley Jefferson de Araujo Lima.pdf: 1500605 bytes, checksum: 2aec7d5c11c9781ce7f70eb2019c01f4 (MD5) Previous issue date: 2015-01-27 / In recent years, the Vehicle Routing Problem (VRP) has attracted an increasing attention from researchers due to the great difficulty of its solution and its presence in various practical situations. As consequence, there has been great effort to develop more robust, agile and flexible algorithms that can be modeled according to the scenario that describes the problem. The Capacitated Vehicle Routing Problem (CVRP) is a version of VRP and consists in determining a set of routes to be followed by a fleet of homogeneous vehicles, which must serve a set of customers. The objective is to minimize the total cost of the routes subject to the following restrictions: i) routes must start and end in the same distribution center; ii) each customer must be visited once and its demand must be met in full by only one vehicle and iii) the sum of customers' demands included in a route cannot exceed the vehicle capacity. The CVRP belongs to the class of NP-hard problems, that is, problems whose the solution usually requires non-polynomial complexity time algorithms and because of this are usually resolved with the use of heuristic and metaheuristics algorithms. In this work, it was investigated the optimization of CVRP using Genetic Algorithm (GA) with alternative chromosome representations and heuristics. To this end, three strategies, each one employing a different model of chromosome representation for encoding solution in AG were proposed. In addition, the heuristics of Gillett and Miller to generate solutions that are included in the initial population of GA and Hill-climbing for refinement of GA solutions, after a number of generations without improvement, were adopted. In the performed experiments, the results obtained by the proposed strategies were compared with each other and also with the best results found in the literature for a set of known instances. These experiments showed that the proposed strategies provided good results with respect to quality of solutions well as the computational cost. In addition, it was possible to evaluate the viability of each employed chromosome representation and the contribution of the heuristics in the convergence process of GA. / Nos últimos anos o Problema de Roteamento de Veículos (PRV) tem atraído cada vez mais a atenção de pesquisadores devido à grande dificuldade de solução e sua presença em várias situações do cotidiano. Em decorrência disso, tem havido um grande esforço para desenvolver algoritmos cada vez mais robustos, ágeis e flexíveis e que possam ser modelados com base no cenário que descreve o problema. O Problema de Roteamento de Veículos Capacitado (PRVC) é uma versão do PRV e consiste em encontrar um conjunto de rotas a serem seguidas por uma frota de veículos homogêneos, os quais devem atender a um conjunto de clientes. O objetivo é minimizar o custo total das rotas respeitando as seguintes restrições: i) as rotas devem iniciar e terminar no mesmo centro de distribuição; ii) cada cliente deve ser visitado uma única vez e sua demanda deve ser atendida integralmente por apenas um veículo e iii) a soma das demandas dos clientes incluídos em uma rota não pode exceder a capacidade do veículo. Problemas desta natureza podem ser classificados como NP-Hard, ou seja, possuem ordem de complexidade não polinomial e normalmente são resolvidos com uso de algoritmos heurísticos e meta-heurísticos. Neste trabalho investigou-se a otimização do PRVC usando Algoritmo Genético (AG) com representações cromossômicas e heurísticas alternativas. Para tanto, foram propostas três estratégias, cada uma delas empregando um modelo diferente de representação cromossômica para codificação da solução no AG. Além disso, foram empregadas as heurísticas de Gillett e Miller para gerar soluções que são incluídas na população inicial do AG e Subida/Descida de Encosta para refinamento das soluções, após um certo número de gerações sem melhoria. Nos experimentos realizados, os resultados obtidos pelas estratégias propostas foram comparados entre si e também com os melhores resultados encontrados na literatura para um conjunto de instâncias conhecidas. Pode-se constatar, a partir desses experimentos, que as estratégias apresentaram bons resultados tanto no que tange a qualidade das soluções quanto ao tempo computacional dispendido. Em adição, foi possível avaliar a viabilidade de cada uma das representações cromossômicas empregadas, além da contribuição das heurísticas no processo de convergência do ag.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2017-08-04T21:26:50Z No. of bitstreams: 1 Renato Alessandro Rocha Santos.PDF: 5054326 bytes, checksum: 739f1115bab9b157f48eb56ca44d4c34 (MD5) / Made available in DSpace on 2017-08-04T21:26:50Z (GMT). No. of bitstreams: 1 Renato Alessandro Rocha Santos.PDF: 5054326 bytes, checksum: 739f1115bab9b157f48eb56ca44d4c34 (MD5) Previous issue date: 2017-03-02 / Over the last decades the Vehicle Routing Problem (VRP) has been subject of research of several authors, mainly because of difficulties found in its optimization as well as its application in real-world situations. In the scientific literature there are several proposed solutions for the PRV using different optimization techniques. However, such solutions are rarely transformed into software tools that can be used by end users, for example, micro enterprises. Thus, the focus of the present work was to develop a Web computational tool for vehicle routing, called “SGRV  Sistema de Gestão de Roteamento de Veículos”, which uses Google Maps features and aims to meet the needs of micro enterprises. Therefore, initially it was conducted a literature search about solution methods for VRP, to support the choice of strategy based on genetic algorithms employed in SGRV. Then, a new bibliographic research was made with the purpose of finding free softwares for the VRP solution, which were object of analysis to mark out the development of the SGRV. For the development of this research was used the methodology Design Science Research. A qualitative evaluation of the SGRV was carried out by four microenterprises from different branches, that used the tool for a certain period and, in the end, answered six questions opened from semi-structured interviews. The experiences of these microenterprises were transcribed and the data obtained reveal the effectiveness of the SGRV in the management of its tasks related to the orders and deliveries of products. / Nas últimas décadas o Problema de Roteamento de Veículos (PRV) tem sido temática de pesquisas de diversos autores, principalmente por causa de dificuldades encontradas para sua resolução, bem como sua aplicabilidade em situações reais do cotidiano. Na literatura científica há diversas propostas de soluções para o PRV empregando diferentes técnicas de otimização. No entanto, tais soluções raramente são transformadas em ferramentas computacionais que possam ser utilizadas por usuários finais como, por exemplo, as microempresas. Assim, o foco do presente trabalho foi desenvolver uma ferramenta computacional Web para roteamento de veículos, denominada SGRV  Sistema de Gestão de Roteamento de Veículos, que emprega recursos do Google Maps e visa suprir as necessidades de microempresas. Para tanto, inicialmente realizou-se uma pesquisa bibliográfica acerca de métodos de solução para o PRV, a fim de subsidiar a escolha de uma estratégia baseada em Algoritmos Genéticos empregada no SGRV. Em seguida, foi feito um novo levantamento bibliográfico com intuito de encontrar softwares de uso livre para a solução do PRV, os quais foram objeto de uma análise que visou balizar a implementação do SGRV. Para o desenvolvimento dessa pesquisa foi empregada a metodologia Design Science Research. Uma avaliação qualitativa do SGRV foi realizada por quatro microempresas de diferentes ramos, as quais utilizaram a ferramenta por um determinado período e, ao final, responderam seis questões abertas a partir de entrevistas semiestruturadas. As experiências dessas microempresas foram transcritas e os dados obtidos revelam a efetividade do SGRV no gerenciamento de suas tarefas relacionadas aos pedidos e entregas de produtos.

Uma hiper-heurística é uma heurística que pode ser utilizada para lidar com qualquer problema de otimização, desde que a ela sejam fornecidos alguns parâmetros, como estruturas e abstrações, relacionados ao problema considerado. As hiper-heurísticas têm sido aplicadas a alguns problemas práticos e apresentadas como métodos de grande potencial, no que diz respeito à capacidade de possibilitar o desenvolvimento, em tempo bastante reduzido, de algoritmos capazes de lidar satisfatoriamente, do ponto de vista prático, com problemas de otimização complexos e pouco conhecidos. No entanto, é difícil situar as hiper-heurísticas em algum nível de qualidade e avaliar a robustez dessas abordagens caso não as apliquemos a problemas para os quais existam diversas instâncias disponíveis publicamente e já experimentadas por algoritmos relevantes. Este trabalho procura dar alguns passos importantes rumo a essas avaliações, além de ampliar o conjunto das hiper-heurísticas, compreender o impacto de algumas alternativas naturais de desenvolvimento e estabelecer comparações entre os resultados obtidos por diferentes métodos, o que ainda nos permite confrontar as duas diferentes classes de hiper-heurísticas que identificamos. Com essas finalidades em mente, desenvolvemos 3 novas hiper-heurísticas e implementamos 2 das hiper-heurísticas mais importantes criadas por outros autores. Para estas últimas, experimentamos ainda algumas extensões e modificações. Os dois métodos hiper-heurísticos selecionados podem ser vistos como respectivos representantes de duas classes distintas, que aparentemente englobam todas as hiper-heurísticas já desenvolvidas e nos permitem denominar cada um desses métodos como \"hiper-heurística de busca direta por entornos\" ou como \"hiper-heurística evolutiva indireta\". Implementamos cada hiper-heurística como uma biblioteca (em linguagem C), de forma a evidenciar e estimular a independência entre o nível em que se encontra a hiper-heurística e aquele onde se apresentam as estruturas e abstrações diretamente relacionadas ao problema considerado. Naturalmente, essa separação é de ingente importância para possibilitar a reutilização imediata das hiper-heurísticas e garantir que nelas haja total ausência de informações relativas a um problema de otimização específico. / A hyperheuristic is a heuristic that can be used to handle any optimization problem, provided that the algorithm is fed with some parameters, as structures and abstractions, related to the problem at hand. Hyperheuristics have been applied to some practical problems and presented as methods with great potential to allow the quick development of algorithms that are able to successfully deal, from a practical standpoint, with complex ill-known optimization problems. However, it\'s difficult to position hyperheuristics at some quality level and evaluate their robustness without applying them to problems for which there are many instances available in the public domain and already attacked by worthy algorithms. This work aims to give some important steps towards that process of evaluation, additionally increasing the number of available hyperheuristics, studying the impact of some natural development alternatives and comparing the results obtained by different methods, what also enables us to confront the two classes of hyperheuristics that we have identified. With those purposes in mind, we have developed 3 original hyperheuristics and implemented 2 of the most important hyperheuristics created by other authors. For those latter two approaches, we have also experimented with some modifications and extensions. The two methods we have chosen for implementation may be seen as respectively representing two distinct classes, which seem to contain all hyperheuristics developed so far and that allow us to classify any of these methods as either being a \"direct neighbourhood search hyperheuristic\" or an \"indirect evolutive hyperheuristic\". We have implemented each hyperheuristic as a library (in the C language), so as to clearly show and estimulate the independence between the level where the hyperheuristic is and that to which the structures and abstractions directly related to the problem at hand belong. Obviously, this separation of concerns is extremely important to make the immediate reuse of hyperheuristics possible and enforce in them the complete absence of information from a specific optimization problem.

In this paper, we study a rich vehicle routing problem incorporating various complexities found in real-life applications. The General Vehicle Routing Problem (GVRP) is a combined load acceptance and generalised vehicle routing problem. Among the real-life requirements are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, and route restrictions for vehicles. The GVRP is highly constrained and the search space is likely to contain many solutions such that it is impossible to go from one solution to another using a single neighbourhood structure. Therefore, we propose iterative improvement approaches based on the idea of changing the neighbourhood structure during the search.