Um importante problema de programação da produção surge em indústrias de papel integrando o problema de planejamento em múltiplas máquinas paralelas com o problema de corte. O problema de dimensionamento de lotes deve determinar a quantidade de jumbos (bobinas grandes de papel) de diferentes tipos de papel a serem produzidos em cada máquina. Estes jumbos são então cortados para atender a demanda de itens (bobinas menores de papel). O planejamento, que minimiza custos de produção e preparação, deve produzir jumbos (cada máquina produz jumbos de larguras diferentes) que diminuam a perda no processo de corte. Por outro lado, o melhor número de jumbos do ponto de vista de minimizar a perda no processo de corte pode acarretar em altos custos de preparação. Ambos são problemas de otimização combinatória não trivial, o que tem motivado extensas pesquisas nas últimas décadas, entretanto, essa combinação não é bem explorada na literatura. Neste trabalho, são propostos um modelo de otimização integrado e métodos heurísticos de solução. Foram realizados experimentos computacionais com o intuito de analisar o desempenho dos métodos propostos e os resultados apresentaram- se bastante satisfatórios, significando que tais métodos são apropriados para tratar o problema integrado. / An important production programming problem arises in paper industries coupling mul- tiple machine scheduling with cutting stock. From machine scheduling the problem of determining the quantity of jumbos (large rolls of paper) of different types of paper to be produced in each machine arises. These jumbos are then cut to meet the demand for items (smaller rolls of paper). Scheduling that minimizes setups and production costs may produce jumbos (each machine produces jumbos of a specific width) which may increase waste in the cutting process. On the other hand, the best number of jumbos in the point of view of minimizing waste in the cutting process may lead to high setup costs. Both problems are non-trivial combinatorial optimization problems, which have motivated ex- tensive research in the last decades, however their combination is not well explored in the literature. In this work, a coupled optimization modelling and heuristic solution methods are proposed. Computational experiments are devised in order to analyze the performance of the methods and the results had been presented sufficiently satisfactory, meaning that such methods are appropriate to deal with the integrated problem.

[ES] Con la globalización de los mercados y el aumento de la competitividad, la coordinación se ha convertido en un punto estratégico en la gestión de la cadena de suministro. De hecho, cada actor de la cadena de suministro ya no debe tomar decisiones sin considerar todos los eslabones, sean proveedores, proveedores de proveedores o clientes y estos internos o externos a la organización. Las cadenas de suministro son cada vez más complejas y distribuidas, compuestas por múltiples organizaciones con diferentes objetivos y políticas. La coordinación se puede lograr utilizando uno de estos dos enfoques para la toma de decisiones coordinadas: centralizada o descentralizada con un mecanismo de coordinación. Pero, las empresas son reacias a compartir información, ya sea por la confidencialidad de los datos o porque los modelos centralizados resultantes son de gran complejidad que dificultan su manejo y actualización. Además, aquellas empresas que buscan tomar decisiones en tiempo real requieren de modelos ligeros y ágiles, que, con toda la información local y coordinada con el resto, permitan tomar decisiones rápidas. Las empresas interesadas en la coordinación descentralizada con un mecanismo de coordinación esperan obtener mejores resultados con respecto a la no coordinación, aunque deberían asumir tener peores resultados que con la coordinación centralizada. Para ello en esta tesis, se han estudiado los distintos mecanismos de coordinación para la toma de decisiones descentralizada, dentro de un entorno del procedimiento de horizontes rodantes y con herramienta de planificación y programación de las operaciones basada en el concepto de stroke, que extiende el concepto de lista de materiales más allá de las estructuras tradicionales. Estos permiten desarrollar la formulación de la programación matemática y los mecanismos de coordinación necesarios para resolver los problemas de planificación de operaciones. Esta tesis se presenta como una secuencia de capítulos, con el objeto de analizar y presentar la propuesta de mecanismo de coordinación distribuido con unos recursos compartidos. Los distintos capítulos han servido de base para la preparación de artículos científicos. Estos artículos han sido presentados en congresos de la materia y remitidos a revistas científicas. / [CAT] Amb la globalització dels mercats i l'augment de la competitivitat, la coordinació s'ha convertit en un punt estratègic en la gestió de la cadena de subministrament. De fet, cada actor de la cadena de subministrament ja no ha de prendre decisions sense considerar totes les baules, siguen proveïdors, sub-proveïdors o clients i aquests interns o externs a l'organització. Les cadenes de subministrament són cada vegada més complexes i distribuïdes, compostes per múltiples organitzacions amb diferents objectius i polítiques. La coordinació es pot aconseguir utilitzant un d'aquests dos enfocaments per a la presa de decisions coordinades: centralitzat o descentralitzat amb un mecanisme de coordinació. Però, les empreses són poc inclinades a compartir informació, ja siga per la confidencialitat de les dades o perquè els models centralitzats resultants són de gran complexitat que dificulten el seu maneig i actualització. A més, aquelles empresa que busquen prendre decisions en temps real requereixen de models lleugers i àgils, que, amb tota la informació local i coordinada amb la resta, permeten prendre decisions ràpides. Les empreses interessades en la coordinació descentralitzada amb un mecanisme de coordinació esperen obtindre millors resultats respecte de la no coordinació encara que haurien d'assumir tindre pitjors resultats que amb la coordinació centralitzada. Per a això en aquesta tesi, s'han estudiat els diferents mecanismes de coordinació per a la presa de decisions descentralitzada, dins d'un entorn d'horitzons rodant i amb eines de planificació i programació de les operacions basada en el concepte de stroke, que estén el concepte de llista de materials més enllà de les estructures tradicionals. Aquests permeten desenvolupar la formulació de la programació matemàtica i els mecanismes de coordinació necessaris per a resoldre els problemes de planificació d'operacions. Aquesta tesi es presenta com una seqüència de capítols, a fi d'analitzar i presentar la proposta de mecanisme de coordinació distribuït amb uns recursos compartits. Els diferents capítols han servit de base per a la preparació d'articles científics. Aquests articles han sigut presentats en congressos de la matèria i remesos a revistes científiques. / [EN] With the globalization of markets and the increase of competitiveness, coordination has become a strategic point in the management of the supply chain. In fact, each actor in the supply chain must no longer make decisions without considering all the links, whether suppliers, sub-suppliers or customers and those internal or external to the organization. Supply chains are increasingly complex and distributed, composed of multiple organizations with different objectives and policies. Coordination can be achieved using one of these two approaches to coordinate decision making: centralized or decentralized with a coordination mechanism. However, companies are reluctant to share information, either because of the confidentiality of the data or because the resulting centralized models are of great complexity that make their management and update them. In addition, those companies that seek to make decisions in real time require lightweight and agile models, which, with all the local information and coordinated with the rest, allow quick decisions. Companies interested in decentralized coordination with a coordination mechanism expect to obtain better results regarding non-coordination although they should assume to have worse results than with centralized coordination. To this end, in this thesis, the different coordination mechanisms for decentralized decision making have been studied, within an environment of rolling horizons and with tools for planning and scheduling operations based on the concept of stroke, which extends the concept of list of materials beyond traditional structures. These allow to develop the formulation of the mathematical programming and the coordination mechanisms necessary to solve the operations planning problems. This thesis is presented as a sequence of chapters, in order to analyse and present the proposal of distributed coordination mechanism with shared resources. The different chapters have served as the basis for the preparation of scientific articles. These articles have been presented at congresses of the subject and submitted to scientific journals. / Rius Sorolla, GV. (2019). Modelos y Algoritmos de Coordinación para la Planificación de Operaciones basadas en el concepto Stroke en Redes de Suministro distribuidas y con alternativas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134017 / TESIS

IKEA Industry has factories in several locations where one location is in Hultsfred, Småland. IKEA Industry Hultsfred produces IKEA:s wardrobe collection PAX. In 2018, 2.8 million wardrobes were manufactured in Hultsfred. This thesis covers the subject of constructing and developing an optimization model which has the same characteristics as the plinth production at IKEA Industry Hultsfred. This means that the optimization model must consider production sequence; the setup time is different depending on which products that have been produced in the machine earlier. The thesis also covers to investigate how much time that is necessary to produce the plinths to Hultsfred, Portugal, and Germany under different circumstances. This thesis also balances inventory levels and setup time. I.e., how the inventory levels are changed if more emphasis is put into minimizing setup time, and the other way around. The results show that lower inventory levels and setup times are achievable over seven days if the production planning technique is changed.

This research is concerned with the development of efficient solutions to various problems that arise in the flow-shop environments which utilize lot-streaming. Lot streaming is a commonly used process of splitting production lots into sublots and, then, of scheduling the sublots in an overlapping fashion on the machines, so as to expedite the progress of orders in production and to improve the overall performance of the production system. The different lot-streaming problems that arise in various flow-shop environments have been divided into two categories, single-lot problems and multiple-lot problems. Further classification of the multiple-lot problems into the lot streaming sequencing problem (LSSP) and the flow-shop lot-streaming (FSLS) problem is made in this work. This classification is motivated by the occurrence of these problems in the industry. Several variants of these problems are addressed in this research. In agreement with numerous practical applications, we assume sublots of equal sizes. It turns out that this restriction paves the way to the relaxation of several typical limitations of current lot-streaming models, such as assumption of negligible transfer and setup times or consideration of only the makespan criterion. For the single-lot problem, a goal programming (GP) approach is utilized to solve the problem for a unified cost objective function comprising of the makespan, the mean flow time, the average work-in-process (WIP), and the setup and handling related costs. A very fast optimal solution algorithm is proposed for finding the optimal number of sublots (and, consequently, the sublot size) for this unified cost objective function in a general m-machine flow shop. For the more complicated multiple-lot problem, a near-optimal heuristic for the solution of the LSSP is developed. This proposed heuristic procedure, referred to as the Bottleneck Minimal Idleness (BMI) heuristic, identifies and employs certain properties of the problem that are irregular in traditional flow-shop problems, particularly the fact that the sublot sizes eminating from the same lot type and their processing times (on the same machines) are identical. The BMI heuristic attempts to maximize the time buffer prior to the bottleneck machine, thereby minimizing potential bottleneck idleness, while also looking-ahead to sequence the lots with large remaining process time earlier in the schedule. A detailed experimental study is performed to show that the BMI heuristic outperforms the Fast Insertion Heuristic (the best known heuristic for flow-shop scheduling), when modified for Lot Streaming (FIHLS) and applied to the problem on hand. For the FSLS problem, several algorithms are developed. For the two-machine FSLS problem with an identical sublot-size for all the lots, an optimal pseudo-polynomial solution algorithm is proposed. For all practical purposes (i.e., even for very large lot sizes), this algorithm is very fast. For the case in which the sublot-sizes are lot-based, optimal and heuristic procedures are developed. The heuristic procedure is developed to reduce the complexity of the optimal solution algorithm. It consists of a construction phase and an improvement phase. In the construction phase, it attempts to find a near-optimal sequence for the lots and then, in the improvement phase, given the sequence, it attempts to optimize the lot-based sublot-sizes of each of the lots. Extensions of the solution procedures are proposed for the general m-machine FSLS problem. A comprehensive simulation study of a flow shop system under lot streaming is conducted to support the validity of the results and to demonstrate the effectiveness of the heuristic procedures. This study clearly indicates that, even in dynamic practical situations, the BMI rule, which is based on the proposed BMI heuristic, outperforms existing WIP rules, commonly used in industry, in scheduling a flow-shop that utilizes lot streaming. With respect to the primary performance measure - cycle time (or MFT) - the BMI rule demonstrates a clear improvement over other WIP rules. It is further shown that it also outperforms other WIP rules with respect to the output variability measure, another important measure in flow-shop systems. The effects of several other factors, namely system randomness, system loading, and bottleneck-related (location and number), in a flow-shop under lot streaming, are also reported. / Ph. D.

This thesis considers two kinds of problems, motivated by practical applications in data center operations and maintenance. Data centers are the brain of the internet, each hosting as many as tens of thousands of IT devices, making them a considerable global energy consumption contributor (more than 1 percent of global power consumption). There is a large body of work at different layers aimed at reducing the total power consumption for data centers. One of the key places to save power is addressing the thermal heterogeneity in data centers by thermal-aware workload distribution. The corresponding optimization problem is challenging due to its combinatorial nature and the computational complexity of thermal models. In this thesis, a holistic theoretical approach is proposed for thermal-aware workload distribution which uses linearization to make the problem model-independent and easier to study. Two general optimization problems are defined. In the first problem, several cooling parameters and heat recirculation effects are considered, where two red-line temperatures are defined for idle and fully utilized servers to allow the cooling effort to be reduced. The resulting problem is a mixed integer linear programming problem which is solved approximately using a proposed heuristic. Numerical results confirm that the proposed approach outperforms commonly considered baseline algorithms and commercial solvers (MATLAB) and can reduce the power consumption by more than 10 percent. In the next problem, additional operational costs related to reliability of the servers are considered. The resulting problem is solved by a generalization of the proposed heuristics integrated with a Model Predictive Control (MPC) approach, where demand predictions are available. Finally, in the second type of problems, we address a problem in inventory management related to data center maintenance, where we develop an efficient dynamic programming algorithm to solve a lot-sizing problem. The algorithm is based on a key structural property that may be of more general interest, that of a just-in-time ordering policy. / Thesis / Doctor of Philosophy (PhD) / Data centers, each hosting as many as tens of thousands of IT devices, contribute to a considerable portion of energy usage worldwide (more than 1 percent of global power consumption). They also encounter other operational costs mostly related to reliability of devices and maintenance. One of the key places to reduce energy consumption is through addressing the thermal heterogeneity in data centers by thermal-aware work load distribution for the servers. This prevents hot spot generation and addresses the trade-off between IT and cooling power consumption, the two main power consump tion contributors. The corresponding optimization problem is challenging due to its combinatorial nature and the complexity of thermal models. In this thesis, we present a holistic approach for thermal-aware workload distribution in data centers, using lin earization to make the problem model-independent and simpler to study. Two quite general nonlinear optimization problems are defined. The results confirm that the proposed approach completed by a proposed heuristic solves the problems efficiently and with high precision. Finally, we address a problem in inventory management related to data center maintenance, where we develop an efficient algorithm to solve a lot-sizing problem that has a goal of reducing data center operational costs.

Les travaux de recherche effectués dans le cadre de cette thèse concernent un problème delotissement et de séquencement pour une ligne de production imparfaite. Deux types d'aléas sont prisen compte : le rendement aléatoire (à cause des rebuts) et le temps d'exécution aléatoire (à cause despannes machines). Les temps de changement de série dépendant de la séquence des produits sontégalement pris en compte.Le problème est issu de d'une fabrication automatisée (usine-automate) des circuits impriméset il a été posé lors de la conception du système de gestion de production de l'atelier fabriquant lespartons conducteurs de plusieurs types. Étant donné que l'usine était complètement automatisée,l'atelier (comme le reste de l'usine) travaillait la plupart de la journée sans personnel autre que celui demaintenance, alors il fallait construire un planning de production pour les 24 heures suivantes. Ceplanning devait être répété chaque jour. Le problème consistait à définir les quantités optimales deproduits à traiter (tailles de lots) et l'ordre de passage des lots dans une ligne de production afind'optimiser un critère.Le problème traité appartient à trois domaines de recherche: 1) lotissement optimale pour lessystèmes de production imparfaits (ou lotissement sous incertitudes); 2) ordonnancement etlotissement déterministe; 3) ordonnancement avec des temps ou (et) coût de changement de série (setup).Dans la littérature scientifique nous trouvons beaucoup d’exemples de problèmes appartenant auun ou à l’intersection de deux de ces domaines. Par contre, nous n’avons pas trouvé les travaux quitraites de problèmes identiques au notre.Etant donné que le problème est trop compliqué tel qu’il est, nous avons cherché des façonsde son modélisation qui nous permettrons le résoudre. Nous avons trouvé trois cas où le problèmeinitial peut être décomposé en plusieurs parties, chacune entre lesquelles peut être transformé dans unproblème connu de la Recherche Opérationnelle. Ensuite nous avons travaillé que sur la partielotissement du problème décomposé tout en montrant comment les autres partis peuvent être résolus.Les problèmes de ce type sont très importants pour l’optimisation d’une chaine logistique.Ces résolutions aident d’organiser la production à la manière efficace, que permet aux entreprises defaire des gains financiers importants. / This thesis contains the research study of a multi-product lot-sizing and sequencing problemfor an imperfect production line. Two types of uncertainties were taken into account: the random yield(because of defective items) and random lead time (due to machine breakdowns). The sequencedependent set-up time between different products was also taken into account.This problem came from the electronics industry, more precisely from the automatedmanufacturing of several types of Printed Circuit Board. Since the plant was completely automated,the considered production line (like the rest of the plant) worked most of the day without any otherstaff but maintenance one, so it was necessary to consider a production schedule for the next 24 hours.This schedule was repeated every day. The problem was to define the optimal quantities of productsto be manufactured (lot sizes) and the sequence of these lots to optimize a given factor. The problemaddressed belongs to three research domains: 1) optimal lot-sizing for the imperfect productionsystems (or lot-sizing under uncertainty), 2) deterministic lot-sizing and scheduling, 3) schedulingwith set-up times or (and) costs. In the literature we can find many examples of problems belonging toone or the intersection of two of these areas. But we did not find any work that deals with similarproblem to ours.Since the problem is too complicated as it is, we looked for ways of modeling it, which wouldhave allowed us to solve it. We found three cases where the original problem could be decomposedinto several parts, each of which could be converted to a known problem of Operations Research.Then we worked on the lot-sizing part of the decomposed problem. Meanwhile, we showed how otherparties could be resolved.These kinds of problems are very important for a supply chain optimizing. Their solutionshelp to organize an efficient production, which in turn allows to make significant financial gains tocompany.

O problema de dimensionamento de lotes com restrições de capacidade (CLSP) consiste em determinar um plano de produção que satisfaça a demanda requerida, respeitando as limitações de capacidade, com o menor custo possível, ou seja, minimizando os custos de produção, estocagem e preparação de máquina. Encontrar uma solução factível para o CLSP, considerando tempo de preparação de máquina, é NP-completo. Nesta dissertação, para a resolução do CLSP, utiliza-se a decomposição de Dantzig-Wolfe e o procedimento de geração de colunas, encontrando bons limitantes inferiores. Duas diferentes estratégias de decomposição são exploradas, decomposição por itens e períodos. Para a obtenção de uma solução inteira para o problema (limitante superior) foram exploradas heurísticas lagrangianas, onde a solução inicial para as heurísticas provém da geração de colunas. Os limitantes obtidos podem ser utilizados em métodos exatos, como por exemplo, em algoritmos do tipo branch-and-price. Experimentos computacionais, baseados em exemplares gerados aleatoriamente, foram realizados e os resultados analisados, as variações dos parâmetros das instâncias foram sugeridas na literatura / The Capacitated Lot Sizing Problem (CLSP) consists in determining a production plan such that all demands are met and the total costs of production, inventory and setup are minimized. Since the problem to find a feasible solution to the CLSP with setup times is NP-complete, large problem instances have been solved by heuristic methods. In this dissertation, we are particularly concerned in using the methodology of Dantzig-Wolfe decomposition and column generation to generate good bounds to the CLSP with setup times and costs. Here, we analyse two types of decomposition which are based on items and time periods (lower bound) and some lagrangian-based heuristics (upper bound). Numerical results based on randomly generated intances suggest that highquality lower bounds are obtained by column generation algorithms, such as well as upper bounds by heuristics. These bounds are useful in exact solution methods, such as branch-and-price algorithms

O planejamento da produção em indústrias moveleiras de pequeno porte é comumente constituído por decisões referentes ao volume de produção e à política de estoque, com o objetivo de minimizar o desperdício de material, os atrasos e as horas-extras utilizadas ao longo do horizonte de planejamento. Administrar tais decisões de uma maneira tratável e eficiente é, em geral, um desafio, especialmente considerando a natureza incerta dos dados. Nessa tese, são desenvolvidos modelos de otimização para apoiar tais decisões no contexto do problema combinado de dimensionamento de lotes e corte de estoque sob incertezas que surge em indústrias moveleiras. Para lidar com as incertezas dos dados, são investigadas duas metodologias: programação estocástica e otimização robusta. Dessa maneira, são propostos modelos de programação estocástica de dois estágios com recurso, assim como modelos estocásticos robustos que incorporam aversão ao risco. A motivação em também desenvolver modelos baseados em otimização robusta é considerar casos práticos em que não há uma descrição probabilística explícita dos dados de entrada, assim como evitar trabalhar com numerosos cenários, o que pode tornar o modelo estocástico computacionalmente intratável. Os experimentos numéricos baseados em exemplares reais de uma empresa moveleira de pequeno porte mostram que as soluções obtidas pelos modelos de programação estocástica fornecem planos de produção robustos e que o (a) decisor (a) pode designar suas preferências em relação ao risco aos modelos, assim como controlar o tradeoff entre o custo total esperado e a robustez da solução. Em relação aos resultados dos modelos de otimização robusta, são obtidos alguns insights entre os chamados budgets de incerteza, as taxas de atendimento da demanda e os valores ótimos. Além disso, evidências numéricas indicam que budgets de incerteza menos conservadores resultam em níveis de serviço razoáveis com baixos custos globais, enquanto a abordagem de pior caso gera, relativamente, boas taxas de atendimento da demanda, mas com custos globais elevados / Production planning procedures in small-size furniture companies commonly consist of decisions with respect to production level and inventory policy, while attempting to minimize trim-loss, backlogging and overtime usage throughout the planning horizon. Managing these decisions in a tractable and efficient way is often a challenge, especially when the uncertainty of data is taken into account. In this thesis, we develop optimization models to support these decisions in the context of the combined lot-sizing and cutting-stock problem that arises in furniture companies. To deal with data uncertainty, we investigate two methodologies: stochastic programming and robust optimization. In the former case, we propose two-stage stochastic programming models with recourse, as well as robust stochastic models to incorporate risk-aversion. In the latter case, our motivation to investigate robust optimization models is the lack of an explicit probabilistic description of the input data. Furthermore, we want to avoid dealing with a large number of scenarios, which typically lead to computationally intractable stochastic programming models. Numerical experiments based on real data from a small-size furniture plant show that the solutions of the stochastic programming models provide robust production plans so that the decision-maker can assign his or her risk preferences to the model and control the tradeoff between the expected total cost and solution robustness. Regarding the results from the robust optimization models, we provide some insights into the relationship among budgets of uncertainty, fill rates and optimal values. Moreover, numerical evidence indicate that less conservative budgets of uncertainty result in reasonable service levels with cheaper global costs, while worst case deterministic approaches lead to relatively good fill rates, but with prohibitive global costs

As decisões operacionais de produção em uma indústria de fiação são planejadas na prática determinando soluções dos sub-problemas de dimensionamento e sequenciamento de lotes e da mistura de fardos de algodão. As tarefas são: definir o tamanho, a sequência, o tempo e alocação de cada lote de produção e quais fardos de algodão devem ser utilizados na produção. Por si só, os sub-problemas representam grandes desafios no planejamento da produção. Entretanto, para melhor representar o ambiente produtivo e alcançar custos de produção mais baixos, indústrias de processo, como as de fiação, procuram integrar mais e mais seus sub-problemas de planejamento. O objetivo dessa tese é apresentar modelos matemáticos e métodos de solução para auxiliar a tomada de decisão no nível operacional do planejamento da produção. Três formulações matemáticas para o dimensionamento e sequenciamento de lotes em um sistema de dois estágios com produção sincronizada são propostas. Um novo método baseado em programação matemática e metaheurísticas e também desenvolvida para a solucão desse sub-problema. Além disso, a integração das decisões relativas a matéria-prima (fardos de algodão) ao dimensionamento e sequenciamento de lotes é analisada. As novas formulações propostas representam de forma mais realista o problema de dimensionamento e sequenciamento de lotes da indústria de fiação e de indústrias de processo com ambiente produtivo similares. O método de solução encontra boas soluções para o problema e supera outros méodos similares presentes em softwares comerciais. Além disso, o método é geral o suficiente para a solução de outros problemas de otimização. O problema integrado de dimensionamento e sequenciamento de lotes e mistura comprovou que restrições relativas à qualidade dos fios influenciam os custos e viabilidade do planejamento da produção. O planejamento integrado dessas óperações trata o sistema considerando restrições que se relacionam, definindo planos de produção mais realistas / In the practice of a spinning industry, the operational decisions of the production planning are determined by the hierarchical solution of the lot-sizing and scheduling problem and the blending problem of the cotton bales. The tasks are: to define the size, sequence, timing and allocation of each production lot and to select which cotton bales are used for production. Each of these problems represents a large challenge in planning the production. However, in order to better represent the production environment and to reach lower production costs, process industries (as the spinning industry) are integrating more and more of the production sub-problems into the planning. The aim of this thesis is to propose novel mathematical models and solution methods to assist the decision maker to plan the production at the operational level. Three formulations for the synchronized two-stage lot sizing and scheduling are proposed. A new method based on mathematical programming and metaheuristics is also developed to solve this sub-problem. In addition, the integration of the lot sizing and scheduling with decisions related to the raw materials (cotton bales) is analyzed. The novel models represent a more realistic lot sizing and scheduling for the spinning industry and process industries of similar production environment. The solution method finds good solutions to the mentioned problem and outperforms other state-of-the-art methods incorporated in commercial softwares. Moreover, the method is general enough to solve other optimization problems. The integrated lot-sizing, scheduling and blending prove that constraints related to the yarn quality influence the costs and the feasibility of the production planning. The integrated planning of these operations approaches the system considering the constraint relationship and defines more realistic production plans

Em diversas indústrias de manufatura (por exemplo, papeleira, moveleira, metalúrgica, têxtil) as decisões do dimensionamento de lotes interagem com outras decisões do planejamento e programação da produção, tais como, a distribuição, o processo de corte, entre outros. Porém, usualmente, essas decisões são tratadas de forma isolada, reduzindo o espaço de soluções e a interdependência entre as decisões, elevando assim os custos totais. Nesta tese, estudamos o processo produtivo de indústrias de móveis de pequeno porte, que consiste em cortar placas grandes disponíveis em estoque para obter diversos tipos de peças que são processadas posteriormente em outros estágios e equipamentos com capacidades limitadas para, finalmente, comporem os produtos demandados. Os problemas de dimensionamento de lotes e corte de estoque são acoplados em um modelo de otimização linear inteiro cujo objetivo é minimizar os custos de produção, estoque de produtos, preparação de máquinas e perda de matéria-prima. Esse modelo mostra o compromisso existente entre antecipar ou não a fabricação de certos produtos aumentando os custos de estoque, mas reduzindo a perda de matéria-prima ao obter melhores combinações entre as peças. O impacto da incerteza da demanda (composta pela carteira de pedidos e mais uma quantidade extra estimada) foi amortizado pela estratégia de horizonte de planejamento rolante e por variáveis de decisão que representam uma produção extra para a demanda esperada no melhor momento, visando a minimização dos custos totais. Dois métodos heurísticos são desenvolvidos para resolver uma simplificação do modelo matemático proposto, o qual possui um alto grau de complexidade. Os experimentos computacionais realizados com exemplares gerados a partir de dados reais coletados em uma indústria de móveis de pequeno porte, uma análise dos resultados, as conclusões e perspectivas para este trabalho são apresentados / In the many manufacturing industries (e.g., paper industry, furniture, steel, textile), lot-sizing decisions generally arise together with other decisions of planning production, such as distribution, cutting, scheduling and others. However, usually, these decisions are dealt with separately, which reduce the solution space and break dependence on decisions, increasing the total costs. In this thesis, we study the production process that arises in small scale furniture industries, which consists basically of cutting large plates available in stock into several thicknesses to obtain different types of pieces required to manufacture lots of ordered products. The cutting and drilling machines are possibly bottlenecks and their capacities have to be taken into account. The lot-sizing and cutting stock problems are coupled with each other in a large scale linear integer optimization model, whose objective function consists in minimizing different costs simultaneously, production, inventory, raw material waste and setup costs. The proposed model captures the tradeoff between making inventory and reducing losses. The impact of the uncertainty of the demand, which is composed with ordered and forecasting products) was smoothed down by a rolling horizon strategy and by new decision variables that represent extra production to meet forecasting demands at the best moment, aiming at total cost minimization. Two heuristic methods are proposed to solve relaxation of the mathematical model. Randomly generated instances based on real world life data were used for the computational experiments for empirical analyses of the model and the proposed solution methods