A presente pesquisa aborda um problema de roteirização e programação de veículos incorporando uma nova restrição operacional: a requisição simultânea de múltiplos veículos para atendimento da demanda. Trata-se de uma característica encontrada em operações de apoio à exploração de petróleo \"offshore\", em que mais de uma embarcação é requerida para executar tarefas de reboque e lançamento de linhas de ancoragem. Esta imposição, somada às restrições de janela de tempo, precedência entre tarefas, autonomia das embarcações e atendimento integral da demanda, configuram este problema. A programação é orientada pela minimização dos custos variáveis da operação e dos custos associados ao nível de serviço no atendimento. Este problema é uma variação do problema clássico de roteirização e programação de veículos com janela de tempo, de classe NP-Difícil. Nesta pesquisa, propõe-se modelar e resolver o problema em escala real por meio do algoritmo \"branch and cut\" acoplado às heurísticas de busca em vizinhança \"local branching\" e \"variable neighborhood search\". Para gerar as soluções iniciais será empregado o método \"feasibility pump\" e uma heurística construtiva. / This research focuses a fleet scheduling problem with new operational constraints: each task requiring multiple types of vehicles simultaneously. This kind of operation occurs in offshore exploitation and production sites, when more than one vessel is needed to accomplish the tugging and mooring of oil platforms. Other constraints are maintained such as time windows, precedence between tasks, route duration and the demand attendance. The solution schedules are cost oriented, which encompasses the routing variable costs and the customer service costs. This is a variation of the classical fleet routing and scheduling, which is an NP-Hard problem. This research aims to solve the real scale problem through a combined use of branch and cut strategy with local search algorithms such as local branching and variable neighborhood search. An efficient heuristic rule will be used in order to generate initial solutions using the feasibility pump method.

Cette thèse porte sur la résolution exacte et heuristique de plusieurs problèmes ayant des applications dans le domaine de l'Informatique dématérialisé (cloud computing). L'Informatique dématérialisée est un domaine en plein extension qui consiste à mutualiser les machines/serveurs en définissant des machines virtuelles représentant des fractions des machines/serveurs. Il est nécessaire d'apporter des solutions algorithmiques performantes en termes de temps de calcul et de qualité des solutions. Dans cette thèse, nous nous sommes intéressés dans un premier temps au problème d'ordonnancement sur plusieurs machines (les machines virtuelles) avec contraintes de précédence, c.-à-d., que certaines tâches ne peuvent s'exécuter que si d'autres sont déjà finies. Ces contraintes représentent une subdivision des tâches en sous tâches pouvant s'exécuter sur plusieurs machines virtuelles. Nous avons proposé plusieurs algorithmes génétiques permettant de trouver rapidement une bonne solution réalisable. Nous les avons comparés avec les meilleurs algorithmes génétiques de la littérature et avons défini les types d'instances où les solutions trouvées sont meilleures avec notre algorithme. Dans un deuxième temps, nous avons modélisé ce problème à l'aide de la programmation linéaire en nombres entiers permettant de résoudre à l'optimum les plus petites instances. Nous avons proposé de nouvelles inégalités valides permettant d'améliorer les performances de notre modèle. Nous avons aussi comparé cette modélisation avec plusieurs formulations trouvées dans la littérature. Dans un troisième temps, nous avons analysé de manière approfondie la sous-structure du sous-graphe d'intervalle ne possédant pas de clique de taille donnée. Nous avons étudié le polytope associé à cette sous-structure et nous avons montré que les facettes que nous avons trouvées sont valides pour le problème d'ordonnancement sur plusieurs machines avec contraintes de précédence mais elles le sont aussi pour tout problème d'ordonnancement sur plusieurs machines. Nous avons étendu la modélisation permettant de résoudre le précédent problème afin de résoudre le problème d'ordonnancement sur plusieurs machines avec des contraintes disjonctives entre les tâches, c.-à-d., que certaines tâches ne peuvent s'exécuter en même temps que d'autres. Ces contraintes représentent le partage de ressources critiques ne pouvant pas être utilisées par plusieurs tâches. Nous avons proposé des algorithmes de séparation afin d'insérer de manière dynamique nos facettes dans la résolution du problème puis avons développé un algorithme de type Branch-and-Cut. Nous avons analysé les résultats obtenus afin de déterminer les inégalités les plus intéressantes afin de résoudre ce problème. Enfin dans le dernier chapitre, nous nous sommes intéressés au problème d'ordonnancement d'atelier généralisé ainsi que la version plus classique d'ordonnancement d'atelier (open shop). En effet, le problème d'ordonnancement d'atelier généralisé est aussi un cas particulier du problème d'ordonnancement sur plusieurs machines avec des contraintes disjonctives entre les tâches. Nous avons proposé une formulation à l'aide de la programmation mathématique pour résoudre ces deux problèmes et nous avons proposé plusieurs familles d'inégalités valides permettant d'améliorer les performances de notre algorithme. Nous avons aussi pu utiliser les contraintes définies précédemment afin d'améliorer les performances pour le problème d'ordonnancement d'atelier généralisé. Nous avons fini par tester notre modèle amélioré sur les instances classiques de la littérature pour le problème d'ordonnancement d'atelier. Nous obtenons de bons résultats permettant d'être plus rapide sur certaines instances / The Cloud Computing appears as a strong concept to share costs and resources related to the use of end-users. As a consequence, several related models exist and are widely used (IaaS, PaaS, SaaS. . .). In this context, our research focused on the design of new methodologies and algorithms to optimize performances using the scheduling and combinatorial theories. We were interested in the performance optimization of a Cloud Computing environment where the resources are heterogeneous (operators, machines, processors...) but limited. Several scheduling problems have been addressed in this thesis. Our objective was to build advanced algorithms by taking into account all these additional specificities of such an environment and by ensuring the performance of solutions. Generally, the scheduling function consists in organizing activities in a specific system imposing some rules to respect. The scheduling problems are essential in the management of projects, but also for a wide set of real systems (telecommunication, computer science, transportation, production...). More generally, solving a scheduling problem can be reduced to the organization and the synchronization of a set of activities (jobs or tasks) by exploiting the available capacities (resources). This execution has to respect different technical rules (constraints) and to provide the maximum of effectiveness (according to a set of criteria). Most of these problems belong to the NP-Hard problems class for which the majority of computer scientists do not expect the existence of a polynomial exact algorithm unless P=NP. Thus, the study of these problems is particularly interesting at the scientific level in addition to their high practical relevance. In particular, we aimed to build new efficient combinatorial methods for solving parallel-machine scheduling problems where resources have different speeds and tasks are linked by precedence constraints. In our work we studied two methodological approaches to solve the problem under the consideration : exact and meta-heuristic methods. We studied three scheduling problems, where the problem of task scheduling in cloud environment can be generalized as unrelated parallel machines, and open shop scheduling problem with different constraints. For solving the problem of unrelated parallel machines with precedence constraints, we proposed a novel genetic-based task scheduling algorithms in order to minimize maximum completion time (makespan). These algorithms combined the genetic algorithm approach with different techniques and batching rules such as list scheduling (LS) and earliest completion time (ECT). We reviewed, evaluated and compared the proposed algorithms against one of the well-known genetic algorithms available in the literature, which has been proposed for the task scheduling problem on heterogeneous computing systems. Moreover, this comparison has been extended to an existing greedy search method, and to an exact formulation based on basic integer linear programming. The proposed genetic algorithms show a good performance dominating the evaluated methods in terms of problems' sizes and time complexity for large benchmark sets of instances. We also extended three existing mathematical formulations to derive an exact solution for this problem. These mathematical formulations were validated and compared to each other by extensive computational experiments. Moreover, we proposed an integer linear programming formulations for solving unrelated parallel machine scheduling with precedence/disjunctive constraints, this model based on the intervaland m-clique free graphs with an exponential number of constraints. We developed a Branch-and-Cut algorithm, where the separation problems are based on graph algorithms. [...]

Sistemas dutoviários desempenham um papel fundamental na cadeia de suprimento da indústria de petróleo. Este tipo de sistema é responsável pelo transporte da maior parte do volume de petróleo e seus derivados. Sistemas de dutos transportam uma grande quantidade de diferentes tipos de petróleo e seus derivados a custo mais baixo que outros tipos de modais. Dutos interligam campos de produção de petróleo, portos, refinarias, centros de distribuição (ou depósitos), e mercado consumidor. O problema estudado neste trabalho é baseado em um sistema que é composto por uma refinaria que pode transferir vários produtos para um terminal (depósito) através de um único duto. Os produtos são conjuntos de derivados de petróleo que devem ser transferidos da refinaria para o terminal ou do terminal para a refinaria. Ambos, refinaria e terminal estão conectados a outras refinarias, terminais e mercados consumidores e com isto formam uma complexa rede de dutos. Por outro lado há um conjunto de demandas externas e internas. Esta última demanda decorre da necessidade de processamento de produtos intermediários que são misturas compostas de várias correntes intermediárias, tais como diluentes de óleos combustíveis, propano intermediário, e diesel intermediário. Com o objetivo de obter vantagens sobre a estrutura da rede de transporte, torna-se benéfica e mesmo necessária a operação do duto em ambas as direções para atender tanto à demanda externa quanto à interna. O objetivo deste trabalho é desenvolver um modelo matemático para a programação de um sistema de poliduto. A formulação para a programação deve considerar a possibilidade de trocar o sentido do poliduto. Neste contexto, a programação de um poliduto envolve decisões tais como sentido de operação, quantidade, temporização e seqüências de produtos, com objetivo de obter uma solução ótima, considerando todas as restrições de demanda, perfil de produção, estoques e custos. O modelo de programação é baseado em uma representação de tempo discreto e composto da área de tancagem da refinaria, um terminal, e um poliduto. Além disto o duto é dividido em segmentos de volumes iguais como em Rejowski Jr e Pinto (2003). As principais variáveis de decisão são a direção da movimentação do duto (da refinaria para terminal ou do terminal para refinaria) e o que está sendo movimentado a cada intervalo. Estas decisões são formuladas através de uma representação disjuntiva. As disjunções são transformadas em uma formulação baseada em programação matemática mista-inteira, a partir da representação Convex-hull. A função objetivo considera os custos de estocagem, movimentação e interface de produtos. O modelo é aplicado inicialmente a um caso protótipo e posteriormente aplicado a um sistema real composto pelos terminais de São Sebastião e Guararema e o poliduto OSPLAN. Neste caso ao todo quatro famílias de produtos são transportadas: gasolina, querosene, nafta e diesel. A programação é gerada para o período de uma semana. / Pipeline systems play a major role in the supply chain of the petroleum industry. These systems are responsible for the transportation of most of the crude oil and petroleum derivatives. Pipeline systems transfer large amounts of different petroleum types and their products at a lower cost than any other transportation mode. Pipelines interconnect oil fields, ports, refineries, distribution centers (or depots), and consumer markets. The problem addressed is this work is based on a system that is composed by an oil refinery that must transfer multiple products through a single pipeline connected to one depot. The products are a set of petroleum derivatives that must be either transported from the refinery to the depot or from the depot to the refinery. Both depot and refinery also connect other refineries as well as other depots and customers, thus forming a complex transportation network. On the other hand, there are several demands that arise either from external customers or from refineries. The latter demand is due from the need of processing intermediate streams with components mixtures such as diluents, propane and diesel. In order to take advantage of the structure of the transportation network, it becomes beneficial and even necessary to operate the pipeline in both directions so that internal and external demands are satisfied. The objective of this work is to develop a mathematical model for the short term scheduling of a multiproduct pipeline system. The scheduling formulation must account for the bidirectionality of the multiproduct pipeline. In this context, the scheduling a multiproduct pipeline involves the from-to decision, the product amounts, their sequence and timing, in the optimal sense, considering all constrains on demands, production rates, inventories, and costs. The scheduling model is based on a discrete time representation and is composed by one refinery tank farm, one depot and one multiproduct pipeline. Moreover, the pipeline is divided into segments of equal volume, as in Rejowski Jr and Pinto (2003). The main decisions variables are the directions of transfer (refinery to depot or depot to refinery) and the types of products at each time interval. These decisions are formulated with a disjunctive representation. The disjunctions are represented in mixed integer formulation based on the convex-hull approach. The objective function involves inventory, transfer and product interface costs. The model is first applied to a prototype case and after applied to a real-world system that is composed of the São Sebastião and Guararema depot and the OSPLAN pipeline. Overall four families of products are transported: gasoline, kerosene, naphtha and oil diesel. These are scheduled over a period of one week.

A geração de quadros de horários escolares é um problema clássico de otimização que tem sido largamente estudado devido a sua importâncias prática e teórica. O problema consiste em alocar um conjunto de aulas entre professor-turma em períodos de tempo pré-determinados, satisfazendo diferentes tipos de requisitos. Devido a natureza combinatória do problema, a resolução de instâncias médias e grandes torna-se uma tarefa desafiadora. Quando recursos são escassos, mesmo uma solução factível pode ser difícil de ser encontrada. Várias técnicas tem sido propostas na literatura científica para resolver o problema de geração de quadros de horários escolares, no entanto, métodos robustos ainda não existem. Visto que o uso de métodos exatos, como por exemplo, técnicas de programação matemática, não podem ser utilizados na prática, para resolver instâncias grandes da realidade, meta-heurísticas e meta-heurísticas híbridas são usadas com frequência como abordagens de resolução. Nesta pequisa, são desenvolvidas técnicas que combinam programação matemática e heurísticas, denominadas mateheurísticas, para resolver de maneira eficiente e robusta algumas variações de problemas de geração de quadros de horários escolares. Embora neste trabalho sejam abordados problemas encontrados no contexto de instituições brasileiras, os métodos propostos também podem ser aplicados em problemas similares oriundo de outros países. / The school timetabling is a classic optimization problem that has been extensively studied due to its practical and theoretical importance. It consists in scheduling a set of class-teacher meetings in a prefixed period of time, satisfying requirements of different types. Given the combinatorial nature of this problem, solving medium and large instances of timetabling to optimality is a challenging task. When resources are tight, it is often difficult to find even a feasible solution. Several techniques have been developed in the scientific literature to tackle the high school timetabling problem, however, robust solvers do not exist yet. Since the use of exact methods, such as mathematical programming techniques, is considered impracticable to solve large real world instances, metaheuristics and hybrid metaheuristics are the most used solution approaches. In this research we develop techniques that combine mathematical programming and heuristics, so-called matheuristics, to solve efficiently and in a robust way some variants of the high school timetabling problem. Although we pay special attention to problems arising in Brazilian institutions, the proposed methods can also be applied to problems from different countries.

Deposição é um fenômeno indesejável que ocorre na superfície dos trocadores de calor ao longo de sua operação, ocasionando redução na efetividade térmica e aumento da resistência ao escoamento nestes equipamentos. Estes efeitos trazem grandes consequências econômicas e ambientais, devido ao aumento dos custos operacionais (energia adicional é requerida), aumento dos custos de projeto (demanda por equipamentos de maior área de troca térmica), limitações hidráulicas (que pode levar a uma diminuição da carga processada) e aumento das emissões (aumento da queima de combustíveis fósseis para suprir a energia adicional requerida). Neste contexto, o presente trabalho tem por objetivo fornecer ferramentas computacionais robustas que apliquem técnicas de otimização para o gerenciamento da deposição em redes de trocadores de calor, visando minimizar os seus efeitos negativos. Estas ferramentas foram desenvolvidas utilizando programação matemática no ambiente computacional GAMS, e três abordagens distintas para a resolução do problema da deposição foram pesquisadas. Uma delas consiste na identificação do conjunto ótimo de trocadores de calor a serem limpos durante uma parada para manutenção da planta, visando restaurar a carga térmica nesses equipamentos através da remoção dos depósitos existentes. Já as duas outras abordagens consistem em otimizar a distribuição das vazões das correntes ao longo de ramais paralelos, uma de forma estacionária e a outra de forma dinâmica, visando maximizar a recuperação de energia ao longo da rede. O desempenho destas três abordagens é ilustrado através de um conjunto de exemplos de redes de trocadores de calor, onde os ganhos reais obtidos com estas ferramentas de otimização desenvolvidas são demonstrados / Fouling is an undesirable phenomenon that occurs over the surface of heat exchangers during its operation, causing reduction of thermal effectiveness and increase of flow resistance along these equipment. These effects bring large economics and environmental consequences, due to the increase of operational costs (additional energy is required), increase of project costs (demand of equipment with larger thermal exchange areas), hydraulic limitations (that can diminish the process throughput) and increase of emissions (increase of fossil fuel firing to supply the additional energy required). In this context, the objective of this work is to provide robust computational tools that apply optimization techniques for fouling management on heat exchanger networks, aiming to reduce its negative effects. These tools were developed using mathematical programming on GAMS software, and three distinct approaches for the resolution of the fouling problem were investigated. One of them consists in the identification of the optimal set of heat exchangers that have to be cleaned during a plant maintenance shutdown, aiming to restore the thermal load in these equipment through the removal of the existent deposits. The other approaches consist in to optimize the distribution of flow rates of the streams along parallel branches, using stationary and dynamic models, in order to maximize the energy recovery in the network. The performance of these three approaches is illustrated through examples of heat exchanger networks, where the real gains obtained with these optimization tools are demonstrated

O planejamento tático da produção é importante para as organizações pois permite um correto dimensionamento dos recursos produtivos, para garantir um atendimento adequado da demanda, e influencia nas decisões de produção em médio prazo buscando soluções que colaborem positivamente no resultado operacional. O objetivo deste trabalho é o desenvolvimento de um modelo de planejamento agregado da produção para aplicação na indústria da carne suína que contemple simultaneamente as atividades finais da produção agropecuária e o ambiente de produção da indústria frigorífica. O modelo proposto contempla um sistema produtivo em dois estágios onde o primeiro estágio apresenta um fluxo divergente de produção envolvendo coprodução e o segundo estágio consiste em uma linha de montagem. O sistema apresentado é composto por uma sequência de rotinas de programação, utilizadas para a geração dos dados de entrada e um modelo matemático baseado em programação linear inteira mista cuja função objetivo é maximizar a margem global. As rotinas para geração de dados de entradas foram programadas em Visual Basic For Application e chamadas de Programa de Geração de Padrões. O modelo de programação matemática foi implementado no software LINGO e suas interfaces com as planilhas do Microsoft Excel. A aplicação do modelo para verificação utilizou dados adaptados de uma empresa envolvida no setor e os resultados obtidos permitiram testar a consistência do modelo para a situação específica. A análise dos resultados demonstrou que o modelo gera soluções que estão alinhadas com os objetivos da organização e responde adequadamente a variações nos dados de entrada. / The tactical planning activities are very important for an organization since it allows an anticipated administration of production resources in order to meet the demand and also because it suggests medium term production decisions that can contribute positively to the operational results of the company. This work aims to develop an aggregate production planning model for the pork industry which takes into consideration factors in the meat processing plant as well as in the final step of farming activities. The presented model approaches a two stage production system where the first stage is characterized by a divergent production flow involving coproduction and the second stange is an assemblage line. The entire system is composed by a sequence of routines used to generate some parameters and a mathematical formulation based on mixed integer linear programming in which the objective function aims to maximize the global margin of the organization. The routines used to generate the parameters where implemented in Visual Basic for Application and were called Pattern Generation Program and the mathematic programming were implemented in LINGO and its interfaces with worksheets of Microsoft Excel. The verification of the model used adapted data from a real company in this industry and could test its consistency for this specific situation. The analyzed results demonstrated that the model generates good solution that contribute to the global objective of the company and the model results response to the changes in the parameter as expected.

The difficulties in responding to variation in product order mixes and load levels effectively in make to order are known. Most of the existing approaches consider releasing jobs to the shop (input control), changing capacity levels (output control) in a controlled way, order acceptance with different definitions of work load and due date assignment. Controlling the processes, routing options and the order accepting capacity with various tool combinations that will decrease tool loading are not considered properly. However the manufacturing flexibility provided by the computer numerically controlled (CNC) machines, provides both part variety and due date achievement given a reasonable extra capacity. Positive effects of flexibility on the due date achievement of the make to order is shown with a variety of experimental and field studies leaving little doubt. However taking flexibility only as a strategic issue and not considering it as a means of planning and management in either the short term or medium term decisions have been commonplace practice. In this study, benefits of providing three kinds of flexibility, considering order pool and acceptance probability of the new arrivals in a periodic setting, is the focal issue. If the required flexible environment is provided, the necessity to make a detailed job loading, route planning and scheduling will be reduced to a low level and a high shop congestion and due date achievement will be realized simultaneously. A typical realistic shop with a scaled part mix is assumed in the flexibility management modeling and simulation experiments are conducted applying periodical flexibility planning approach. These experiments briefly support the ideas that worth of anticipation is more than plain expectations and flexibility improves robustness.

In the push for clean and renewable fuels, timber derived biomass is a promising frontier for biofuel production in the United States. This thesis approaches the established timberlands biofuel implementation problem with three different mathematical programming studies, each testing feasibility and sustainability in different economic and supply related situations. In the first study, a competitive game theory approach was utilized to provide new insights into the behavior within a timberlands supply chain. We utilized Stackelberg game theory modeled with bilevel programming to represent the competing harvesting and manufacturing sectors. In the second study, the initial bilevel model was utilized in a larger two stage multiperiod model with parameter uncertainty. In this more realistic model, the first stage contained logistical decisions around biorefinery investments, such as location and capacity, while the second stage was composed of multiple discrete bilevel scenarios representing potential situations in the timberlands system. The final study focused on long term land management strategies for the timberlands supply chain. Introduction of a new biorefinery investment meant that management strategies must be altered to ensure consistent material flows to manufacturers as well as sustain the new production facility. A modified cyclic scheduling formulation was used to model a timberlands system and its planting and harvesting schedule to accommodate a new biorefinery. This cyclic model added an initial startup period to initiate biofuel production and provide time to adapt land management. The overall contribution of these studies was to analyze a biorefinery's impact on the established behavior in a timberlands supply chain. In particular, the goals of these models were to develop introductory decision making tools for timberlands supply chain managers.

In this study, we consider the General Lot Sizing and Scheduling Problem in single level capacitated environments with sequence dependent item changeovers. Process industries may be regarded as suitable application areas of the problem. The focus on capacity utilization and intensively time consuming changeovers necessitate the integration of lot sizing and sequencing decisions in the production plan. We present a mathematical model which captures the essence of cases in the most generic and realistic setting of the problem. We discuss the impact and validity of some of the assumptions commonly encountered in the related literature. We also represent the problem using an alternative formulation and attempt to enhance the formulations with the use of some additional inequalities. Finally, we develop a heuristic by restricting the number of possible changeovers. Computational results are discussed.

A geração de quadros de horários escolares é um problema clássico de otimização que tem sido largamente estudado devido a sua importâncias prática e teórica. O problema consiste em alocar um conjunto de aulas entre professor-turma em períodos de tempo pré-determinados, satisfazendo diferentes tipos de requisitos. Devido a natureza combinatória do problema, a resolução de instâncias médias e grandes torna-se uma tarefa desafiadora. Quando recursos são escassos, mesmo uma solução factível pode ser difícil de ser encontrada. Várias técnicas tem sido propostas na literatura científica para resolver o problema de geração de quadros de horários escolares, no entanto, métodos robustos ainda não existem. Visto que o uso de métodos exatos, como por exemplo, técnicas de programação matemática, não podem ser utilizados na prática, para resolver instâncias grandes da realidade, meta-heurísticas e meta-heurísticas híbridas são usadas com frequência como abordagens de resolução. Nesta pequisa, são desenvolvidas técnicas que combinam programação matemática e heurísticas, denominadas mateheurísticas, para resolver de maneira eficiente e robusta algumas variações de problemas de geração de quadros de horários escolares. Embora neste trabalho sejam abordados problemas encontrados no contexto de instituições brasileiras, os métodos propostos também podem ser aplicados em problemas similares oriundo de outros países. / The school timetabling is a classic optimization problem that has been extensively studied due to its practical and theoretical importance. It consists in scheduling a set of class-teacher meetings in a prefixed period of time, satisfying requirements of different types. Given the combinatorial nature of this problem, solving medium and large instances of timetabling to optimality is a challenging task. When resources are tight, it is often difficult to find even a feasible solution. Several techniques have been developed in the scientific literature to tackle the high school timetabling problem, however, robust solvers do not exist yet. Since the use of exact methods, such as mathematical programming techniques, is considered impracticable to solve large real world instances, metaheuristics and hybrid metaheuristics are the most used solution approaches. In this research we develop techniques that combine mathematical programming and heuristics, so-called matheuristics, to solve efficiently and in a robust way some variants of the high school timetabling problem. Although we pay special attention to problems arising in Brazilian institutions, the proposed methods can also be applied to problems from different countries.