Internet of Things (IoT) devices are increasingly being used everywhere, from the factory to the hospital to the house to the car. IoT devices typically have limited processing resources, so they must rely on cloud servers to accomplish their tasks. Thus, many obstacles need to be overcome while offloading tasks to the cloud. In reality, an excessive amount of data must be transferred between IoT devices and the cloud, resulting in issues such as slow processing, high latency, and limited bandwidth. As a result, the concept of edge computing was developed to place compute nodes closer to the end users. Because of the limited resources available at the edge nodes, when it comes to meeting the needs of IoT devices, tasks must be optimally scheduled between IoT devices, edge nodes, and cloud nodes.  In this thesis, we model the offloading problem in an edge cloud infrastructure as a Mixed-Integer Linear Programming (MILP) problem and look for efficient optimization techniques to tackle it, aiming to minimize the total delay of the system after completing all tasks of all services requested by all users. To accomplish this, we use the exact approaches like simplex to find a solution to the MILP problem. Due to the fact that precise techniques, such as simplex, require a large number of processing resources and a considerable amount of time to solve the problem, we propose several heuristics and meta-heuristics methods to solve the problem and use the simplex findings as a benchmark to evaluate these methods. Heuristics are quick and generate workable solutions in certain circumstances, but they cannot guarantee optimal results. Meta-heuristics are slower than heuristics and may require more computations, but they are more generic and capable of handling a variety of problems. In order to solve this issue, we propose two meta-heuristic approaches, one based on a genetic algorithm and the other on simulated annealing. Compared to heuristics algorithms, the genetic algorithm-based method yields a more accurate solution, but it requires more time and resources to solve the MILP, while the simulated annealing-based method is a better fit for the problem since it produces more accurate solutions in less time than the genetics-based method. / Internet of Things (IoT) devices are increasingly being used everywhere. IoT devices typically have limited processing resources, so they must rely on cloud servers to accomplish their tasks. In reality, an excessive amount of data must be transferred between IoT devices and the cloud, resulting in issues such as slow processing, high latency, and limited bandwidth. As a result, the concept of edge computing was developed to place compute nodes closer to the end users. Because of the limited resources available at the edge nodes, when it comes to meeting the needs of IoT devices, tasks must be optimally scheduled between IoT devices, edge nodes, and cloud nodes.  In this thesis, the offloading problem in an edge cloud infrastructure is modeled as a Mixed-Integer Linear Programming (MILP) problem, and efficient optimization techniques seeking to minimize the total delay of the system are employed to address it. To accomplish this, the exact approaches are used to find a solution to the MILP problem. Due to the fact that precise techniques require a large number of processing resources and a considerable amount of time to solve the problem, several heuristics and meta-heuristics methods are proposed. Heuristics are quick and generate workable solutions in certain circumstances, but they cannot guarantee optimal results while meta-heuristics are slower than heuristics and may require more computations, but they are more generic and capable of handling a variety of problems.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24219号 / 工博第5047号 / 新制||工||1788(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 教授 山田 忠史, 准教授 QURESHI Ali Gul / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

The Unit Commitment Problem (UCP) poses a significant challenge in optimizing powergeneration schedules within complex and dynamic energy systems. This study explores theapplication of Genetic Algorithms (GAs) as a promising approach to address UCP, their ability tonavigate complex solution spaces and adapt to changing operational conditions. The work provides a broad exploration of their effectiveness, challenges, and future prospects. The objective of UCP is to efficiently optimize power generation schedules within complex energy systems, seeking cost-effective and reliable solutions while accommodating various operational constraints. Various encoding techniques and GA operations are implemented and evaluated incomparison to the solutions obtained from a commercial Mixed-Integer Linear Programming (MILP) solver. The key findings point to the potential for achieving high quality solutions and robustness in the application of these techniques. However, it is important to acknowledge and address challenges such as encoding complexity, extensive computation times, the risk of premature convergence, and the complications of handling complex constraints that continue to exist in this domain. The future scope lies in hybrid approaches, scalability enhancement and incorporation of multi-objective optimization, offering unrealized potential for the efficient andsustainable operation of modern energy systems.

This thesis addresses the Vehicle Routing Problem with Interdiction (VRPI), an extension of the classic Vehicle Routing Problem (VRP) that incorporates the risk of route interdiction due to events such as natural disasters, armed conflicts, and infrastructural failures, among others. These interdictions introduce uncertainty and complexity into logistics planning, requiring innovative approaches to the routing process. This research employs both exact methods, using the CPLEX solver, and heuristic methods, particularly using the Greedy Randomized Adaptive Search Procedure (GRASP), to solve VRPI with different instance sizes. This research’s key contributions include successfully implementing the GRASP algorithm on large-scale benchmark instances, representing a significant advancement over prior implementations that focused on smaller, randomly generated instances. A flexible framework was also developed to adapt the GRASP methodology for different VRP variants, including the Capacitated Vehicle Routing Problem (CVRP) and Split Delivery Vehicle Routing Problem (SDVRP), with and without interdiction. A feasibility analysis for small instances was developed using CPLEX, highlighting the sensitivity of VRPI solutions to interdiction probabilities, particularly in scenarios with tight capacity constraints. The findings of this analysis are extended to large instances. Additionally, a 3-fold logic was incorporated in the GRASP implementation—focused on minimizing cost, minimizing interdiction, and minimizing demand—proved crit- ical in facing the VRPI challenges, and provided high-quality solutions with reduced computational effort. Including the minimum demand logic in GRASP was instrumen- tal during the implementation and numerical experimentation for large benchmark in- stances. The implications of this thesis are significant for operational research (OR), particularly in high-risk environments where route interdictions can occur. Future research directions include generating more diverse benchmark instances for VRPI, exploring the impact of variability in interdiction probabilities on solution quality and computational time, and applying exact methods like dynamic programming to solve large VRP instances. / Thesis / Master of Science (MSc)

Orientador: Ruben Augusto Romero Lazaro / Resumo: Este trabalho apresenta uma proposta baseada na meta-heurística Busca Tabu, chamada de Busca Tabu Reformulada para resolver o problema de operação ótima dos sistemas de distribuição, utilizando uma estratégia integrada de reconfiguração e alocação de bancos de capacitores fixos e chaveados para obter a topologia radial que apresente o menor custo de operação. Para encontrar a topologia radial inicial foi aplicado o algoritmo de Prim, em que foi obtida uma solução reconfigurada, e essa solução encontrada foi submetida à uma heurística para alocação de capacitores fixos e chaveados. A proposta de solução inicial é submetida ao algoritmo de Busca Tabu Reformulada que utiliza uma vizinhança que considera como solução vizinha uma topologia vizinha da topologia radial corrente e com a proposta de alocação de bancos de capacitores modificada. Como proposta da metodologia Busca Tabu Reformulada o procedimento é repetido até um critério de parada definido. Todos os programas foram escritos em linguagem FORTRAN 77. Os algoritmos propostos foram testados com os sistemas de 33, 70, 84 e 136 barras. / Abstract: This paper presents a proposal based on the Tabu Search metaheuristic called Tabu Search Reformulated to solve the problem of optimal operation of the distribution systems, using an integrated strategy of reconfiguration and allocation of fixed and switched capacitor banks to obtain the radial topology which presents the lowest operating cost. To find the initial radial topology the Prim algorithm was applied, in which a reconfigured solution was obtained, and this solution was submitted to a heuristic for the allocation of fixed and switched capacitors. The initial solution proposal is submitted to the Reformulated Tabu Search algorithm that uses a neighborhood that considers as neighbor solution a neighboring topology of the current radial topology and with the proposed allocation of modified capacitor banks. As a proposal of the Tabu Search Reformulated methodology, the procedure is repeated up to a defined stop criterion. All the programs were written in FORTRAN 77 language. The proposed algorithms were tested with the 33, 70, 84 and 136-node systems. / Mestre

[pt] Nesta tese são propostos modelos e algoritmos aproximados para o Problema da Árvore Geradora de Custo Mínimo com Restrição de Diâmetro (AGMD). Este problema modela tipicamente aplicações em projetos de redes de computadores onde todos os vértices devem comunicar-se entre si a um custo mínimo, garantindo um certo nível de serviço. Os modelos propostos por Achuthan e Caccetta para o AGMD são reforçados através da introdução de restrições válidas. Uma relaxação lagrangeana é proposta para o modelo de multifluxo básico de Gouveia e Magnanti. Essa relaxação é utilizada para o desenvolvimento de heurísticas lagrangeanas. Adaptações são realizadas nas heurísticas construtivas propostas por Deo e Abdalla, e por Raidl e Julstrom. São propostas ainda quatro estratégias de busca local, uma heurística do tipo GRASP e outra híbrida. São obtidos limites superiores a menos de 2% do ótimo para as classes de instâncias usadas nos trabalhos de Gouveia e Magnanti, e de Santos, Lucena e Ribeiro. Além disto, obteve-se os melhores resultados conhecidos até o presente momento para 11 instâncias de grafos completos usadas por Raidl, Julstrom e Gruber. / [en] In this work, models and approximation algorithms to solve the Diameter Constrained Minimum Spanning Tree Problem (AGMD) are proposed. This problem typically models network design applications where all vertices must communicate with each other at a minimum cost, while meeting a given quality requirement. The formulations proposed by Achuthan and Caccetta are strengthened with valid inequalities. A lagrangean relaxation is proposed for the multicommodity flow model developed by Gouveia and Magnanti. Adaptations are made in the constructive heuristics proposed by Deo and Abdalla and by Raidl and Julstrom. Four local search procedures, a GRASP algorithm and a hybrid heuristic are proposed. Upper bounds within 2% of the optimal solution values are obtained for the two classes of instances used by Gouveia and Magnanti and by Santos, Lucena and Ribeiro. Moreover, stronger upper bounds are reported for 11 instances in complete graphs used by Raidl, Julstrom and Gruber

O problema do caixeiro viajante (PCV) é um problema clássico de otimização que possui diversas variações, aplicações e instâncias. Encontrar a solução ótima para muitas instâncias desse problema é geralmente muito difícil devido o alto custo computacional. Vários métodos de otimização, conhecidos como meta-heurísticas (MHs), são capazes de encontrar boas soluções para o PCV. Muitos algoritmos baseados em diversas MHs têm sido propostos e investigados para diferentes variações do PCV. Como não existe um algoritmo universal que encontre a melhor solução para todas as instâncias de um problema, diferentes MHs podem prover a melhor solução para diferentes instâncias do PCV. Desse modo, a seleção a priori da MH que produza a melhor solução para uma dada instância é uma tarefa difícil. A pesquisa desenvolvida nesta tese investiga o uso de abordagens de meta-aprendizado para selecionar as MHs mais promissoras para novas instâncias de PCV. Essas abordagens induzem meta-modelos preditivos a partir do treinamento das técnicas de aprendizado de máquina em um conjunto de meta-dados. Cada meta-exemplo, em nosso conjunto de meta-dados, representa uma instância de PCV descrita por características (meta-atributos) do PCV e pelo desempenho das MHs (meta-atributo alvo) para essa instância. Os meta-modelos induzidos são usados para indicar os valores do meta-atributo alvo para novas instâncias do PCV. Vários experimentos foram realizados durante a investigação desta pesquisa e resultados importantes foram obtidos / The traveling salesman problem (TSP) is a classical optimization problem that has several variations, applications and instances. To find the optimal solution for many instances of this problem is usually a very hard task due to high computational cost. Various optimization methods, known as metaheuristics (MHs), are capable to generate good solutions for the TSP. Many algorithms based on different MHs have been proposed and investigated for different variations of the TSP. Different MHs can provide the best optimization solution for different TSP instances, since there is no a universal algorithm able to find the best solution for all instances. Thus, a priori selection of the MH that produces the best solution for a given instance is a hard task. The research developed in this thesis investigates the use of meta-learning approaches to select the most promising MHs for new TSP instances. These approaches induce predictive meta-models from the training of machine learning techniques on a set of meta-data. In our meta-data, each meta-example is a TSP instance described by problem characteristics (meta-features) and performance of MHs (target meta-features) for this instance. The induced meta-models are used to indicate the values of the target meta-feature for new TSP instances. During the investigation of this research, several experiments were performed and important results were obtained

Esta tese de doutorado apresenta modelos matemáticos de problemas de dimensionamento de lotes para planejamento da produção na indústria de embalagens de vidro, que são essenciais em qualquer cadeia de produção, pois são responsáveis por proteger e conservar os produtos (alimentos e bebidas). Na literatura científica são raros os trabalhos que abordam estudos sobre problemas combinados de dimensionamento de lotes e planejamentos da produção em indústrias de embalagens de vidro. Com a finalidade de preencher esta lacuna, a presente tese tem por objetivo propor modelos inéditos e métodos de resolução aplicáveis em problemas nas indústrias de embalagens de vidro. Dessa forma, propõem-se dois modelos baseados em problemas reais para a construção ou reforma de forno(s), denominados de Problema de Instalação de um Novo Forno e Problema de Instalação de Múltiplos Fornos, que verificam a capacidade de fusão e as configurações das máquinas instaladas. Outros dois modelos são desenvolvidos a partir de estudos de casos referentes ao planejamento e ao controle da produção de ampolas de garrafas térmicas. No primeiro estudo, considera-se o máximo da produção líquida e no segundo, minimiza-se os set-up, sendo que em ambos os casos a realidade de uma fábrica é refletida. A complexidade desses modelos contribui para o uso de métodos heurísticos e meta-heurísticos como técnicas para resolução dos mesmos. No entanto, considera-se também a avaliação da associação desses métodos ao uso de programação matemática. Para isso, modelos matemáticos são propostos dentro do contexto das indústrias consideradas. Desta forma, uma heurística de Filtro Guloso e as meta-heurísticas como o Algoritmo Genético Simples, o Algoritmo Genético Multi-Populacional e o Algoritmo Genético Modificado são utilizados na determinação das variáveis inteiras presentes nos modelos matemáticos. Além disso, utiliza-se um método exato, por meio da ferramenta CPLEX, para determinar as variáveis contínuas. Os estudos são conduzidos a partir de dados fornecidos por indústrias localizadas no Brasil e em Portugal. Portanto, os resultados colaboram com o estado da arte nessa área de pesquisa e com o processo de fabricação industrial de embalagens de vidro. / This doctoral dissertation presents mathematical models of lot-sizing problems for production planning in the glass containers industry, which are essential in any production chain, as they are responsible for protecting and conserving products (food and beverages). In the scientific literature, studies addressing combined problems of batch sizing and production planning in glass containers industries are rare. In order to fill this gap, this thesis aims to propose novel models and resolution methods applicable to problems in the glass containers industry. Thus, we propose two models based on real problems for the construction or remodelling of the furnace (s), called New Furnace Installation Problem and Multiple Furnace Installation Problem, which verify fusibility and configurations of installed machines. We developed two other models from case studies regarding the planning and control of the production of thermos vials. In the first study, we consider the maximum net production; in the second, we minimize the set-up. Both cases reflect the reality of a factory. The complexity of these models contributes to the use of heuristic and metaheuristic methods as techniques for their resolution. However, we also consider the evaluation of the association of these methods with the use of mathematical programming. For this, we propose mathematical models within the context of the considered industries. Thus, a Greedy Filter heuristic and metaheuristics such as the Simple Genetic Algorithm, the Multi-Population Genetic Algorithm and the Modified Genetic Algorithm are used to determine the integer variables present in mathematical models. Besides, we use an exact method from CPLEX tool to determine continuous variables. The studies are conducted from data provided by industries located in Brazil and Portugal. Therefore, the results collaborate with state of the art in this research area and with the industrial glass containers manufacturing process.

[pt] A indústria de transporte marítimo é essencial para o comércio internacional. No entanto, no despertar da crise financeira de 2008, essa indústria foi severamente atingida. Nessas ocasiões, empresas de transporte só são capazes de obter lucro se suas frotas forem roteadas de forma eficaz. Neste trabalho, nós estudamos uma classe de problemas de roteamento de navios relacionados ao Pickup and Delivery Problem with Time Windows. Para resolver esses problemas, nós introduzimos um método heurístico e um exato. O método heurístico é uma meta-heurística híbrida com uma vizinhança larga baseada em set partitioning, enquanto o método exato é um algoritmo de branch-and-price. Nós conduzimos experimentos em um conjunto de instâncias baseadas em rotas de navios reais. Os resultados obtidos mostram que nossos algoritmos superam as metodologias estado da arte. Em seguida, nós adaptamos o conjunto de instâncias para modelar um problema de roteamento de navios no qual a velocidade em cada segmento de rota é uma variável de decisão, e o consumo de combustível por unidade de tempo é uma função convexa da velocidade e carga do navio. A fim de resolver esse novo problema de roteamento de navios com otimização de velocidade, nós estendemos nossa meta-heurística para encontrar decisões de velocidade ótimas em toda avaliação de solução vizinha de uma busca local. Nossos experimentos demonstram que essa abordagem pode ser altamente rentável, e que requer apenas um aumento moderado de recursos computacionais. / [en] The shipping industry is essential for international trade. However, in the wake of the 2008 financial crisis, this industry was severely hit. In these times, transportation companies can only obtain profit if their fleet is routed effectively. In this work, we study a class of ship routing problems related to the Pickup and Delivery Problem with Time Windows. To solve these problems, we introduce a heuristic and an exact method. The heuristic method is a hybrid metaheuristic with a set-partitioning-based large neighborhood, while the exact method is a branch-and-price algorithm. We conduct experiments on a benchmark suite based on real-life shipping segments. The results obtained show that our algorithms largely outperform the state-of-the-art methodologies. Next, we adapt the benchmark suite to model a ship routing problem where the speed on each sailing leg is a decision variable, and fuel consumption per time unit is a convex function of the ship speed and payload. To solve this new ship routing problem with speed optimization, we extend our metaheuristic to find optimal speed decisions on every local search move evaluation. Our computational experiments demonstrate that such approach can be highly profitable, with only a moderate increase in computational effort.

[pt] Clusterização desempenha um papel importante em data mining, sendo útil em muitas áreas que lidam com a análise exploratória de dados, tais como recuperação de informações, extração de documentos e segmentação de imagens. Embora sejam essenciais em aplicações de data mining, a maioria dos algoritmos de clusterização são métodos ad-hoc. Eles carecem de garantias na qualidade da solução, que em muitos casos está relacionada a uma convergência prematura para um mínimo local no espaço de busca. Neste trabalho, abordamos o problema de clusterização a partir da perspectiva de otimização, onde propomos um algoritmo genético híbrido para resolver o problema Minimum Sum-of-Squares Clustering (MSSC, em inglês). A meta-heurística proposta é capaz de escapar de mínimos locais e gerar soluções quase ótimas para o problema MSSC. Os resultados mostram que o método proposto superou os resultados atuais da literatura – em termos de qualidade da solução – para quase todos os conjuntos de instâncias considerados para o problema MSSC. / [en] Clustering plays an important role in data mining, being useful in many fields that deal with exploratory data analysis, such as information retrieval, document extraction, and image segmentation. Although they are essential in data mining applications, most clustering algorithms are adhoc methods. They have a lack of guarantee on the solution quality, which in many cases is related to a premature convergence to a local minimum of the search space. In this research, we address the problem of data clustering from an optimization perspective, where we propose a hybrid genetic algorithm to solve the Minimum Sum-of-Squares Clustering (MSSC) problem. This meta-heuristic is capable of escaping from local minima and generating near-optimal solutions to the MSSC problem. Results show that the proposed method outperformed the best current literature results - in terms of solution quality - for almost all considered sets of benchmark instances for the MSSC objective.