POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.

O principal objetivo deste estudo é propor uma nova metodologia para lidar com o problema de Planejamento da Expansão de Redes de Transmissão de Energia Elétrica com Múltiplos Cenários de Geração (PERTEEG). Com a metodologia proposta neste trabalho almeja-se construir planos de expansão de redes de transmissão de energia elétrica que sejam capazes de, no menor custo de investimento possível, satisfazer às novas exigências dos sistemas elétricos modernos, tais como construção de redes de transmissão livres de congestionamento e robustas à incerteza em relação aos cenários de geração futuros. Através de estudos realizados na literatura do problema, verificou-se que novos modelos e metodologias de abordagem do PERTEEG se fazem necessários. Ao se modelar o PERTEEG visando construir redes de transmissão que contornem as incertezas em relação aos cenários de geração futuros e concomitantemente minimizar o custo de investimento para a expansão do sistema, o planejador se depara com um problema de otimização multiobjetivo. Existem na literatura da pesquisa operacional diversos algoritmos que visam lidar com problemas multiobjetivos. Nesta tese, foram aplicados dois desses algoritmos: Nondominated Sorting Genetic Algorithms-II (NSGA-II) e SPEA2: Strength Pareto Evolutionary Algorithm (SPEA2). Em primeira análise, se destacou uma das maiores dificuldade de lidar com o PERTEEG, a saber, o esforço computacional elevado. Por isso, vislumbrou-se que uma possível solução para contornar esta dificuldade esteja na computação paralela. Para se confirmar esta suspeita, nesta tese foram implementadas versões paralelas dos algoritmos sequenciais testados. A qualidade das soluções encontradas pelos algoritmos foram bastante superiores às soluções encontradas pelos algoritmos sequenciais. Neste trabalho também será mostrado que as soluções ótimas clássicas considerando somente o objetivo de m´mínimo custo são incapazes de atender às novas necessidades dos sistemas elétricos de potência. Testes computacionais foram realizados e analisados neste trabalho. Considerando as metodologias conhecidas na literatura para medição da qualidade das soluções encontradas por algoritmos multiobjetivo, se pode afirmar de que a proposta de abordagem do problema de PERTEEG pode ser viável tanto do ponto de vista de engenharia como do ponto de vista da computação matemática. / The main objective of this study is to propose a new methodology to deal with the long-term transmission system expansion planning with multiple generation dispatch scenarios problem (TEP-MDG). With the methodology proposed in this thesis we aim to build expansion plans with minimum investment cost and also capable of meeting the new demands of modern electrical systems, such as uncertainty about the future generation scenarios and congestion in the transmission systems. By modeling the TEP-MDG aiming to build transmission networks that circumvent the uncertainties regarding the future generation scenarios and simultaneously minimize the cost of investment for transmission networks expansion, the planner faces a multiobjective optimization problem. One can find various algorithms that aim to deal with multiobjective problems in the literature of operations research. In this thesis, we apply two of these algorithms: Nondominated Sorting Genetic Algorithms-II (NSGA-II) and SPEA2: Strength Pareto Evolutionary Algorithm (SPEA2). In a first analysis, we have found that the most critical issue with the TEP-MOG is the high computational demand. Therefore, in order to circumvent this difficulty we have implemented parallel versions of the sequential algorithms tested. In performed tests, the parallel algorithms have found solutions of superior quality than the solutions found by the sequential algorithms. In this thesis we also show that optimal solutions considering only the classical least cost objective are unable to meet the electric power systems new demands. Tests have been performed and analyzed in this work. By considering the methods known in the literature convinced to measure the quality of solutions found by multiobjective algorithms, we concluded that the proposed approach to TEP-MDG may be feasible from the point of view of both engineering and computational mathematics.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-02-08T15:41:51Z No. of bitstreams: 1 Wagner Lourenzi Simões_.pdf: 1389162 bytes, checksum: 302aec842d2f4e8b0a7c78ecbae24357 (MD5) / Made available in DSpace on 2017-02-08T15:41:51Z (GMT). No. of bitstreams: 1 Wagner Lourenzi Simões_.pdf: 1389162 bytes, checksum: 302aec842d2f4e8b0a7c78ecbae24357 (MD5) Previous issue date: 2016-12-06 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste estudo, foi desenvolvida uma ferramenta computacional baseada em metaheurísticas para a otimização do sequenciamento de produção em Flow Shop permutacionais aplicados à montagem de placas eletrônicas que operam em ambientes High-Mix, Low-Volume. O ambiente High-Mix, Low-Volume exige a realização de um grande número de setups para atender à flexibilidade exigida. Esse elevado número de sucessivos setups para a produção de pequenos lotes impacta negativamente nos custos operacionais da empresa. Uma das formas de se obter vantagem ao lidar com um grande mix de produção é explorando características similares entre os produtos, de forma que, através de um sequenciamento adequado, seja possível reduzir o tempo total de parada para setup e, por consequência, reduzir também o tempo total de processamento (makespan). A literatura apresenta muitos exemplos de sucesso na aplicação de técnicas de otimização para o sequenciamento da produção como forma de ganho de vantagem competitiva. Porém, a complexidade e o grande esforço computacional exigidos na solução deste problema, por muitas vezes, inviabilizam sua aplicação na rotina das indústrias. Neste contexto, as metaheurísticas emergem como uma opção para a viabilização de ferramentas para otimização do sequenciamento de produção. Dentre as abordagens metaheurísticas existentes, destacam-se as abordagens híbridas que combinam estratégias de busca local com algoritmos evolutivos como opções para a geração, de forma rápida, de boas soluções para o problema de sequenciamento, ainda que estes métodos não possam garantir a otimalidade da solução. A ferramenta desenvolvida, baseada no uso combinado das metaheurísticas Busca Tabu e Algoritmo Genético, busca a melhor sequência possível dentro do tempo computacional disponível de forma a reduzir os tempos gastos com operações de tempo de setup, e consequentemente o makespan. O Algoritmo Hibrido foi avaliado utilizando instâncias da literatura e instâncias advindas de um caso real. Os resultados dos testes indicam a superioridade da abordagem híbrida sobre as abordagens canônicas do algoritmo Genético e Busca Tabu. Os resultados obtidos na avaliação de instâncias reais indicam a aplicabilidade da ferramenta em ambientes reais, obtendo bons resultados na otimização dos tempos de setup, mesmo para o sequenciamento de grandes quantidades de produtos diferentes. / This work proposes the development of a metaheuristics based computation tool, to solve the permutation flow shop scheduling problem (PFSSP) in the electronic manufacturing operating in High-mix, Low-volume enviroment. To operate in HMLV enviroment is demanded a large number of setup changes to comply the flexibility required. This elevated number of successive setup changes to produce little batches have negative impacts on the operation costs. One way for to obtain advantages handling a large product mix is to explore the similar features between this products. Through a proper scheduling we can reduce the total downtime to setup changes, and consequently reduces the process time (makespan). The literature brings many success examples in the production scheduling optimization as a way to obtain competitive advantages. But, the complexity and the computational effort demanded to solve this problems, sometimes, turns the practical application unfeasible in the factories routine. In this contexto emerges the metaheuristics as an option to viability this type of application. Among the mataheuristics approaches, outstands the hybrid approaches that combine local search strategies with evolutionary algorithms as a way to obtain good and fast solutions for the scheduling problems, although the optimality is not been guaranted. The tool proposed combine the metaheuristics Genetic Algorithm and Tabu Search to optimize the flow shop scheduling in the shortest possible time to allow the practical application in industry. The tool was evaluate based on quality metrics like makespan and mean setup time. The Hybrid Algorithm has been evaluated using instances of the literature and instances arising from a real case. The results of the tests indicate a superiority of the hybrid approach over canonical approaches of the Genetic algorithm and Tabu Search. The results obtained in the evaluation of real instances indicate an applicability of the tool in real environments, obtaining good results in the optimization of textit setup times, also for the sequencing of large products. The Hybrid Algorithm has been evaluated using instances of the literature and instances arising from a real case. The tests results indicate a superiority of the hybrid approach over canonical approaches of the Genetic algorithm and Tabu Search. The results obtained in the evaluation of real instances indicate an applicability of the tool in real environments, obtaining good results in the setup time optimization, also for the sequencing of large products.

L’affectation de fr´equences (AFP) consiste `a attribuer des fr´equences radio aux liens de communications d’un r´eseauen respectant un spectre de fr´equences donn´e et des contraintes d’interf´erence ´electromagn´etique sur les liens. Vu lalimitation des ressources spectrales pour chaque application, les ressources en fr´equences sont souvent insuffisantespour d´eployer un r´eseau sans interf´erence. Dans ce cas, le r´eseau est surcontraint et le probl`eme est irr´ealisable.R´esoudre le probl`eme consiste alors `a identifier les zones surcontraintes pour en revoir la conception.Le travail que nous pr´esentons concerne la recherche d’une de ces zones surcontraintes avec une approche algo-rithmique bas´ee sur la mod´elisation du probl`eme par un CSP. Le probl`eme de l’affectation de fr´equences doit doncˆetre mod´elis´e comme un probl`eme de satisfaction de contraintes (CSP) qui est repr´esent´e par un tripl´e : un ensemblede variables (les liens radio), un ensemble de contraintes (les interf´erences ´electromagn´etiques), et un ensemble dedomaines (les fr´equences admises).Sous forme de CSP, une zone perturb´ee peut ˆetre consid´er´ee comme un sous-ensemble irr´ealisable irr´eductible duprobl`eme (IIS pour Irreductible Infeasible Subset). Un IIS est un sous probl`eme de taille minimale qui est irr´ealisable,c’est-`a-dire que tous les sous-ensembles d’un IIS sont r´ealisables. L’identification d’un IIS dans un CSP se rapporte `a deux r´esultats g´en´eraux int´eressants. Premi`erement, en localisant un IIS on peut plus facilement prouver l’irr´ealisabilit´ed’un probl`eme donn´e car l’irr´ealisabilit´e d’un IIS, qui est suppos´e ˆetre petit par rapport au probl`eme complet, est plusrapidement calculable que sur le probl`eme entier. Deuxi`emement, on peut localiser la raison de l’irr´ealisabilit´e; dansce cas, sur un probl`eme r´eel, le d´ecideur peut proposer des solutions pour relˆacher des contraintes de l’IIS, et peut-ˆetre aboutir `a une solution r´ealisable pour son probl`eme. La recherche d’IIS consiste donc `a r´esoudre un probl`emefondamental qui fait partie des outils de prise de d´ecision.Ce travail propose des algorithmes pour identifier un IIS dans un CSP incoh´erent. Ces algorithmes ont ´et´e test´essur des instances connues du probl`eme de l’affectation des fr´equences et du probl`eme de k-coloration de graphe. Lesr´esultats ont montr´es d’une grande am´elioration sur des instances du probl`eme de l’affectation des fr´equences parrapport aux m´ethodes connues. / The frequency assignment (FAP) consists in assigning the frequency on the radio links of a network which satisfiesthe electromagnetic interference among the links. Given the limited spectrum resources for each application, the fre-quency resources are often insufficient to deploy a wireless network without interference. In this case, the network isover-contrained and the problem is infeasible. Our objective is to identify an area with heavy interference.The work presented here concerns the detection for one of these areas with an algorithmic approach based onmodeling the problem by CSP. The problem of frequency assignment can be modeled as a constraint satisfactionproblem (CSP) which is represented by a triple: a set of variables (radio links), a set of constraints (electromagneticinterference) and a set of available frequencies.The interfered area in CSP can be considered a subset of irreducible feasible subset (IIS). An IIS is a infeasiblesubproblem with irreducible size, that is to say that all subsets of an IIS are feasible. The identification of an IIS ina CSP refers to two general interests. First, locating an IIS can easily prove the infeasibility of the problem. Becausethe size of IIS is assumed to be smaller compared to the entire problem, its infeasibility is relatively easier to prove.Second, we can locate the reason of infeasibility, in this case, the decision maker can provide the solutions to relax theconstraints inside IIS, which perhaps leads to a feasible solution to the problem.This work proposes algorithms to identify an IIS in the over-constrained CSP. These algorithms have tested on the well known benchmarks of the FAP and of the problem of graph k-coloring. The results show a significant improve-ment on instances of FAP compared to known methods.

M.Sc. University of KwaZulu-Natal, Durban 2013.

Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: In a minerals processing environment, stable production processes, cost minimisation and energy efficiency are key to operational excellence, safety and profitability. At an ilmenite smelter, typically found in the heavy minerals industry, it is no different. Management of an ilmenite smelting process is a complex, multi-variable challenge with high costs and safety risks at stake. A by-product of ilmenite smelting is superheated carbon monoxide (CO) gas, or furnace off-gas. This gas is inflammable and extremely poisonous to humans. At the same time the gas is a potential energy source for various on-site heating applications. Re-using furnace off-gas can increase the energy efficiency of the energy intensive smelting process and can save on the cost of procuring other gas for heating purposes. In this research project, the management of CO gas from the Tronox KZN Sands ilmenite smelter in South Africa was studied with the aim of optimising the current utilisation of the gas. In the absence of any buffer capacity in the form of a pressure vessel, the stability of the available CO gas is directly dependent on the stability of the furnaces. The CO gas has been identified as a partial replacement for methane gas which is currently purchased for drying and heating of feed material and pre-heating of certain smelter equipment. With no buffer capacity between the furnaces and the gas consuming plants, a dynamic prioritisation approach had to be found if the CO was to replace the methane. The dynamics of this supply-demand problem, which has been termed the “CO gas problem”, needed to be studied. A discrete-event simulation model was developed to match the variable supply of CO gas to the variable demand for gas over time – the demand being a function of the availability of the plants requesting the gas, and the feed rates and types of feed material processed at those plants. The problem was formulated as a multi-objective optimisation problem with the two main, conflicting objectives, identified as: 1) the average production time lost per plant per day due to CO-methane switchovers; and 2) the average monthly saving on methane gas costs due to lower consumption thereof. A metaheuristic, namely multi-objective optimisation using the cross-entropy method, or MOO CEM, was applied as optimisation algorithm to solve the CO gas problem. The performance of the MOO CEM algorithm was compared with that of a recognised benchmark algorithm for multi-objective optimisation, the NSGA II, when both were applied to the CO gas problem. The background of multi-objective optimisation, metaheuristics and the usage of furnace off-gas, particularly CO gas, were investigated in the literature review. The simulation model was then developed and the optimisation algorithm applied. The research aimed to comment on the merit of the MOO CEM algorithm for solving the dynamic, stochastic CO gas problem and on the algorithm’s performance compared to the benchmark algorithm. The results served as a basis for recommendations to Tronox KZN Sands in order to implement a project to optimise usage and management of the CO gas. / AFRIKAANSE OPSOMMING: In mineraalprosessering is stabiele produksieprosesse, kostebeperking en energie-effektiwiteit sleuteldrywers tot bedryfsprestasie, veiligheid en wins. ‘n Ilmenietsmelter, tipies aangetref in swaarmineraleprosessering, is geen uitsondering nie. Die bestuur van ‘n ilmenietsmelter is ‘n komplekse, multi-doelwit uitdaging waar hoë kostes en veiligheidsrisiko’s ter sprake is. ‘n Neweproduk van die ilmenietsmeltproses is superverhitte koolstofmonoksiedgas (CO gas). Hierdie gas is ontvlambaar en uiters giftig vir die mens. Terselfdertyd kan hierdie gas benut word as energiebron vir allerlei verhittingstoepassings. Die herbenutting van CO gas vanaf die smelter kan die energie-effektiwiteit van die energie-intensiewe smeltproses verhoog en kan verder kostes bespaar op die aankoop van ‘n ander gas vir verhittingsdoeleindes. In hierdie navorsingsprojek is die bestuur van die CO gasstroom wat deur die ilmenietsmelter van Tronox KZN Sands in Suid-Afrika geproduseer word, ondersoek met die doel om die huidige benuttingsvlak daarvan te verbeter. Weens die afwesigheid van enige bufferkapasiteit in die vorm van ‘n drukbestande tenk, is die stabiliteit van CO gas beskikbaar vir hergebruik direk afhanklik van die stabiliteit van die twee hoogoonde wat die gas produseer. Die CO gas kan gedeeltelik metaangas, wat tans aangekoop word vir die droog en verhitting van voermateriaal en vir die voorverhitting van sekere smeltertoerusting, vervang. Met geen bufferkapasiteit tussen die hoogoonde en die aanlegte waar die gas verbruik word nie, was die ondersoek van ‘n dinamiese prioritiseringsbenadering nodig om te kon vasstel of die CO die metaangas kon vervang. Die dinamika van hierdie vraag-aanbod probleem, getiteld die “CO gasprobleem”, moes bestudeer word. ‘n Diskrete-element simulasiemodel is ontwikkel as probleemoplossingshulpmiddel om die vraag-aanbodproses te modelleer en die prioritiseringsbenadering te ondersoek. Die doel van die model was om oor tyd die veranderlike hoeveelhede van geproduseerde CO teenoor die veranderlike gasaanvraag te vergelyk. Die vlak van gasaanvraag is afhanklik van die beskikbaarheidsvlak van die aanlegte waar die gas verbruik word, sowel as die voertempo’s en tipes voermateriaal in laasgenoemde aanlegte. Die probleem is geformuleer as ‘n multi-doelwit optimeringsprobleem met twee hoof, teenstrydige doelwitte: 1) die gemiddelde verlies aan produksietyd per aanleg per dag weens oorgeskakelings tussen CO en metaangas; 2) die gemiddelde maandelikse besparing op metaangaskoste weens laer verbruik van dié gas. ‘n Metaheuristiek, genaamd MOO CEM (multi-objective optimisation using the cross-entropy method), is ingespan as optimeringsalgoritme om die CO gasprobleem op te los. Die prestasie van die MOO CEM algoritme is vergelyk met dié van ‘n algemeen aanvaarde riglynalgoritme, die NSGA II, met beide toepas op die CO gasprobleem. The agtergrond van multi-doelwit optimering, metaheuristieke en die benutting van hoogoond af-gas, spesifiek CO gas, is ondersoek in die literatuurstudie. Die simulasiemodel is daarna ontwikkel en die optimeringsalgoritme is toegepas.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: For wood manufacturers around the world, the single biggest cost factor is known to be its raw material. Thus maximum utilisation, specifically volume recovery of this raw material, is of key importance for the industry. The wood products industry consists of several interrelated manufacturing steps for converting trees into logs and logs into finished lumber. At most primary and secondary wood processors the different manufacturing steps are optimised in isolation or based on operator experience. This can lead to suboptimal decisions and a substantial waste of raw material. The objective of this study was to determine the optimal machine settings for two interrelated operations, namely the sawing and ripping operations which have traditionally been optimised individually. A model, having two decision variables, was developed which aims to satisfy market demand at a minimal cost. The first decision was how to saw the log supply into different thicknesses by choosing specific sawing patterns. The second was to decide on a rip saw’s settings, namely part priority values, which determines how the products from the primary sawing operation are ripped into products of a certain thickness and width. The techniques used to determine the machine settings included static simulation with the SIMSAW software to represent the sawing operation and mixed integer programming to model the ripping operation. A metaheuristic, namely the Population Based Incremental Learning algorithm, was the link between the two operations and determined the optimal settings for the combined process. The model’s objective function was formulated to minimise the cost of production. This cost included the raw material waste cost and the over or under production cost. The over production cost was estimated to include the stock keeping costs. The under production cost was estimated as the buy-in cost of purchasing the under supplied products from another wood supplier. The model performed well against current decision software available in South Africa, namely the Sawmill Production Planning System package, which combines simulation (SIMSAW) and mixed integer programming techniques to maximise profit. The model added further value in modelling and determining the ripping priority settings in addition to the primary sawing patterns. / AFRIKAANSE OPSOMMING: Die grootste enkele koste vir houtprodukvervaardigers wêreldwyd is dié van hulle roumateriaal. Die maksimale gebruik van rou materiaal, of volume herwinning, is dus van primêre belang vir hierdie industrie. Die vervaardigingsproses in die houtprodukte-industrie bestaan uit ‘n verskeidenheid interafhanklike stappe om bome na stompe te verwerk en stompe na eindprodukte. By meeste primêre -en sekondêre houtvervaardigers word die verskillende vervaardigingsstappe in isolasie ge-optimeer. Hierdie praktyk lei tot sub-optimale besluite en ‘n vermorsing van roumateriale. Die doelwit van hierdie studie was om die optimale masjienverstellings vir twee interafhanklike prosesse, die primêre -en kloofsaag prosesse, te bepaal. Tradisioneel word hierdie twee prosesse individueel optimeer. ‘n Model met twee besluitnemingsveranderlikes is ontwikkel wat poog om die markaanvraag te bevredig teen ‘n minimum koste. Die eerste besluit was watter saagpatroon gekies moet word om die stompe in die regte dikte produkte te saag. Die tweede besluit was wat die kloofsaagstellings, ook bekend as prioriteitswaardes, moet wees sodat die regte wydte produkte gesaag word. Die tegnieke wat gebruik is sluit statiese simulasie met SIMSAW sagteware in om die primêre saagproses te modelleer en gemengde heelgetalprogammering (“mixed integer programming”) om die kloofsaagproses te modelleer. ‘n Metaheuristiek genaamd die “Population Based Incremental Learning” algoritme,was die skakel tussen die twee operasies om die optimale masjienstellings vir die proses te bepaal. Die model se doelfunksie was geformuleer om die koste van produksie te minimeer. Hierdie koste sluit die roumateriaal afvalkoste en die kostes van oor -en onderproduksie in. Die oorproduksiekoste was ‘n skatting van die voorraadkostes. Die onderproduksiekoste was ‘n skatting van die koste om voorraad van ‘n ander verskaffer aan te koop. Die model het goed opgeweeg teen die beskikbare besluitnemingssagteware in Suid Afrika, die “Sawmill Production Planning System”, wat ‘n kombinasie van SIMSAW en ‘n gemengde heelgetalprogrammeringstegniek is. Die model het verder waarde toegevoeg deur die kloofsaag se prioriteitswaardes te modelleer saam met die primêre saagpatrone.

Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-12-19Bitstream added on 2014-06-13T20:21:17Z : No. of bitstreams: 1 escobarzuluaga_ah_dr_ilha.pdf: 1508525 bytes, checksum: b6e7b58056f84298f2b063ead5371a59 (MD5) / Fundação de Ensino Pesquisa e Extensão de Ilha Solteira (FEPISA) / O principal objetivo deste estudo é realizar uma análise de aspectos críticos que surgem na modelagem matemática do problema de planejamento da expansão de sistemas de transmissão a longo prazo, assim como o desenvolvimento de ferramentas computacionais para a prova de novos modelos e metodologias que possam contribuir na solução do problema de planejamento de sistemas de transmissão de energia elétrica considerando as condições dos sistemas modernos de energia elétrica. Com esta metodologia, busca-se obter uma rede de transmissão mais eficiente, e com o menor custo possível, que se adapte as novas exigências produzidas pela introdução da desregulação nos sistemas elétricos. Para isto combinam-se três aspectos: rede futura livre de congestionamento, desplanificação e incerteza na geração e na demanda futura, os quais são manipuladas desde a perspectiva mono-objetivo e multiobjetivo. A possibilidade de eliminar completamente o congestionamento na rede de transmissão é analisada através da inclusão no modelo de todos os cenários de geração factíveis futuros, e não somente alguns cenários como outros estudos. Considerar uma operação sem congestionamento para o futuro está associado a grandes custos de investimento. Para atenuar este grande custo uma opção é incluir a possibilidade de desplanificação e a inclusão dos efeitos das incertezas presentes na geração e na demanda futura no problema de planejamento. O problema de planejamento de sistemas de transmissão é um problema de programação não linear inteira mista (PNLIM) quando é usado o modelo DC. Praticamente todos os algoritmos usados para resolver este problema utilizam uma sub-rotina de programação linear (PL) para resolver problemas de PL resultantes do algoritmo de solucão do problema de planejamento, os quais são denominados... / The main goal for this study is to do an analysis of the critical issues that appear in the mathematical modeling of the transmission system expansion planning problem, when long term is considered. A methodology was developed and a computational tool, to solve the transmission expansion planning in modern electrical systems. With this methodology more efficient electrical networks are obtained, at low investment costs. This is accomplished taking into account three important aspects: open access, or congestion-free planning, uncertainty in demand and generation, and de-planning. The problem is solved using mono-objective and multi-objective methodologies. For this investigation, congestion-free transmission networks should consider all the future and feasible scenarios of generation, unlike some papers, where only a few scenarios are taken in to account. This feature is associated to high investment costs. Lower costs are often obtained by the inclusion of uncertainty in future demand and future generation. The transmission system expansion planning problem is a no-linear integer-mixed programming problem (PNLIM) when the DC model is used. Practically, all the algorithms used in the solution process, for this problem, use one subroutine of linear programming (PL) for solved the PL problems that result during the solution process, in the denominated operative problem. The solution of the PL’s is the part of the problem that requires the biggest computational effort, because during the solution process is necessary to solved thousands or millions of PL’s, for high size problems. the PNLIM problem is solved through the combination of a meta-heuristic method and a linear programming method. The meta-heuristic method solves the denominated investment problem and the PL the denominated operational problem. The transmission planning problem considering multiples generation scenarios... (Complete abstract click electronic access below)

Made available in DSpace on 2014-12-17T15:47:03Z (GMT). No. of bitstreams: 1 GivanaldoRS_TESE.pdf: 2106802 bytes, checksum: 88486cf095bfcefea309b73b76e7de67 (MD5) Previous issue date: 2013-10-25 / This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems / A presente tese prop?e uma arquitetura multiagente para hibridiza??o de metaheur?sticas, inspirada na t?cnica de Otimiza??o por Nuvem de Part?culas, e tem como principal contribui??o a proposta de uma abordagem efetiva para resolu??o de problemas de otimiza??o combinat?ria. A escolha da Otimiza??o por Nuvem de Part?culas como inspira??o deu-se pelo fato desta t?cnica ser inerentemente multiagente, permitindo explorar os recursos dos sistemas multiagente, tais como as t?cnicas de aprendizado e coopera??o. Na arquitetura proposta, as part?culas s?o agentes aut?nomos com mem?ria e m?todos de decis?o e de aprendizagem, utilizando estrat?gias de busca para se moverem no espa?o de solu??es. Os conceitos de posi??o e velocidade, originalmente definidos na Otimiza??o por Nuvem de Part?culas, s?o redefinidos para esta abordagem. A arquitetura proposta foi aplicada ao Problema do Caixeiro Viajante e ao Problema Quadr?tico de Aloca??o, realizando experimentos computacionais que comprovaram sua efetividade. Os resultados dos experimentos foram bastante promissores, apresentando desempenho satisfat?rio, considerando que o potencial da arquitetura proposta ainda n?o foi totalmente explorado. Em pesquisas futuras, a abordagem proposta ser? aplicada a problemas de otimiza??o combinat?ria multiobjetivo, os quais s?o mais pr?ximos aos problemas do mundo real. No ?mbito da pesquisa aplicada, pretende-se trabalhar tanto com alunos em n?vel de gradua??o como em n?vel t?cnico a aplica??o da arquitetura proposta em problemas pr?ticos do mundo real

Made available in DSpace on 2014-12-17T15:48:01Z (GMT). No. of bitstreams: 1 EdmilsonFMOJ_DISSERT.pdf: 4310075 bytes, checksum: c753f90b3f1afd654108edecd6a3fc70 (MD5) Previous issue date: 2012-02-27 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This work seeks to propose and evaluate a change to the Ant Colony Optimization based on the results of experiments performed on the problem of Selective Ride Robot (PRS, a new problem, also proposed in this paper. Four metaheuristics are implemented, GRASP, VNS and two versions of Ant Colony Optimization, and their results are analyzed by running the algorithms over 32 instances created during this work. The metaheuristics also have their results compared to an exact approach. The results show that the algorithm implemented using the GRASP metaheuristic show good results. The version of the multicolony ant colony algorithm, proposed and evaluated in this work, shows the best results / Este trabalho tem o objetivo de propor e avaliar uma variante para o algoritmo de col?nia de formigas baseando-se no resultado de experimentos executados sobre o problema do Passeio do Rob? Seletivo (PRS, um novo problema, tamb?m proposto neste trabalho. S?o implementadas quatro metaheur?sticas, GRASP, VNS, e duas vers?es do Otimiza??o por Col?nia de Formigas, e analisados seus resultados executando-os sobre 32 inst?ncias criadas no trabalho. As metaheur?sticas tamb?m tem seu resultado comparado com o de um algoritmo exato. Os resultados mostram que o algoritmo implementado utilizando a metaheur?stica GRASP apresenta bons resultados. A vers?o multi-col?nias do algoritmo de col?nia de formigas, proposta e avaliada no trabalho, apresenta os melhores resultados