In the last decades there has been a lot of interest in computational models and metaheuristics algorithms capable to solve combinatorial optimization problems. The recent trend is to define these algorithms taking inspiration by the observation of natural systems. In this thesis the Ant Colony System (ACS) is presented which has been inspired by the observation of real ant colonies. ACS is initially proposed to solve the symmetric and asymmetric travelling salesman problems where it is shown to be competitive with other metaheuristics. Although this is an interesting and promising result, it was immediately clear that ACS, as well as other metaheuristics, in many cases cannot compete with specialized local search methods. An interesting trend is therefore to couple metaheuristics with a local optimizer, giving birth to so-called hybrid methods. Along this line, the thesis investigates MACS-VRPTW (Multiple ACS for the Vehicle Routing Problem with Time Windows) and HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem (SOP). In the second part the thesis introduces some modifications of the original ACS algorithm. These modifications are able to speed up the method and to make it more competitive in case of large problem instances. The resulting framework, called Enhanced Ant Colony System is tested for the SOP. Finally the thesis presents the application of ACS to solve real-life vehicle routing problems where additional constraints and stochastic information are included. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Todo início de período letivo, gestores de instituições de ensino se deparam com um típico problema: montar as grades horárias das turmas, segundo as demandas de aulas de suas disciplinas e considerando as restrições de disponibilidade horária de todos os envolvidos. Conhecido na literatura como School Timetabling Problem (STP), este típico problema de otimização combinatória é reconhecidamente complexo por conta do seu elevado número de variáveis e restrições. Devido à dependência das regras do sistema educacional de cada país, o STP pode ter inúmeras variantes, cada uma com o seu próprio conjunto de particularidades. Este trabalho se propõe a oferecer um modelo para o STP considerando o sistema educacional brasileiro, visando alocar não apenas professores, mas também determinando que disciplina cada professor deve ministrar e alocando os locais de aula. O modelo proposto, baseado na meta-heurística simulated annealing, foi concebido para que cada instituição de ensino usuária tenha liberdade para definir a penalidade de cada tipo possível de inconformidade ou restrição, de modo que o algoritmo empregado possa encontrar uma solução com o menor custo possível. / Every beginning of term, educational institution managers face a typical problem: planning the classes' timetable, according to their lesson demands for each subject, considering, furthermore, the schedule constrains of all actors. Known as school timetabling problem (STP), this typical combinatorial optimization problem is remarkably complex due to the high number of variables and constraints. Owing to the rules of each country's educational system, STP can have uncountable variants, each one with their own set of features. This dissertation searches to offer a model to STP considering the Brazilian Educational System, focusing on allocating not only the teachers but also determining which subject each teacher should teach and allocating classrooms, laboratories and the like. The propesed model, based on the metaheuristic simulated annealing, was conceived so that each educational institution using this model has the freedom to define which penalty will be applied to each possible kind of noncomformity and constraint, in order for the applied algorithm to find a solution at the lowest cost as possible.

O Problema do Lapidário tem como objetivo encontrar o modelo de lapidação que resulte no maior aproveitamento volumétrico para uma dada gema bruta. Nesta dissertação apresentamos um Algoritmo Genético com variáveis de valores reais, e um GRASP Contínuo como heurísticas para resolução deste problema. Ambos os algoritmos maximizam o fator de escala do modelo de lapidação, sobre todas as posições de centro e ângulos de giro que o modelo pode assumir, buscando encontrar o modelo de maior volume inscrito no interior da gema, representada virtualmente por uma malha triangular. Propomos também um algoritmo de avaliação de uma instância do problema, o qual determina eficientemente o maior fator de escala, para um dado centro e orientação, que o modelo de lapidação pode assumir permanecendo completamente no interior da gema. Os algoritmos propostos foram avaliados em um conjunto de 50 gemas reais para o problema, utilizando como modelos base os cortes redondo e oval. Por fim, comparamos os resultados computacionais obtidos em relação a aproveitamento volumétrico e tempo de execução com os principais trabalhos relatados na literatura, demonstrando que as heurísticas propostas são competitivas com as demais abordagens. / The goal of the gemstone cutting problem is to find the largest cutting design which fits inside a given rough gemstone. In this work, we propose a real-valued Genetic Algorithm and a Continuous GRASP heuristic to solve it. The algorithms determine the largest scaling factor, over all possibilities of centers and orientations which the cutting could assume, finding the cutting with the largest volume as possible inside a gemstone, represented by a triangular mesh. We also propose an algorithm to evaluate a problem instance. This method efficiently determines the greatest scaling factor, for a given center and orientation, such that the cutting fits inside the rough gemstone. The proposed algorithms are validated for an instance set of 50 real-world gemstones, using the round and oval cuttings. Finally, we compare our computational results, for volume yield and running time, with the state-of-art. Ours methods are proved be competitive with the previous approachs.

Made available in DSpace on 2014-07-29T14:57:46Z (GMT). No. of bitstreams: 1 dissertacao_jean.pdf: 321149 bytes, checksum: 11c9f94be02284e8412d026b60b596d0 (MD5) Previous issue date: 2010-08-16 / The Course Scheduling is a hard resolution problem, found in most of the learning institutions. Just like the others timetabling problems, the Course Scheduling have a strong associative characteristic, that means that its resolution is made of associations between events and resources. In the educational case, the lectures are events, while the teachers workload are resources. Techniques and methods have being used on the solution of these kind of problems, however is small the number of universities using software based solutions. This work is a starting point to software based solutions applied to the Federal University of Goiás. / O Agendamento Semanal de Aulas é um problema de difícil resolução enfrentado em grande maioria das instituições de ensino. Assim como os demais problemas de timetabling, possui como característica principal a sua natureza associativa, ou seja, sua resolução envolve a associação entre uma certa quantidade de recursos e eventos que utilizarão tais recursos. Especificamente em relação ao problema em questão, as aulas a serem ministradas podem ser caracterizadas como eventos, enquanto que a carga horária dos professores envolvidos podem ser vistas como recursos disponíveis (Programação de Horários de Aulas). Técnicas e métodos de grande relevância na ciência da computação estão relacionados na pesquisa e na solução destes tipos de problemas, contudo, a utilização de tais tecnologias no cotidiano de escolas e universidades ainda é pequena. Neste contexto, propõe-se uma abordagem para a resolução de Problemas de Programação de Horários, incluindo o Problema de Alocação de Professores a Disciplinas, e utiliza-se o Instituto de Informática da Universidade Federal de Goiás como um estudo de caso para tal.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2017-06-19T13:06:19Z No. of bitstreams: 2 Dissertação - Fernando Henrique Fernandes de Camargo - 2017.pdf: 1478139 bytes, checksum: 1a8dae23d70a76b9e72b96aae929d85e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-07-10T12:28:25Z (GMT) No. of bitstreams: 2 Dissertação - Fernando Henrique Fernandes de Camargo - 2017.pdf: 1478139 bytes, checksum: 1a8dae23d70a76b9e72b96aae929d85e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-07-10T12:28:25Z (GMT). No. of bitstreams: 2 Dissertação - Fernando Henrique Fernandes de Camargo - 2017.pdf: 1478139 bytes, checksum: 1a8dae23d70a76b9e72b96aae929d85e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-03-20 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / For countries like Brazil, which has hybrid resources as the major source of electricity, the optimization of the operation of the hydroelectric plants is extremely important and it’s being studied recurrently. Adopting a known temporal decomposition model of this optimization problem, this dissertation is proposed to compare the best multiobjective algorithms of the current literature, applying them to the medium term planning of hydroelectric plants. After several experiments, two algorithms are selected as the best options. / Para um país como o Brasil, que tem seus recursos hídricos como maior fonte de geração de energia elétrica, a otimização da operação das usinas hidrelétricas é extremamente importante e vem sendo estudada de maneira recorrente. Adotando um conhecido modelo de decomposição temporal desse problema de otimização, esta dissertação propôe-se a realizar uma comparação entre os melhores algoritmos de otimização multiobjetivo da literatura atual, aplicado-os ao planejamento de médio prazo de usinas hidrelétricas. Após diversos experimentos realizados, dois algoritmos são selecionados como as melhores opções.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-10T18:30:25Z No. of bitstreams: 1 anaameliadesouzapereira.pdf: 7981340 bytes, checksum: 0446c7b651ada497c790051f8b213d35 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-13T19:24:03Z (GMT) No. of bitstreams: 1 anaameliadesouzapereira.pdf: 7981340 bytes, checksum: 0446c7b651ada497c790051f8b213d35 (MD5) / Made available in DSpace on 2017-03-13T19:24:03Z (GMT). No. of bitstreams: 1 anaameliadesouzapereira.pdf: 7981340 bytes, checksum: 0446c7b651ada497c790051f8b213d35 (MD5) Previous issue date: 2016-09-26 / O sequenciamento da produção é um processo importante de tomada de decisão usado nas indústrias a fim de alocar tarefas aos recursos. Dada a relevância desse tipo de problema, a pesquisa em programação da produção faz-se necessária. Este trabalho envolve o processo de otimização nos seguintes problemas: máquina única, máquinas paralelas idênticas, máquinas paralelas idênticas com release time, máquinas paralelas não relacionadas com setup time dependente da sequência e das máquinas, e flow shop flexível com setup time dependente da sequência e dos estágios. Além disso, múltiplos e conflitantes objetivos devem ser otimizados ao mesmo tempo na programação de produção, e a literatura vem mostrando avanço nesse sentido. O presente trabalho analisa os objetivos comumente adotados e propõe um conjunto de pares de objetivos. Análise de correlação e árvore de agregação são utilizadas aqui para indicar as possibilidades de agregação entre os objetivos conflitantes. Meta-heurísticas são comumente adotadas para resolver os problemas de escalonamento abordados neste trabalho e duas delas, o Non-dominated Sorting Genetic Algorithm II (NSGA-II) e a Presa Predador (PP), são aplicados aos problemas multiobjetivo propostos a fim de estudar suas adequações aos novos casos. O NSGA-II é um dos Algoritmos Genéticos mais utilizados em problemas de escalonamento. A PP é uma abordagem evolutiva recente para problemas de programação da produção, cada predador é responsável por tratar um único objetivo. Uma generalização para a técnica PP em que os predadores consideram de forma ponderada ambos os objetivos é também proposta. Adicionalmente, a influência da adoção de busca local sobre essas técnicas é analisada. Experimentos computacionais adotando hipervolume como métrica de desempenho foram conduzidos visando avaliar as técnicas computacionais consideradas neste trabalho e suas variantes. / The sequencing of the production is an important process in decision-making and it is used in industries in order to allocate tasks to resources. Given the relevance of this kind of problem, the research in production scheduling is necessary. This study involves the process of optimization in the following problems: single machines, parallel identical machines, parallel identical machines with release time, unrelated parallel machines with setup time dependent on the sequence and on the machines, and flow shop which is flexible with setup time dependent on the sequence and stages. Moreover, multiple and conflicting objectives must be optimized at the same time in production scheduling and the literature has been showing progress in this sense. The present study analyses the commonly adopted objectives and suggests a set of objective pairs. Correlation analysis and aggregation trees are used here to indicate possibilities of aggregation among the conflicting objectives. Metaheuristics are commonly used to solve the sequencing problems addressed in this study and two of them, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Predator-Prey(PP), are applied to the proposed multiobjective problems in order to study their adjustments to the new cases. The NSGA-II is one of the most used genetic algorithms in sequencing problems. The PP is a recent evolutionary approach to scheduling problems, where each Predator is responsible for dealing with just one objective. A generalization of the PP technique, in which Predators considered both objectives using weights, is also proposed. In addition, the influence of the adoption of local search on these techniques is analyzed. Computational experiments adopting the hypervolume as a performance measure were conducted aiming at evaluating the computational techniques considered in this study and their variants.

[pt] A otimização em esportes é uma área que reúne diversas aplicações relacionadas ao planejamento e gestão de atividades esportivas. Diversas técnicas de otimização combinatória têm sido aplicadas, por exemplo, à  construção de tabelas de torneios e à  análise do desempenho de equipes em competições. Um problema que surge no contexto da organização de competições esportivas consiste na determinação de quais árbitros atuarão em cada partida de um determinado torneio. Diversas regras devem ser observadas no processo de atribuição de árbitros, que em geral envolve também a consideração de vários objetivos. Esta tese tem como objetivo principal apresentar um estudo sobre um problema de atribuição de árbitros, comum a várias ligas esportivas amadoras. Demonstra-se que a versão de decisão do problema estudado é um problema NP-completo. Considera-se inicialmente duas variantes mono-objetivo do PAA, que diferem uma da outra pela função objetivo adotada. Propõe-se modelos de programação linear inteira que permitem uma abordagem exata para a resolução de instâncias de pequeno e médio portes. Com o intuito de tratar instâncias de tamanho real, propõe-se também abordagens aproximadas de resolução baseadas na metaheurí­stica Iterated Local Search (ILS). Uma vez que o PAA tem origem em aplicações reais, ligadas a processos de tomada de decisões, é natural que envolva a consideração de diversos objetivos, muitas vezes em conflito. Tal fato motivou a investigação do uso de técnicas de otimização multi-critério que possam ser utilizadas na construção de um sistema de suporte a decisão e aplicadas a uma variante bi-objetivo do PAA, que considera simultaneamente as duas funções objetivo utilizadas nas variantes mono-objetivo estudadas. Abordagens de resolução exata e aproximada para esta variante bi-objetivo são propostas e seus resultados discutidos. / [en] Optimization in sports is a field of increasing interest. Combinatorial optimization techniques have been applied e.g. to game scheduling and playoff elimination. A problem that arises in competition management is the assignment of referees to games already scheduled. There are a number of rules and objectives that should be taken into account when referees are assigned to games. We address two mono-objective versions of a Referee Assignment Problem (RAP) common to many amateur leagues of sports such as soccer, baseball, and basketball. The problem is formulated by integer programming and its decision version is proved to be NP-complete. To tackle real-life large instances of the RAP, we propose a three-phase heuristic approach based on a constructive procedure, a repair heuristic to make solutions feasible, and a local search heuristic to improve feasible solutions, based on the metaheuristic iterated local search. Numerical results on realistic instances are presented and discussed. This work also investigates the solution of a bi-objective version of the RAP, which combines both objective functions used in the mono-objective versions. Exact and heuristic approaches are proposed to solve this bi-objective version and its computational results are discussed.

Les travaux de recherche présentés dans ce mémoire s'inscrivent dans la lignée des approches de modélisation hydrologiques prédictives dirigées par les données. Nous avons particulièrement développé leur application sur le contexte difficile des phénomènes de crue éclairs caractéristiques des bassins versants de la région Caraïbe qui pose un dé fi sé.curi taire. En envisageant le problème de la prévision de crues comme un problème d'optimisation combinatoire difficile nous proposons d'utiliser la notion de métaneuristiques, à travers les algorithmes évolutionnaire notamment pour leur capacité à parcourir efficacement de grands espaces de recherche et fi fournir des solutions de bOlIDe qualité en des temps d'exécution raisonnables. Nous avons présenté l'approche de prédiction AV2D : Aggregate Variable Data Driven dom le concept central est la notion de variable agrégée. L'idée sous-jacente à ce concept est de considérer le pouvoir prédictif de nouvelles variables définies comme le résultat de fonctions tatistiques, dites d'agrégation calculées sur de donnée' correspondant à des périodes de temps précédent uo événem nt à prédire. Ces variable sont caractérisées par des ensembles de paramètres correspondant a leur pJ:opriétés. Nous avons imroduitle variables agrégées hydrométéorologiques permettant de répondre au problème de la classification d événements hydrologiques. La complexité du parcours de l'espace de recherche engendré par les paramètres définissant ces variables a été prise en compte grâce à la njse en oeuvre d'un algorithme évolutionnaire particulier dont les composants ont été spécifiquement définis pour ce problème. Nous avons montré, à travers une étude comparative avec d'autres approches de modélisation dirigées par les données, menée sur deux cas d'études de bassins versant caribéens, que l'approche AV2D est particulièrement bien adaptée à leur contexte. Nous étudions par la suite les bénéfices offerts par les approches de modélisation hydrologiques modulaires dirigées par les données, en définissant un procédé de division en sous-processus prenant en compte les caractéristiques paniculières des bassins versants auxquels nous nous intéressons. Nou avons proposé une extension des travaux précédents à travers la définition d'une approche de modélisation modulaire M2D: Spatial Modular Data Driven, consistant à considérer des sous-processus en divisant l'ensemble des exemples à classifier en sous-ensembles correspondant à des comportements hydrologiques homogènes. Nous avons montré à travers une étude comparative avec d autres approches dU'igées par les données mises en oeuvre sur les mêmes sous-ensembles de données que celte approche permet d améliorer les résultats de prédiction particulièrement à coun Lenne. Nous avons enfin proposé la modélisation d un outil de pi / The work presented in this thesis is in the area of data-driven hydrological modeling approaches. We particularly investigared their application on the difficult problem of flash flood phenomena typically observed in Caribbean watersheds. By considering the problem of flood prediction as a combinatorial optimization problem, we propose to use the notion of Oleraheuristics, through evolutionary algorithms, especially for their capacity ta visit effjciently large search space and to provide good solutions in reasonable execution times. We proposed the hydrological prediction approach AV2D: Aggregate Variable Data Driven which central concept is the notion of aggregate variable. The underlying idea of this [concept is to consider the predictive power of new variables defined as the results of statistical functions, called aggregation functions, computed on data corresponding ta time periods before an event ta predict. These variables are characterized by sets of parameters corresponding ta their specifications. We introduced hydro-meteorological aggregate variables allowing ta address the classification problem of hydrological events. We showed through a comparative study on two typical caribbean watersheds, using several common data driven modelling techniques that the AV2D approach is panicul.rly weil fitted ta the studied context. We also study the benefits offered by modulaI' approaches through the definition of the SM2D: Spatial Modular DataDriven approach, consisting in considering sub-processes partly defined by spatial criteria. We showed that the results obtained by the AV2D on these sub-processes allows to increase the performances particularly for short term prediction. Finally we proposed the modelization of a generic control tool for hydro-meteorological prediction systems, H2FCT: Hydro-meteorological Flood Forecasting Control 1'001

De nombreux problèmes de décision issus du monde réel sont de nature NP-difficile. Il est également fréquent que de tels problèmes rassemblent plusieurs objectifs à optimiser simultanément, généralement contradictoires entre eux. Pour aborder cette classe de problèmes, les métaheuristiques multiobjectifs fournissent des outils particulièrement efficaces. Par ailleurs, pour traiter des problèmes de transport, l'élaboration de modèles permettant de caractériser l’évolution spatio-temporelle d’une population est un élément essentiel. Dans le cadre de ces travaux, nous nous intéressons à la chaine complète qui permet de guider une décision dans le domaine de l'aménagement du territoire et du transport. Nous considérons ainsi les deux principales phases impliquées dans le processus de décision : la modélisation des déplacements de la population d'une part, et l'élaboration d'une métaheuristique hybride pour résoudre des problèmes d'optimisation multiobjectif d'autre part. Afin de modéliser l’évolution de la présence de personnes sur un territoire, nous proposons dans cette thèse un nouveau modèle de mobilité. L'originalité de ce travail réside dans l'utilisation de données nouvelles issues de la téléphonie mobile, ainsi que dans l'exploitation d'informations géographiques et socio-économiques pour caractériser le pouvoir d'attraction du territoire. Nous proposons par ailleurs une heuristique pour résoudre des problèmes multiobjectifs. L’étude de l'influence de différents opérateurs sur la construction de l'ensemble Pareto, nous a amené à concevoir une heuristique hybride de type mémétique, qui se révèle être significativement plus efficace que des approches de référence. Les deux principales phases, modélisation et optimisation, ont été expérimentées et validées dans un contexte réel. Elles ont donné lieu au développement d’une plate-forme logicielle d’aide à la décision utilisée notamment pour proposer des emplacements de stations pour un service d'auto-partage électrique. / Many decision-making problems in the real world are NP-hard. These problems commonly feature several mutually-contradictory objectives to be optimized simultaneously. Multiobjective metaheuristics provide particularly effective means of addressing this class of problems. Moreover, for transportation problems, the development of models able to evaluate the spatiotemporal evolution of a population is essential. In our research, we are interested in the complete chain guiding a decision in the fields of transportation and territory planning. We consider the two main phases involved in the decision-making process: building a population mobility model and developing a hybrid metaheuristic to solve multiobjective optimization problems. In order to compute the evolution of population presence on a territory, in this thesis we propose a new mobility model; its originality lies in employing new data from mobile phone networks as well as geographic and socio-economic information to indicate the attractiveness of the territory. We have also developed a heuristic to solve multiobjective problems: following the study of the influence of several operators on the Pareto front, we have designed a hybrid memetic heuristic that is significantly more effective than reference approaches. The two main phases of modelling and optimizing have been tested and validated in a real context, allowing us to develop a decision-making software platform that can be used to provide station locations for an electric car-sharing service.

The operational scheduling at coal handling facilities is of the utmost importance to ensure that the coal consuming processes are supplied with a constant feed of good quality coal. Although the Sasol Coal Handling Facility (CHF) were not designed to perform coal blending during the coal handling process, CHF has to blend the different sources to ensure that the quality of the feed supplied is of a stable nature. As a result, the operation of the plant has become an extremely complex process. Consequently, human intelligence is no longer sufficient to perform coal handling scheduling and therefore a scheduling model is required to ensure optimal plant operation and optimal downstream process performance. After various attempts to solve the scheduling model optimally, i.e. with exact solution methods, it was found that it is not possible to accurately model the complexities of CHF in such a way that the currently available exact solvers can solve it in an acceptable operational time. Various alternative solution approaches are compared, in terms of solution quality and execution speed, using a simplified version of the CHF scheduling problem. This investigation indicates that the Simulated Annealing (SA) metaheuristic is the most efficient solution method to provide approximate solutions. The metaheuristic solution approach allows one to model the typical sequential thoughts of a control room operator and sequential operating procedures. Thus far, these sequential rules could not be modelled in the simultaneous equation environment required for exact solution methods. An SA metaheuristic is developed to solve the practical scheduling model. A novel SA approach is applied where, instead of the actual solution being used for neighbourhood solution representation, the neighbours are indirectly represented by the rules used to generate neighbourhood solutions. It is also found that the initial temperature should not be a fixed value, but should be a multiple of the objective function value of the initial solution. An inverse arctan-based cooling schedule function outperforms traditional cooling schedules as it provides the required diversification and intensification behaviour of the SA. The scheduling model solves within 45 seconds and provides good, practically executable results. The metaheuristic approach to scheduling is therefore successful as the plant complexities and intricate operational philosophies can be accurately modelled using the sequential nature of programming languages and provides good approximate optimal solutions in a short solution time. Tests done with live CHF data indicate that the metaheuristic solution outperforms the current scheduling methodologies applied in the business. The implementation of the scheduler will lead to a more stable factory feed, which will increase production yields and therefore increase company profits. By reducing the amount of coal re-handling (in terms of throw-outs and load-backs at mine bunkers), the scheduler will reduce the coal handling facility’s annual operating cost by approximately R4.6 million (ZAR). Furthermore, the approaches discussed in this document can be applied to any continuous product scheduling environment. Additional information available on a CD stored at Level 3 of the Merensky Library. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted