Segundo uma pesquisa recente realizada junto ao setor de fundições, uma importante preocupação do setor é melhorar seu planejamento de produção. Um plano de produção em uma fundição envolve duas etapas interdependentes: a determinação das ligas a serem fundidas e dos lotes que serão produzidos. Neste trabalho, estudamos o problema de dimensionamento de lotes para fundições de pequeno porte, cujo objetivo é determinar um plano de produção de mínimo custo. Como sugerido na literatura, a heurística proposta trata as etapas do problema de forma hierárquica: inicialmente são definidas as ligas e, posteriormente, os lotes que são produzidos a partir delas. Para a solução do problema, propomos um algoritmo genético que explora um conjunto de possibilidades para a determinação das ligas e utiliza uma heurística baseada em relaxação lagrangiana para determinação dos itens a serem produzidos. Além disso, uma abordagem para o mesmo problema é proposta utilizando o problema da mochila para determinar os itens a serem produzidos. Bons resultados foram obtidos pelos métodos propostos / According to a recent research made by the foundry sector, one of the most concern of the industry is to improve its production planning. A foundry production plan involves two independent stages: the determination of alloys to be merged and the lots that will be produced. In this work, we studied the lot-sizing problem for small foundries, whose purpose is to determine a plan of minimum production cost. As suggested in the literature, the heuristic proposed addresses the problem stages in a hierarchical way: rst we dene the alloys and, subsequently, the lots that are produced from them. We propose a genetic algorithm that explores some possible sets of alloys produced and uses a Lagrangian heuristic to determine the items to be produced. Also, we propose one approach to the same problem that uses the knapsack problem to determine the items to be produced. Good results were obtained by the methods proposed

Dans cette thèse, nous étudions deux problèmes de planification de production motivés par des applications du monde réel. Tout d'abord, un problème de planification de production pour un projet de fabrication de vêtements est étudié et un outil d'optimisation est développé pour le résoudre. Deuxièmement, une version restreinte du problème de dimensionnement du lot de capacité avec des configurations dépendantes de la séquence est explorée. Diverses formulations mathématiques sont développées et une analyse de complexité est effectuée pour donner une première analyse du problème. / In this thesis, we study two production planning problems motivated by challenging real-world applications. First, a production planning problem for an apparel manufacturing project is studied and an optimization tool is developed to tackle it. Second, a restricted version of the capacitated lot sizing problem with sequence dependent setups is explored. Various mathematical formulations are developed and complexity analysis is performed to offer a first glance to the problem.

Les travaux de cette thèse s'inscrivent dans le contexte de la coordination des décisions de planification survenant dans une chaîne logistique à deux acteurs: un fournisseur et un producteur souhaitant chacun diminuer leur propre coût. Les décisions de planification prises de manière indépendante par chaque acteur peuvent amener à une mauvaise performance de la chaîne logistique en terme de coûts, d'où la nécessité d'une coordination. Nous étudions des mécanismes de partage de coûts entre des acteurs en définissant des stratégies de coordination entre les acteurs par la mise en place de contrats. Nous considérons le cas où le producteur (resp. fournisseur) peut imposer son plan de production optimal au fournisseur (resp. distributeur). Différentes hypothèses de partage de coûts, ainsi que la problématique d'asymétrie d'information sont prises en compte dans ces travaux. Nous effectuons également des analyses expérimentales mesurant la diminution du coût de la chaîne logistique obtenue quand les acteurs coopèrent. Ce contexte nous amène à étudier de nouveaux problèmes de lot-sizing pour lesquels nous proposons une analyse de complexité et des algorithmes de programmation dynamique pour les résoudre. Nous proposons également une étude théorique des problèmes de lot-sizing à deux niveaux avec une capacité de stockage limitée. / This thesis focus on the coordination of planning decisions in a two-level supply chain composed of one supplier and one retailer. Each actor wants to minimize his own cost. The planning decisions independently took by the actors can lead to a poor performance in terms of costs, hence the necessity of coordination. We study cost sharing mechanisms between the actors by designing contracts. In this work, we consider the case where the retailer (resp. supplier) can impose his optimal production plan to the supplier (resp. retailer). Different cost sharing hypothesis, as well as the asymmetric information problem are taking into account in this thesis. We also perform an experimental analysis in order to evaluate the decrease of the supply chain cost obtained when the actors cooperate. This context leads us to study new lot-sizing problems for which we propose a complexity analysis and dynamic programming algorithms in order to solve them. We also propose a theoritical study of two-level lot-sizing problems with inventory bounds.

Cette thèse développe des méthodes d'optimisation pour la gestion de la Supply Chain et a pour thème central la flexibilité définie comme la capacité à fournir un service ou un produit au consommateur dans un environnement incertain. La recherche a été menée dans le cadre d'un partenariat entre Argon Consulting, une société indépendante de conseil en Supply Chain et l'École des Ponts ParisTech. Dans cette thèse, nous développons trois sujets rencontrés par Argon Consulting et ses clients et qui correspondent à trois différents niveaux de décision (long terme, moyen terme et court terme).Lorsque les entreprises élargissent leur portefeuille de produits, elles doivent décider dans quelles usines produire chaque article. Il s'agit d'une décision à long terme, car une fois qu'elle est prise, elle ne peut être facilement modifiée. Plus qu'un problème d'affectation où un article est produit par une seule usine, ce problème consiste à décider si certains articles doivent être produits par plusieurs usines et par lesquelles. Cette interrogation est motivée par la grande incertitude de la demande. En effet, pour satisfaire la demande, l'affectation doit pouvoir équilibrer la charge de travail entre les usines. Nous appelons ce problème le multi-sourcing de la production. Comme il ne s'agit pas d'un problème récurrent, il est essentiel de tenir compte du risque au moment de décider le niveau de multi-sourcing. Nous proposons un modèle générique qui inclut les contraintes techniques du problème et une contrainte d'aversion au risque basée sur des mesures de risque issues de la théorie financière. Nous développons un algorithme et une heuristique basés sur les outils standards de la Recherche Opérationnelle et de l'Optimisation Stochastique pour résoudre le problème du multi-sourcing et nous testons leur efficacité sur des données réelles.Avant de planifier la production, certains indicateurs macroscopiques doivent être décidés à horizon moyen terme tels la quantité de matières premières à commander ou la taille des lots produits. Certaines entreprises utilisent des modèles de stock en temps continu, mais ces modèles reposent souvent sur un compromis entre les coûts de stock et les coûts de lancement. Ces derniers sont des coûts fixes payés au lancement de la production et sont difficiles à estimer en pratique. En revanche, à horizon moyen terme, la flexibilité des moyens de production est déjà fixée et les entreprises estiment facilement le nombre maximal de lancements. Poussés par cette observation, nous proposons des extensions de certains modèles classiques de stock en temps continu, sans coût de lancement et avec une limite sur le nombre d'installations. Nous avons utilisé les outils standard de l'Optimisation Continue pour calculer les indicateurs macroscopiques optimaux.Enfin, la planification de la production est une décision à court terme qui consiste à décider quels articles doivent être produits par la ligne de production pendant la période en cours. Ce problème appartient à la classe bien étudiée des problèmes de Lot-Sizing. Comme pour les décisions à moyen terme, ces problèmes reposent souvent sur un compromis entre les coûts de stock et les coûts de lancement. Fondant notre modèle sur ces considérations industrielles, nous gardons le même point de vue (aucun coût de lancement et une borne supérieure sur le nombre de lancement) et proposons un nouveau modèle.Bien qu'il s'agisse de décisions à court terme, les décisions de production doivent tenir compte de la demande future, qui demeure incertaine. Nous résolvons notre problème de planification de la production à l'aide d'outils standard de Recherche Opérationnelle et d'Optimisation Stochastique, nous testons l'efficacité sur des données réelles et nous la comparons aux heuristiques utilisées par les clients d'Argon Consulting / This thesis develops optimization methods for Supply Chain Management and is focused on the flexibility defined as the ability to deliver a service or a product to a costumer in an uncertain environment. The research was conducted throughout a partnership between Argon Consulting, which is an independent consulting firm in Supply Chain Operations and the École des Ponts ParisTech. In this thesis, we explore three topics that are encountered by Argon Consulting and its clients and that correspond to three different levels of decision (long-term, mid-term and short-term).When companies expand their product portfolio, they must decide in which plants to produce each item. This is a long-term decision since once it is decided, it cannot be easily changed. More than a assignment problem where one item is produced by a single plant, this problem consists in deciding if some items should be produced on several plants and by which plants. This is motivated by a highly uncertain demand. So, in order to satisfy the demand, the assignment must be able to balance the workload between plants. We call this problem the multi-sourcing of production. Since it is not a repeated problem, it is essential to take into account the risk when making the multi-sourcing decision. We propose a generic model that includes the technical constraints of the assignment and a risk-averse constraint based on risk measures from financial theory. We develop an algorithm and a heuristic based on standard tools from Operations Research and Stochastic Optimization to solve the multi-sourcing problem and we test their efficiency on real datasets.Before planning the production, some macroscopic indicators must be decided at mid-term level such as the quantity of raw materials to order or the size of produced lots. Continuous-time inventory models are used by some companies but these models often rely on a trade-off between holding costs and setups costs. These latters are fixed costs paid when production is launched and are hard to estimate in practice. On the other hand, at mid-term level, flexibility of the means of production is already fixed and companies easily estimate the maximal number of setups. Motivated by this observation, we propose extensions of some classical continuous-time inventory models with no setup costs and with a bound on the number of setups. We used standard tools from Continuous Optimization to compute the optimal macroscopic indicators.Finally, planning the production is a short-term decision consisting in deciding which items must be produced by the assembly line during the current period. This problem belongs to the well-studied class of Lot-Sizing Problems. As for mid-term decisions, these problems often rely on a trade-off between holding and setup costs. Basing our model on industrial considerations, we keep the same point of view (no setup cost and a bound on the number of setups) and propose a new model. Although these are short-term decisions, production decisions must take future demand into account, which remains uncertain. We solve our production planning problem using standard tools from Operations Research and Stochastic Optimization, test the efficiency on real datasets, and compare it to heuristics used by Argon Consulting's clients

Cette thèse a pour objectif d'étudier l'utilisation de la programmation par contraintes pour développer un solveur de planification de production. Nous nous concentrons sur des problèmes de dimensionnement de lots de production (lot-sizing) qui sont des problèmes majeurs et difficiles de la planification de la production et profitons d'une des principales forces de la programmation par contraintes, à savoir les contraintes globales. Nous définissons une contrainte globale LotSizing qui s'appuie sur un problème générique de lot-sizing mono-produit à un seul niveau, qui tient compte des capacités de production et de stockage, des coûts unitaires de production et de stockage et des coûts fixes. Cette contrainte globale est un outil de modélisation intuitif pour les problèmes complexes de lot-sizing car elle permet de modéliser chaque nœud des réseaux de distribution. Nous utilisons des techniques de programmation dynamique classiques du lot-sizing pour développer des algorithmes de filtrage pour la contrainte globale. Nous modélisons également des problèmes multi-produits.Enfin, nous introduisons un nouvel algorithme de filtrage générique s'appuyant sur la programmation linéaire. Nous montrons que la cohérence d'arc pour les contraintes considérées peut être obtenue avec la résolution d'un seul programme linéaire lorsque la contrainte a une formulation idéale et nous généralisons le résultat pour faire du filtrage partiel lorsqu'aucune restriction n'est faite sur ces contraintes. Cette technique peut être pertinente lors de la résolution de sous-problèmes de flot ou de séquence sous-jacents au lot-sizing. / In this thesis we investigate the potential use of constraint programming to develop a production planning solver. We focus on lot-sizing problems that are crucial and challenging problems of the tactical level of production planning and use one of the main strengths of constraint programming, namely global constraints. The goal of this work is to set the grounds of a constraint programming framework for solving complex lot-sizing problems. We define a LotSizing global constraint based on a generic single-item, single-level lot-sizing problem that considers production and inventory capacities, unitary production and inventory costs and setup costs. This global constraint is an intuitive modeling tool for complex lot-sizing problems as it can model the nodes of lot-sizing networks. We use classical dynamic programming techniques of the lot-sizing field to develop powerful filtering algorithms for the global constraint. Furthermore we model multi-item problems that are natural extensions of the core problem.Finally we introduce a new generic filtering algorithm based on linear programming. We show that arc consistency can be achieved with only one call to a linear programming solver when the global constraint has an ideal formulation and adapt the result to provide partial filtering when no restriction is made on the constraints. This technique can be useful to tackle polynomial lot-sizing underlying flow and sequence sub-problems.

L'incertitude a été toujours présente dans les problèmes d'optimisation. Dans ce travail, nous nous intéressons aux problèmes d'optimisation multi-niveaux où l'incertitude apparaît très naturellement. Les problèmes d'optimisation multi-niveaux avec incertitude ont suscité un intérêt à la fois théorique et pratique. L'optimisation robuste fait partie des méthodes les plus étudiées pour traiter ces problèmes. En optimisation robuste, nous cherchons une solution qui optimise la fonction objective pour le pire scénario appartenant à un ensemble d'incertitude donné. Les problèmes d'optimisation robuste multi-niveaux sont difficiles à résoudre, même de façon heuristique. Dans cette thèse, nous abordons les problèmes d'optimisation robuste à travers le prisme des méthodes de décomposition. Ces méthodes décomposent le problème en un problème maître (MP) et plusieurs problèmes satellites de séparation (AP). Dans ce contexte, les solutions et les relaxations heuristiques ont une importance particulière. Même pour les problèmes d'optimisation combinatoires, les relaxations sont importantes pour analyser l'écart de l'optimalité des solutions heuristiques. Un autre aspect important est l'utilisation des heuristiques comme integrés dans une méthode exacte. Les principales contributions de ce travail sont les suivantes. Premièrement, nous proposons une nouvelle relaxation pour les problèmes multi-niveaux basée sur l’approche dite d’information parfaite dans le domaine de l’optimisation stochastique. L'idée principale derrière cette méthode est d'éliminer les contraintes de non anticipativité du modèle pour obtenir un problème plus simple. Nous pouvons ensuite fournir des algorithmes combinatoires ad-hoc et des formulations de programmation mixte en nombres entiers compactes pour ce problème. Deuxièmement, nous proposons de nouveaux algorithmes de programmation dynamique pour résoudre les problèmes satellites apparaissant dans une classe spécifique de problèmes robustes pour un ensemble d'incertitude de type budget. Ce type d'incertitude est basé sur le nombre maximum d'écarts autorisés et leur taille. Ces algorithmes peuvent être appliqués à des problèmes de lot-sizing et à des problèmes de tournées de véhicules. Enfin, nous proposons un modèle robuste pour un problème lié à l’installation équitable de capteurs. Ce modèle fait le lien entre l'optimisation robuste et l'optimisation stochastique avec contraintes probabilistes ambigües. / Uncertainty has always been present in optimization problems, and it arises even more severely in multistage optimization problems. Multistage optimization problems underuncertainty have attracted interest from both the theoretical and the practical level.Robust optimization stands among the most established methodologies for dealing with such problems. In robust optimization, we look for a solution that optimizes the objective function for the worst possible scenario, in a given uncertainty set. Robust multi-stage optimization problems are hard to solve even heuristically. In this thesis, we address robust optimization problems through the lens of decompositions methods. These methods are based on the decomposition of the robust problem into a master problem (MP) and several adversarial separation problems (APs). The master problem contains the original robust constraints, however, written only for finite numbers of scenarios. Additional scenarios are generated on the y by solving the APs. In this context, heuristic solutions and relaxations have a particular importance. Similarly to combinatorial optimization problems, relaxations are important to analyze the optimality gap of heuristic solutions. Heuristic solutions represent a substantial gain from the computational viewpoint, especially when used to solve the separation problem. Because the adversarial problems must be solved several times, good heuristic solution may avoid the exact solution of the APs. The main contributions of this work are three-fold. First, we propose a new relaxation for multi-stage problems based on the approach named perfect information in the field of stochastic optimization. The main idea behind this method is to remove nonanticipativity constraints from the model to obtain a simpler problem for which we can provide ad-hoc combinatorial algorithms and compact mixed integer programming formulations. Second, we propose new dynamic programming algorithms to solve the APs for robust problems involving budgeted uncertainty, which are based on the maximum number of deviations allowed and on the size of the deviations. These algorithms can be applied to lot-sizing problems and vehicle routing problems among others. Finally, we study the robust equitable sensor location problem. We make the connection between the robust optimization and the stochastic programming with ambiguous probabilistic constraints. We propose linear models for several variants of the problem together withnumerical results.

En période de crise financière, les entreprises ont besoin de trésorerie pour réagir efficacement aux aléas et assurer leur solvabilité. Cette thèse se situe à l’interface entre l’opérationnel et la finance pour développer des modèles de planification tactique gérant simultanément les flux physiques et financiers dans la supply chain. Le coût de financement des opérations basé sur le besoin en fond de roulement (BFR) est intégré comme un nouvel aspect financier jamais considéré dans la littérature de lot-sizing. Nous débutons par une extension du modèle EOQ considérant les coûts de financement du BFR. L’objectif est la maximisation du profit. Une quantité de production optimale est obtenue analytiquement ainsi que l’analyse de la sensibilité du modèle. De plus, les comparaisons avec le modèle EOQ et un modèle qui considère le coût du capital sont étudiées. Ensuite, un modèle basé sur un lot-sizing dynamique est établi. La propriété ZIO est démontrée et permet l’utilisation d’un algorithme en temps polynomial. Enfin un scénario multi-niveau à capacité infini est étudié avec une approche séquentielle puis centralisée. La propriété ZIO est prouvée dans ces deux cas. Des algorithmes de programmation dynamique sont utilisés pour obtenir une solution optimale. Cette thèse peut être considérée comme un premier, mais significatif, travail combinant la planification de production et la gestion du besoin en fond de roulement dans des modèles de planification tactique. Nous montrons que les aspects financiers ont un impact significatif sur les plans de production. Les cas étudiés dans cette thèse peuvent être considérés comme des sous-problèmes dans l’étude de scénario plus réalistes. / In financial crisis, companies always need free cash flow to efficiently react to any uncertainties to ensure solvency. Thus, this thesis serves as an interface between operations and finance to develop tactical production planning models for joint management of physical and financial flows in the supply chain. In these models, the financing cost of operation-based working capital requirement (WCR) is integrated as a new financial aspect never before considered in the lot-sizing literature. We first focus on extending the classic EOQ model by considering the financing cost of WCR with a profit maximization objective. The optimal analytic production quantity formula is derived as well as sensitivity analysis of this model. Moreover, a comparison with the EOQ model and with the formula which considers the cost of capital are discussed. Secondly, a dynamic lot-sizing-based, discounted cash flow model is established based on Uncapacitated lot-sizing model. The zero-inventory ordering property is proven valid for this case and a polynomial-time algorithm can thus be established. Thirdly, multi-level and infinite capacity scenario is investigated with both sequential and centralized approaches. The ZIO property is demonstrated valid in both cases. Dynamic-programming based algorithms are constructed in order to obtain an optimal solution. This thesis should be considered as a first, but significant setup of combining production planning and working capital management. It is shown the significant financial consequences of lot-sizing decision on production planning. The cases investigated in this thesis may be tackled as subproblems in the study of more realistic scenarios.

Neste trabalho foi estudado um problema de dimensionamento de lotes em uma indústria química brasileira, cujo objetivo era determinar o tamanho dos lotes dos produtos para atender às demandas, minimizando os custos produtivos. Os itens podem ser produzidos em máquinas paralelas distintas, através de diferentes processos, e devem ser armazenados em taques cativos, exclusivos a um produto, ou multipropósitos, compartilhado entre produtos, desde que não simultaneamente. Foram propostos dois modelos matemáticos de programação inteira mista para representar o problema, o primeiro apresentava uma função objetivo compreendendo o preço das matérias-primas consumidas nas reações, os gastos com a estocagem de produtos e o custo de descarte de produtos quando os tanques de armazenamento não tiverem capacidade suficiente para armazená-los, já o segundo estendendo este modelo para considerar custos de preparação de máquina. Experimentos computacionais com os modelos propostos, utilizando instâncias geradas a partir dos dados fornecidos pela empresa, mostraram que o software de otimização empregado foi capaz de resolver poucas instâncias, após uma hora de processamento. Portanto, foram propostas heurísticas construtivas do tipo LP-and-fix e relax-and-fix, além de heurísticas de melhoria do tipo fix-and-optimize. Após serem realizados testes com essas heurísticas, constatou-se que algumas proporcionaram a obtenção de soluções factíveis de boa qualidade, quando comparadas às obtidas pelo software, sendo ainda capazes de resolver um maior número de instâncias / In this dissertation the lot sizing problem in a chemical Brazilian industry was studied, with the goal to determine the products lot size to satisfy the demands, minimizing the production costs. The items can be produced on distinct parallel machines through different processes and then must be stored in exclusive tanks, used by only one product, or multipurpose tanks, when more than one product can use the tank, but not simultaneously. Two models were proposed to represent the problem, the first one aiming to minimize the price of raw material consumed in the reactions, storage product spending and the cost of discarting products when the storage tanks do not have enough capacity to store them, and the second one considering setup cost either. Computational experiments using the proposed models, with instances were generated from the data provided by the company, showed that the used optimization software was able to solve only few instances after processing for one hour. In this dissertation we propose constructives heuristics such LP-and-fix and relax-and-fix, and improving heuristics like fix-and-optimize. After performing the tests with those heuristics, it was found that some of them provided feasible solutions with good quality, when compared to the ones obtained by the software, and they were also able to solve a larger number of instances

O problema integrado de programação de produção e dimensionamento de lotes em ambiente fowshop consiste em estabelecer tamanhos de lotes de produção e alocar máquinas para processá-los dentro de um horizonte de planejamento, em uma linha de produção com máquinas dispostas em série. O problema considera que a demanda deve ser atendida sem atrasos, que a capacidade das máquinas deve ser respeitada e que as preparações de máquinas são dependentes da sequência de produção e preservadas entre períodos do horizonte de planejamento. O objetivo é determinar uma programação de produção visando minimizar os custos de preparação de máquina, de produção e de estoque. Um modelo matemático da literatura é apresentado assim como procedimentos para obtenção de limitantes inferiores. Além disso, abordamos o problema por meio de distintas versões da metaheurística Times Assíncronos (A-Teams). Os procedimentos propostos foram comparados com heurísticas da literatura baseadas em Programação Inteira Mista (MIP). As metodologias desenvolvidas e os resultados obtidos são apresentados nesta dissertação / The integrated production scheduling and lot sizing problem in a fowshop environment consists in establishing production lot sizes and alocate machines to process them inside a planning horizon, in a production line with machines arranged in series. The problem considers that demand must be met without backlogging, the capacity of the machines must be respected, machine setup are sequence-dependent and preserved between periods of the planning horizon. The objective is to determine a production schedule to minimize the setup, production and inventory costs. A mathematical model from the literature is presented as well as procedures for obtaining lower bounds. In addition, we propose to address the problem through different versions of the metaheuristic Asynchronous Teams (A-Teams). The procedures were compared with literature heuristics based on Mixed Integer Programming (MIP). The developed methodologies and the obtained results are presented in this dissertation

Production planning problems are of paramount importance within supply chain planning, supporting decisions on the transformation of raw materials into finished products. Lot sizing in production planning refers to the tactical/operational decisions related to the size and timing of production orders to satisfy a demand. The objectives of lot-sizing problems are generally economical-related, such as saving costs or increasing profits, though other aspects may be taken into account such as quality of the customer service and reduction of inventory levels. Lot-sizing problems are very common in production activities and an efficient planning of such activities gives the company a clear advantage over concurrent organizations. To that end it is required the consideration of realistic features of the industrial environment and product characteristics. By means of mathematical modelling, such considerations are crucial, though their inclusion results in more complex formulations. Although lot-sizing problems are well-known and largely studied, there is a lack of research in some real-world aspects. This thesis addresses two main characteristics at the lot-sizing context: (a) setup crossover; and (b) perishable products. The former allows the setup state of production line to be carried over between consecutive periods, even if the line is not yet ready for processing production orders. The latter characteristic considers that some products have fixed shelf-life and may spoil within the planning horizon, which clearly affects the production planning. Furthermore, two types of perishable products are considered, according to the duration of their lifetime: medium-term and short-term shelf-lives. The latter case is tighter than the former, implying more constrained production plans, even requiring an integration with other supply chain processes such as distribution planning. Research on stronger mathematical formulations and solution approaches for lot-sizing problems provides valuable tools for production planners. This thesis focuses on the development of mixed-integer linear programming (MILP) formulations for the lot-sizing problems considering the aforementioned features. Novel modelling techniques are introduced, such as the proposal of a disaggregated setup variable and the consideration of lot-sizing instead of batching decisions in the joint production and distribution planning problem. These formulations are subjected to computational experiments in state-of-the-art MILP-solvers. However, the inherent complexity of these problems may require problemdriven solution approaches. In this thesis, heuristic, metaheuristic and matheuristic (hybrid exact and heuristic) procedures are proposed. A lagrangean heuristic addresses the capacitated lot-sizing problem with setup carryover and perishable products. A novel dynamic programming procedure is used to achieve the optimal solution of the uncapacitated single-item lot-sizing problem with setup carryover and perishable item. A heuristic, a fix-and-optimize procedure and an adaptive large neighbourhood search approach are proposed for the operational integrated production and distribution planning. Computational results on generated set of instances based on the literature show that the proposed methods yields competitive performances against other literature approaches. / Problemas de planejamento da produção são de suma importância no planejamento da cadeia de suprimentos, dando suporte às decisões da transformação de matérias-primas em produtos acabados. O dimensionamento de lotes em planejamento de produção é definido pelas decisões tático-operacionais relacionadas com o tamanho das ordens de produção e quando fabricá-las para satisfazer a demanda. Os objetivos destes problemas são geralmente de cunho econômico, tais como a redução de custos ou o aumento de lucros, embora outros aspectos possam ser considerados, tais como a qualidade do serviço ao cliente e a redução dos níveis de estoque. Problemas de dimensionamento de lotes são muito comuns em atividades de produção e um planejamento eficaz de tais atividades, estabelece uma clara vantagem à empresa em relação à concorrência. Para este objetivo, é necessária a consideração de características realistas do ambiente industrial e do produto. Para a modelagem matemática do problema, estas considerações são cruciais, embora sua inclusão resulte em formulações mais complexas. Embora os problemas de dimensionamento de lotes sejam bem conhecidos e amplamente estudados, várias características reais importantes não foram estudadas. Esta tese aborda, no contexto de dimensionamento de lotes, duas características muito relevantes: (a) preservação da preparação total e parcial; e (b) produtos perecíveis. A primeira permite que o estado de preparação de uma linha de produção seja mantido entre dois períodos consecutivos, mesmo que a linha de produção ainda não esteja totalmente pronta para o processamento de ordens de produção. A ultima característica determina que alguns produtos tem prazo de validade fixo, menor ou igual do que o horizonte de planejamento, o que afeta o planejamento da produção. Além disso, de acordo com a duração de sua vida útil, foram considerados dois tipos de produtos perecíveis: produtos com tempo de vida de médio e curto prazo. O ultimo caso resulta em um problema mais apertado do que o anterior, o que implica em planos de produção mais restritos. Isto pode exigir uma integração com outros processos da cadeia de suprimentos, tais como o planejamento de distribuição dos produtos acabados. Pesquisas sobre formulações matemáticas mais fortes e abordagens de solução para problemas de dimensionamento de lotes fornecem ferramentas valiosas para os planejadores de produção. O foco da tese reside no desenvolvimento de formulações de programação linear inteiro-mistas (MILP) para os problemas de dimensionamento de lotes, considerando as características mencionadas anteriormente. Novas técnicas de modelagem foram introduzidas, como a proposta de variáveis de preparação desagregadas e a consideração de decisões de dimensionamento de lotes ao invés de decisões de agrupamento de ordens de produção no problema integrado de planejamento de produção e distribuição. Estas formulações foram submetidas a experimentos computacionais em MILP-solvers de ponta. No entanto, a complexidade inerente destes problemas pode exigir abordagens de solução orientadas ao problema. Nesta tese, abordagens heurísticas, metaheurísticas e matheurísticas (híbrido de métodos exatos e heurísticos) foram propostas para os problemas discutidos. Uma heurística lagrangeana aborda o problema de dimensionamento de lotes com restrições de capacidade, preservação da preparação total e produtos perecíveis. Um novo procedimento de programação dinâmica e utilizado para encontrar a solução ótima do problema de dimensionamento de lotes de um único produto perecível, sem restrições de capacidade e preservação da preparação total. Uma heurística, um procedimento x-and-optimize e uma abordagem por buscas adaptativas em grande vizinhanças são propostas para o problema integrado de planejamento de produção e distribuição. Resultados computacionais em conjuntos de instâncias geradas com base na literatura mostram que os métodos propostos obtiveram performances competitivas com relação a outras abordagens da literatura.