De nos jours, les plateformes portuaires cherchent à massifier leurs capacités de projection de conteneurs vers et à partir de leurs réseaux hinterland en misant sur les modes ferroviaires et fluviaux. Cela pour évacuer plus rapidement un volume quasi croissant de conteneurs livré par voie maritime et d’éviter les situations indésirables, tels que les situations d'asphyxie. De plus, les plateformes portuaires ont pris conscience que leur attractivité aux yeux des prestataires logistiques dépend non seulement de leur fiabilité et de leurs qualités nautiques mais également de leur capacité à offrir une desserte massifiée de leur hinterland. Contrairement à ce qui a pu être observé en Europe, la part du transport massifié a quasiment stagné au Havre dans les dernières années. A cet effet, le port du Havre a mis en place un terminal multimodal de conteneurs lié par rail et par voie navigable à un hinterland riche et dense en population (Bassin parisien, Marchés européens), et par des navettes ferroviaires aux autres terminaux maritimes du port Havre. L’intérêt économique et stratégique de ce nouveau terminal est de renforcer la position du Grand Port Maritime du Havre au niveau national, européen et mondial, et d’un point de vue écologique, diminuer l’utilisation excessive du routier en misant sur les modes moins polluants. Dans cette thèse, les efforts se focalisent sur la modélisation et la simulation du déroulement des opérations de manutention et d’allocation de ressources dans un terminal à conteneurs et particulièrement l’ordonnancement des portiques de manutention. Étant donné qu’un terminal à conteneurs est un système complexe, nous avons d’abord défini une démarche de modélisation qui facilite le processus de construction du modèle de simulation. Cette démarche est un processus itératif permettant de raffiner le modèle au fur et à mesure des étapes de développement réalisées. Les différentes étapes de développement sont liées par une série de diagrammes qui permet d’exprimer de façon claire les éléments et les relations formant le modèle de simulation. Ensuite, nous avons intégré dans notre modèle deux stratégies de non-croisement de portiques au niveau de la cour ferroviaire du terminal multimodal. Le but de ces stratégies est la minimisation des temps et des mouvements improductifs pour améliorer la performance et la productivité des portiques de manutention. La première stratégie est basée sur des règles de mouvement et sur la collaboration et coopération entre agents portiques. Tandis que la deuxième stratégie est basée sur une heuristique. Ces deux solutions ont été testées en utilisant l’outil de simulation AnyLogic et les résultats obtenus montrent la qualité de nos solutions. Concernant le problème d’ordonnancement des portiques de la cour fluviale, nous l’avons étudié en utilisant un couplage Optimisation-Simulation. Dans ce problème les temps de chargement et de déchargement de conteneurs et les temps de déplacement des portiques entre les baies sont considérés comme incertains. Le couplage est composé d’une méta-heuristique colonie de fourmis et d’un modèle de simulation à base d’agents. Chaque solution (une séquence de tâches) trouvée par l’algorithme d'optimisation est simulée et évaluée pour déterminer les nouvelles durées des tâches qui seront ensuite injectées comme données d’entrée de l’algorithme avant l’itération suivante. / Nowadays, seaports seek to achieve a better massification share of their hinterland transport by promoting rail and river connections in order to more rapidly evacuate increasing container traffic shipped by sea and to avoid landside congestion. Furthermore, the attractiveness of a seaport to shipping enterprises depends not only on its reliability and nautical qualities but also on its massified hinterland connection capacity. Contrary to what has been observed in Europe, the massification share of Le Havre seaport has stagnated in recent years. To overcome this situation, Le Havre Port Authority is putting into service a multimodal hub terminal. This terminal is linked only with massified modes to a rich and dense geographical regions (Ile de France, Lyon), and with rail shuttles to the maritime terminals of Le Havre seaport. The aim of this new terminal is to restrict the intensive use of roads and to provide a river connection to its maritime terminals (MTs) that do not include a river connection from the beginning. In this study, we focus on the modeling and the simulation of container terminal operations (planning, scheduling, handling …) and particularly crane scheduling in operating areas. Designing multi-agents based simulation models for the operation management of a complex and dynamic system is often a laborious and tedious task, which requires the definition of a modeling approach in order to simplify the design process. In this way, we defined a top-down approach with several steps of specification, conception, implementation and verification-validation. This approach is an iterative process that allows the model to become more complex and more detailed. In this thesis, we pay more attention to crane scheduling problem in operating areas. For the rail-rail transshipment yard of the multimodal terminal, we designed two anti-collision strategies that aim to minimize unproductive times and moves to improve crane productivity and to speed up freight train processing. These strategies are tested using multi-method simulation software (Anylogic) and the simulation results reveal that our solutions are very satisfactory and outperform other existing solutions. With regard the fluvial yard, the stochastic version of crane scheduling problem is studied. The problem is solved with a mixed Optimization-Simulation approach, where the loading and unloading times of containers and travel times of cranes between bays are considered uncertain. The used approach is composed of an Ant Colony Optimization (ACO) metaheuristic coupled to an agent-based simulation model. Each solution (a tasks sequence) found by the optimization algorithm is simulated and evaluated to determine the new tasks’ periods which will then be injected as input to the ACO algorithm before the next iteration. The coupling is executed until the difference between the last iterations is too low.

[pt] Métodos que combinam estratégias normalmente encontradas em algoritmos metaeurísticos com técnicas para resolver problemas de programação inteira mista (MIP) têm apresentado ótimos resultados nos últimos anos. Este trabalho propõe dois novos algoritmos nessa linha: um algoritmo que faz pós-processamento nas soluções encontradas pelo resolvedor MIP. Os dois algoritmos utilizam um novo tipo de vizinhança, chamada de vizinhança elipsoidal, que possui fortes semelhanças com as técnicas de relinking de algoritmos PR e que neste trabalho é generalizada e extendida para múltiplas soluções. O problema generalizado de alocação (GAP) é usado para os experimentos. São testados também um resolvedor MIP puro (ILOG CPLEX versão 11) e um algoritmo branch and price que utiliza as heurísticas RINS e guided dives. Os algoritmos testados são comparados entre e com heurísticas específicas para o GAP. Os resultados são satisfatórios e indicam que as vizinhanças elipsoidais conseguem frequentemente melhorar as soluções encontradas pelo resolvedor MIP, encontrando a melhor solução para algumas instâncias. / [en] Methods that mix strategies usually found in metaheristic algorithms with techniques to solve mixed integer programming problems (MIPs) have had great results over the past few years. This wprk proposes two new algorithms in this philosophy: one is based on the Path Relink (PR) metaheuristc, while the other one is a simple algorithm that does post-processing in the solutions found by the MIP solver. Both algorithms use a new neighborhood structure, called ellipsoidal neighborhood, that has strong resemblances with the relinking step from PR algorithms and that, in this work, is generalized and extended for multiple solutions. The generalized assignment problem (GAP) is used for the computational experiments. Also tested are MIP solver (ILOG CPLEX version 11) and a branch and price algorithm that uses the RINS and guides dives heuristics. The tested algorithms are compared among themselves and with GAP-specific heuristics. The results are satisfactory and show that the ellipsoidal neighborhood can frequently improve the solutions found by the MIP solver, even finding the best result for some instances.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-16T14:48:47Z No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2015-12-16T15:15:57Z (GMT) No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) / Made available in DSpace on 2015-12-16T15:15:57Z (GMT). No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) Previous issue date: 2015-08-07 / O atendimento à demanda requer um uso eficiente do sistema geração sem afetar sua confiabilidade. Para o sistema termoelétrico o uso eficiente está diretamente relacionado com a queima de combustível e, consequentemente, com o custo de operação. Portanto, a minimização deste custo é obtida a partir da alocação da potência ativa a ser gerada para cada termoelétrica, que configura um problema de otimização denominado de Despacho Econômico (DE). Este trabalho aborda de forma real o problema do Despacho Econômico, devido a consideração das características que ocorrem durante a geração de energia elétrica. Estas características são as restrições de Zonas de Operação Proibidas (ZOP), Múltiplo Combustível (MC) e o efeito de ponto de válvula, que torna o problema do Despacho Econômico num problema não convexo e descontínuo. A proposta deste trabalho é a adoção de duas metaheurísticas bioinspiradas para resolver o problema do Despacho Econômico com características reais de operação. As técnicas bioinspiradas que são utilizadas consistem na: (i) Otimização via Enxame de Partículas e (ii) otimização baseada no fenômeno da ecolocalização do morcego, denominado Algoritmo do Morcego. Ambas as metaheurísticas são implementadas noMATLAB® e para a otimização do problema não linear e não convexo do Despacho Econômico é utilizado o modelo LINGO. Os resultados obtidos através das técnicas bioinspiradas aplicadas ao estudos de casos, são comparados comos encontrados na literatura especializada e, por fim, é feito a análise da eficiência das metaheurísticas utilizadas, cujo Algoritmo do Morcego apresenta o melhor desempenho. / The demand supply requires efficient use of generation system without affecting its reliability. For the thermoelectric systemits efficient use is directly related with fuel burn and, consequently, with cost operation. Therefore, to minimize this cost is obtained from the allocation of active power to be generated for each thermoelectric, which sets up an optimization problem called Economic Dispatch (DE). Thiswork considers a realway of the Economic Dispatch problemdue consideration of the characteristics that occur during eletricity generation. These features are restrictions Prohibited Operating Zones (POZ), multiple fuel and the valve-point effect, which makes the Economic Dispatch problem in non-convex and discontinuous problem. The proposal this work is adopting two bioinspired metaheuristics to solve the EconomicDispatch problemwith real operating characteristics. The bioinspired techniques that are used consist of: (i) Particle Swarm Optimization and (ii) Optimization based on bat echolocation phenomenon, called Bat Algorithm. Both metaheuristics are implemented in MATLAB® and for optimization of non-linear and non-convex problem is used LINGO model. The results obtained through the bioinspired techniques applied the study cases, are compared with those found in literature and, finally, is made the analysis of the efficiency of metaheuristics used, which Bat Algorithm has the best performance.

A programação concorrente é cada vez mais utilizada nos sistemas atuais com o objetivo de reduzir custos e obter maior eficiência no processamento. Com a importância da programação concorrente é imprescindível que programas que implementam esse paradigma apresentem boa qualidade e estejam livres de defeitos. Assim,diferentes técnicas e critérios de teste vêm sendo definidos para apoiar a validação de aplicações desenvolvidas nesse paradigma. Nesse contexto, a geração automática de dados de teste é importante, pois permite reduzir o custo na geração e seleção de dados relevantes. O uso de técnicas meta-heurísticas tem sido uma área de grande interesse entre os pesquisadores para geração de dados, pois essas técnicas apresentam abordagens aplicáveis a problemas complexos e de difícil solução. Considerando esse aspecto, este trabalho apresenta uma abordagem de geração automática de dados para o teste estrutural de programas concorrentes em MPI (Message Passing Interface). A meta-heurística usada foi Algoritmo Genético em que a busca é guiada por critérios de teste que consideram características implícitas de programas concorrentes. O desempenho da abordagem foi avaliado por meio da cobertura dos dados detestes, da eficácia em revelar defeitos e do custo de execução. Para comparação, a geração aleatória foi considerada. Os resultados indicaram que é promissor usar geração de dados de teste no contexto de programas concorrentes, com resultados interessantes em relação à eficácia e cobertura dos requisitos de teste. / Concurrent programming has been increasingly used in current systems in order to reduce costs and obtain higher processing efficiency and, consequently, it is expected that these systems have high quallity. Therefore, different techniques and testing criteria have been proposed aiming to support the verification and validation of the concurrent applications. In this context, the automated data test generation allows to reduce the testing costs during the generation and selection of data tests. Metaheuristic technique has been widely investigated to support the data test generation because this technique has presented good results to complex and costly problems. In this work, we present an approach to the automated data test generation for message passing concurrent programs in MPI (Message Passing Interface). The generation of data test is performed using the genetic algorithm metaheuristic technique, guiding by structural testing criteria. An experimental study was conducted to evaluate the proposed approach, analyzing the effectiveness and application cost. The results indicate that the genetic algorithm is a promising approach to automated test data generation for concurrent programs, presenting good results in relation to effectiveness and data test coverage.

This thesis deals with design and implementation of an investment model, which applies methods of Post-modern portfolio theory. Particle swarm optimization (PSO) metaheuristic was used for portfolio optimization and the parameters were analyzed with several experiments. Johnsons SU distribution was used for estimation of future returns as it proved to be the best of analyzed distributions. The result is software application written in Python, which is tested for stability and performance of model in extreme situations.

The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.

This thesis deals with the implementation of the metaheuristic algorithms into the Drools Planner. The Drools Planner is an open source tool for solving optimization problems. This work describes design and implementation of Ant colony optimization metaheuristics in the Drools Planner. Evaluation of the algorithm results is done by Drools Planner benchmark with different kinds of optimization problems.

Recent advances in the field of computational intelligence have led to a numberof promising optimization algorithms. These algorithms have the potential to findoptimal or near-optimal solutions to complex problems within a reasonable timeframe. Structural optimization is a research field where such algorithms are appliedto optimally design structures. Although a significant amount of research has been published in the field ofstructural optimization since the 1960s, little of the research effort has been utilizedin structural design practice. One reason for this is that only a small portion ofthe research targets real-world applications. Therefore there is a need to conductresearch on cost optimization of realistic structures, particularly large structureswhere significant cost savings may be possible. To address this need, a software application for cost optimization of beam bridgeswas developed. The software application was limited to road bridges in concretethat are straight and has a constant width of the bridge deck. Several simplificationswere also made to limit the scope of the thesis. For instance, a rough design ofthe substructure was implemented, and the design of some structural parts wereneglected. This thesis introduces the subject of cost optimization, treats fundamentaloptimization theory, explains how the software application works, and presents acase study that was carried out to evaluate the application. The result of the case study suggests a potential for significant cost savings. Yet,the speeding up of the design process is perhaps the major benefit that should inclinedesigners to favor optimization. These findings mean that current optimizationalgorithms are robust enough to decrease the cost of beam bridges compared to aconventional design. However, the software application needs several improvementsbefore it can be used in a real design situation, which is a topic for future research. / Nya framsteg inom forskningen har lett till ett antal lovande optimeringsalgoritmer.Dessa algoritmer har potentialen att hitta optimala eller nästan optimala lösningartill komplexa problem inom rimlig tid. Strukturoptimering är ett forskningsområdedär dessa algoritmer tillämpas för att dimensionera konstruktioner på ett optimaltsätt. Även om en betydande mängd forskning har publicerats inom området strukturoptimeringsedan 1960-talet, så har endast lite av forskningsinsatserna kommit tillanvändning i praktiken. Ett skäl till detta är att endast en liten del av forskningenär inriktad mot verklighetsförankrade tillämpningar. Därför finns det ett behov avatt bedriva forskning på kostnadsoptimering av realistiska konstruktioner, särskiltstora konstruktioner där betydande kostnadsbesparingar kan vara möjligt. För att möta detta behov har ett datorprogram för kostnadsoptimering avbalkbroar utvecklats. Programmet begränsades till vägbroar i betong som är rakaoch har en konstant bredd. Flera förenklingar gjordes också för att begränsaomfattningen av arbetet. Till exempel implementerades en grov dimensionering avunderbyggnaden, och dimensioneringen av vissa komponenter försummades helt ochhållet. Detta examensarbete presenterar ämnet kostnadsoptimering, behandlar grundläggandeoptimeringsteori, förklarar hur programmet fungerar, och presenterar enfallstudie som genomfördes för att utvärdera programmet. Resultatet av fallstudien visar en potential för betydande kostnadsbesparingar.Trots det så är tidsbesparingarna i dimensioneringsprocessen kanske den störstafördelen som borde locka konstruktörer att använda optimering. Dessa upptäckterinnebär att aktuella optimeringsalgoritmer är tillräckligt robusta för att minskakostnaden för balkbroar jämfört med en konventionell dimensionering. Dock måsteprogrammet förbättras på flera punkter innan det kan användas i en verklig dimensioneringssituation,vilket är ett ämne för framtida forskning.

Global warming is a serious threat to the existence of human life on earth. Greenhouse gas emission is the major cause of global warming. Carbon dioxide is the major greenhouse gas emitted due to human activity. Road transport accounts for 15% of CO2 emissions worldwide. There are many initiatives adopted worldwide to minimize the emission of CO2 due to road transportation. Vehicle engines are upgraded to make it environment friendly, electric vehicles are promoted, public transport is promoted, etc. Apart from these, proper road network planning could also reduce emissions. It is not practical to completely replace the road transport system due to its importance in the transport of passengers and goods. This thesis is focused on finding ideal road conditions, that produce minimum CO2 emissions. The road network parameters that are studied in this thesis are speed limit, the number of vehicle lanes, and junction design. Due to time constraints, it is not feasible to do this study on a real road network. Hence, a simple artificial road network is created using a traffic simulator named SUMO for analysis. Both traffic congestion and the higher speed of the vehicles cause higher emissions. Roads are heavily interconnected in cities and road parameters of the adjacent roads should be adjusted together with the road being studied, to have an impact on the overall traffic. As adjacent road networks are too many numbers in cities, it is not feasible to validate every possible option. There is no algorithm invented so far to analyze such problems having too many possible states. However, there are optimization algorithms that can determine approximate solutions for such problems. In this thesis, I compared the performance of five nature-inspired metaheuristic algorithms on the sustainable road network problem. The five algorithms studied in this study are Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony, Differential Evolution, and Harmony Search. The differential evolution algorithm generated the best result and was able to reduce the emissions by 6% and it is followed by genetic algorithms. Statistical tests are performed to evaluate whether the differences are significant or not.