O problema de planejamento da produção integrado ao problema de corte de estoque surge em várias indústrias de manufatura, tais como indústria de papel, móveis, aço entre outras, e consiste em um problema de otimização combinatória bastante complexo, devido ao fato de integrar dois problemas conhecidos na literatura de difícil resolução. As aplicações práticas deste problema vêm aumentando em muitas empresas que buscam tornar seus processos produtivos mais eficientes. Neste trabalho, estudamos o problema de otimização integrado que surge em pequenas indústrias de móveis, em que placas de MDF disponíveis em estoque devem ser cortadas em itens menores, de diversos tamanhos e quantidades para comporem os produtos demandados. O modelo matemático de otimização linear inteiro proposto permite que alguns produtos sejam antecipados e estocados. Essa antecipação da produção aumenta os custos de estoque, porém com o aumento da demanda de peças é possível gerar padrões de corte melhores e diminuir os custos com a perda de material. Consideramos no modelo dois tipos de variáveis de antecipação, uma de estoque convencional para atender uma demanda em carteira e outra para aproveitar a produção e atender uma demanda prevista, chamada variável oportunista. A função objetivo consiste em minimizar os custos dos processos de produção e de corte. Para resolver a relaxação linear deste problema, propomos um método lagrangiano e utilizamos a estratégia de horizonte rolante. Alguns testes computacionais são realizados e os resultados apresentados / The integrated problem of cutting stock and production planning arises in a several manufacturing industries, such as paper, furniture, steel among others, and it is a complex combinatorial optimization problem, due to the fact that it integrates two well-known NP problems of the literature. The real world applications of this problem have increased in many industries that search for more efficient production process. In this work, we studied an integrated optimization problem that arises in small furniture industries, where MDF boards available in inventory must be cut into enough quantities of items to compose demanded finish-goods. The model of integer linear optimization proposed allows anticipating some products and keeping them in inventory. This production anticipation makes increase the inventory costs, although makes it possible to determine better cutting patterns and decreases the costs of the cutting process. We consider in the model two types of anticipation variables, the first one to the ordinary inventory to meet ordered products and an other one, called chance variables, to meet a forecasting demand. The objective function is to minimize the costs of production process and waste of material. To solve a linear relaxation of this problem, we proposed lagrangian approach and used a rolling horizon strategy. Some computational tests are performed and results shown

Die Habilitationsschrift beschäftigt sich mit Theorie, Algorithmen und Software zur Lösung von nichtkonvexen, gemischt-ganzzahligen, nichtlinearen Optimierungsproblemen (MINLP). Sie besteht aus 14 Kapiteln, die in zwei Teile gegliedert sind. Im ersten Teil werden grundlegende Optimierungswerkzeuge beschrieben und im zweiten Teil werden Lösungsalgorithmen vorgestellt. Fast alle vorgeschlagenen Algorithmen wurden als Teil der objektorientierten C++ Bibliothek LaGO implementiert. Numerische Experimente mit verschiedenen MINLP-Problemen zeigen die Möglichkeiten und Grenzen dieser Verfahren. / This book is concerned with theory, algorithms and software for solving nonconvex mixed integer nonlinear programs. It consists of two parts. The first part describes basic optimization tools, such as block-separable reformulations, convex and Lagrangian relaxations, decomposition methods and global optimality criteria. The second part is devoted to algorithms. Starting with a short overview on existing methods, we present deformation, rounding, partitioning and Lagrangian heuristics, and a branch-cut-and-price algorithm. The algorithms are implemented as part of an object-oriented library, called LaGO. We report numerical results on several mixed integer nonlinear programs to show abilities and limits of the proposed solution methods.

Uma maneira de resolver problemas de programação linear de grande escala é explorar a relaxação lagrangeana das restrições \"difíceis\'\' e utilizar métodos de subgradientes. Populares por fornecerem rapidamente boas aproximações de soluções duais, eles não produzem diretamente as soluções primais. Para obtê-las com custo computacional adequado, pode-se construir seqüências ergódicas ou utilizar uma técnica proposta recentemente, denominada algoritmo do volume. As propriedades teóricas de convergência não foram bem estabelecidas nesse algoritmo, mas pequenas modificações permitem a demonstração da convergência dual. Destacam-se como adaptações o algoritmo do volume revisado, um método de feixes específico, e o algoritmo do volume incorporado ao método de variação do alvo. Este trabalho foi baseado no estudo desses algoritmos e de todos os conceitos envolvidos, em especial, análise convexa e otimização não diferenciável. Estudamos as principais diferenças teóricas desses métodos e realizamos comparações numéricas com problemas lineares e lineares inteiros, em particular, o corte máximo em grafos. / One way to solve large-scale linear programming problems is to exploit the Lagrangian relaxation of the difficult constraints and use subgradient methods. Such methods are popular as they give good approximations of dual solutions. Unfortunately, they do not directly yield primal solutions. Two alternatives to obtain primal solutions under reasonable computational cost are the construction of ergodic sequences and the use of the recently developed volume algorithm. While the convergence of ergodic sequences is well understood, the convergence properties of the volume algorithm is not well established in the original paper. This lead to some modifications of the original method to ease the proof of dual convergence. Three alternatives are the revised volume algorithm, a special case of the bundle method, and the volume algorithm incorporated by the variable target value method. The aim of this work is to study such algorithms and all related concepts, especially convex analysis and nondifferentiable optimization. We analysed the main theoretical differences among the methods and performed numerical experiments with linear and integer problems, in particular, the maximum cut problem on graphs.

Mathematical models for plant location problems form an important class of integer and mixed-integer linear programs. The Two-Stage Capacitated Plant Location Problem (TSCPLP), the subject of this thesis, consists of a three level structure: in the first or upper-most level are the production plants, the second or central level contains the distribution depots, and the third level is the customers. The decisions to be made are: the subset of plants and depots to open; the assignment of customers to open depots, and therefore open plants; and the flow of product from the plants to the depots, to satisfy the customers' service or demand requirements at minimum cost. The formulation proposed for the TSCPLP is unique from previous models in the literature because customers can be served from multiple open depots (and plants) and the capacity of both the set of plants and the set of depots is restricted. Surrogate constraints are added to strengthen the bounds from relaxations of the problem. The need for more understanding of the strength of the bounds generated by this procedure for the TSCPLP is evident in the literature. Lagrangian relaxations are chosen based more on ease of solution than the knowledge that a strong bound will result. Lagrangian relaxation has been applied in heuristics and also inserted into branch-and-bound algorithms, providing stronger bounds than traditional linear programming relaxations. The current investigation provides a theoretical and computational analysis of Lagrangian relaxation bounds for the TSCPLP directly. Results are computed through a Lagrangian heuristic and CPLEX. The test problems for the computational analysis cover a range of problem size and strength of capacity constraints. This is achieved by scaling the ratio of total depot capacity to customer demand and the ratio of total plant capacity to total depot capacity on subsets of problem instances. The analysis shows that there are several constraints in the formulation that if dualized in a Lagrangian relaxation provide strong bounds on the optimal solution to the TSCPLP. This research has applications in solution techniques for the TSCPLP and can be extended to some transformations of the TSCPLP. These include the single-source TSCPLP, and the multi-commodity TSCPLP which accommodates for multiple products or services.

This operations research thesis should be situated in the field of the power generation industry. The general objective of this work is to efficiently solve the Generalized Unit Commitment (GUC) problem by means of specialized software. The GUC problem generalizes the Unit Commitment (UC) problem by simultane-ously solving the associated Optimal Power Flow (OPF) problem. There are many approaches to solve the UC and OPF problems separately, but approaches to solve them jointly, i.e. to solve the GUC problem, are quite scarce. One of these GUC solving approaches is due to professors Batut and Renaud, whose methodology has been taken as a starting point for the methodology presented herein.This thesis report is structured as follows. Chapter 1 describes the state of the art of the UC and GUC problems. The formulation of the classical short-term power planning problems related to the GUC problem, namely the economic dispatching problem, the OPF problem, and the UC problem, are reviewed. Special attention is paid to the UC literature and to the traditional methods for solving the UC problem. In chapter 2 we extend the OPF model developed by professors Heredia and Nabona to obtain our GUC model. The variables used and the modelling of the thermal, hydraulic and transmission systems are introduced, as is the objective function. Chapter 3 deals with the Variable Duplication (VD) method, which is used to decompose the GUC problem as an alternative to the Classical Lagrangian Relaxation (CLR) method. Furthermore, in chapter 3 dual bounds provided by the VDmethod or by the CLR methods are theoretically compared.Throughout chapters 4, 5, and 6 our solution methodology, the Radar Multiplier (RM) method, is designed and tested. Three independent matters are studied: first, the auxiliary problem principle method, used by Batut and Renaud to treat the inseparable augmented Lagrangian, is compared with the block coordinate descent method from both theoretical and practical points of view. Second, the Radar Sub- gradient (RS) method, a new Lagrange multiplier updating method, is proposed and computationally compared with the classical subgradient method. And third, we study the local character of the optimizers computed by the Augmented Lagrangian Relaxation (ALR) method when solving the GUC problem. A heuristic to improve the local ALR optimizers is designed and tested.Chapter 7 is devoted to our computational implementation of the RM method, the MACH code. First, the design of MACH is reviewed brie y and then its performance is tested by solving real-life large-scale UC and GUC instances. Solutions computed using our VD formulation of the GUC problem are partially primal feasible since they do not necessarily fulfill the spinning reserve constraints. In chapter 8 we study how to modify this GUC formulation with the aim of obtaining full primal feasible solutions. A successful test based on a simple UC problem is reported. The conclusions, contributions of the thesis, and proposed further research can be found in chapter 9.

Lagrangian relaxation is a successful solution approach for many combinatorial optimisation problems, one of them being the train timetabling problem (TTP). We model this problem using time expanded networks for the single train schedules and coupling constraints to enforce restrictions like station capacities and headway times. Lagrangian relaxation of these coupling constraints leads to shortest path subproblems in the time expanded networks and is solved using a proximal bundle method. However, large instances of our practical partner Deutsche Bahn lead to computationally intractable models. In this thesis we develop two new algorithmic techniques to improve the solution process for this kind of optimisation problems. The first new technique, Dynamic Graph Generation (DGG), aims at improving the computation of the shortest path subproblems in large time expanded networks. Without sacrificing any accuracy, DGG allows to store only small parts of the networks and to dynamically extend them whenever the stored part proves to be too small. This is possible by exploiting the properties of the objective function in many scheduling applications to prefer early paths or due times, respectively. We prove that DGG can be implemented very efficiently and its running time and the size of nodes that have to be stored additionally does not depend on the size of the time expanded network but only on the length of the train routes. The second technique is an asynchronous and parallel bundle method (APBM). Traditional bundle methods require one solution of each subproblem in each iteration. However, many practical applications, e.g. the TTP, consist of rather loosely coupled subproblems. The APBM chooses only small subspaces corresponding to the Lagrange multipliers of strongly violated coupling constraints and optimises only these variables while keeping all other variables fixed. Several subspaces of disjoint variables may be chosen simultaneously and are optimised in parallel. The solutions of the subspace problem are incorporated into the global data as soon as it is available without any synchronisation mechanism. However, in order to guarantee convergence, the algorithm detects automatically dependencies between different subspaces and respects these dependencies in future subspace selections. We prove the convergence of the APBM under reasonable assumptions for both, the dual and associated primal aggregate data. The APBM is then further extended to problems with unknown dependencies between subproblems and constraints in the Lagrangian relaxation problem. The algorithm automatically detects these dependencies and respects them in future iterations. Again we prove the convergence of this algorithm under reasonable assumptions. Finally we test our solution approach for the TTP on some real world instances of Deutsche Bahn. Using an iterative rounding heuristic based on the approximate fractional solutions obtained by the Lagrangian relaxation we are able to compute feasible schedules for all trains in a subnetwork of about 10% of the whole German network in about 12 hours. In these timetables 99% of all passenger trains could be scheduled with no significant delay and the travel time of the freight trains could be reduced by about one hour on average.

La programmation linéaire en nombres entiers est une approche robuste qui permet de résoudre rapidement de grandes instances de problèmes d'optimisation discrète. Toutefois, les problèmes gagnent constamment en complexité et imposent parfois de fortes limites sur le temps de calcul. Il devient alors nécessaire de développer des méthodes spécialisées afin de résoudre approximativement ces problèmes, tout en calculant des bornes sur leurs valeurs optimales afin de prouver la qualité des solutions obtenues. Nous proposons d'explorer une approche de reformulation en nombres entiers guidée par la relaxation lagrangienne. Après l'identification d'une forte relaxation lagrangienne, un processus systématique permet d'obtenir une seconde formulation en nombres entiers. Cette reformulation, plus compacte que celle de Dantzig et Wolfe, comporte exactement les mêmes solutions entières que la formulation initiale, mais en améliore la borne linéaire: elle devient égale à la borne lagrangienne. L'approche de reformulation permet d'unifier et de généraliser des formulations et des méthodes de borne connues. De plus, elle offre une manière simple d'obtenir des reformulations de moins grandes tailles en contrepartie de bornes plus faibles. Ces reformulations demeurent de grandes tailles. C'est pourquoi nous décrivons aussi des méthodes spécialisées pour en résoudre les relaxations linéaires. Finalement, nous appliquons l'approche de reformulation à deux problèmes de localisation. Cela nous mène à de nouvelles formulations pour ces problèmes; certaines sont de très grandes tailles, mais nos méthodes de résolution spécialisées les rendent pratiques. / Integer linear programming is a robust and efficient approach to solve large-scale instances of combinatorial problems. However, problems constantly gain in complexity and sometimes impose strong constraints on computation times. We must then develop specialised methods to compute heuristic primal solutions to the problem and derive lower bounds on the optimal value, and thus prove the quality of our primal solutions. We propose to guide a reformulation approach for mixed integer programs with Lagrangian relaxations. After the identification of a strong relaxation, a mechanical process leads to a second integer formulation. This reformulation is equivalent to the initial one, but its linear relaxation is equivalent to the strong Lagrangian dual. We will show that the reformulation approach unifies and generalises prior formulations and lower bounding approaches, and that it exposes a simple mechanism to reduce the size of reformulations in return for weaker bounds. Nevertheless, our reformulations are large. We address this issue by solving their linear relaxations with specialised methods. Finally, we apply the reformulation approach to two location problems. This yields novel formulations for both problems; some are very large but, thanks to the aforementioned specialised methods, still practical.

O problema de planejamento da produção integrado ao problema de corte de estoque surge em várias indústrias de manufatura, tais como indústria de papel, móveis, aço entre outras, e consiste em um problema de otimização combinatória bastante complexo, devido ao fato de integrar dois problemas conhecidos na literatura de difícil resolução. As aplicações práticas deste problema vêm aumentando em muitas empresas que buscam tornar seus processos produtivos mais eficientes. Neste trabalho, estudamos o problema de otimização integrado que surge em pequenas indústrias de móveis, em que placas de MDF disponíveis em estoque devem ser cortadas em itens menores, de diversos tamanhos e quantidades para comporem os produtos demandados. O modelo matemático de otimização linear inteiro proposto permite que alguns produtos sejam antecipados e estocados. Essa antecipação da produção aumenta os custos de estoque, porém com o aumento da demanda de peças é possível gerar padrões de corte melhores e diminuir os custos com a perda de material. Consideramos no modelo dois tipos de variáveis de antecipação, uma de estoque convencional para atender uma demanda em carteira e outra para aproveitar a produção e atender uma demanda prevista, chamada variável oportunista. A função objetivo consiste em minimizar os custos dos processos de produção e de corte. Para resolver a relaxação linear deste problema, propomos um método lagrangiano e utilizamos a estratégia de horizonte rolante. Alguns testes computacionais são realizados e os resultados apresentados / The integrated problem of cutting stock and production planning arises in a several manufacturing industries, such as paper, furniture, steel among others, and it is a complex combinatorial optimization problem, due to the fact that it integrates two well-known NP problems of the literature. The real world applications of this problem have increased in many industries that search for more efficient production process. In this work, we studied an integrated optimization problem that arises in small furniture industries, where MDF boards available in inventory must be cut into enough quantities of items to compose demanded finish-goods. The model of integer linear optimization proposed allows anticipating some products and keeping them in inventory. This production anticipation makes increase the inventory costs, although makes it possible to determine better cutting patterns and decreases the costs of the cutting process. We consider in the model two types of anticipation variables, the first one to the ordinary inventory to meet ordered products and an other one, called chance variables, to meet a forecasting demand. The objective function is to minimize the costs of production process and waste of material. To solve a linear relaxation of this problem, we proposed lagrangian approach and used a rolling horizon strategy. Some computational tests are performed and results shown

Uma maneira de resolver problemas de programação linear de grande escala é explorar a relaxação lagrangeana das restrições \"difíceis\'\' e utilizar métodos de subgradientes. Populares por fornecerem rapidamente boas aproximações de soluções duais, eles não produzem diretamente as soluções primais. Para obtê-las com custo computacional adequado, pode-se construir seqüências ergódicas ou utilizar uma técnica proposta recentemente, denominada algoritmo do volume. As propriedades teóricas de convergência não foram bem estabelecidas nesse algoritmo, mas pequenas modificações permitem a demonstração da convergência dual. Destacam-se como adaptações o algoritmo do volume revisado, um método de feixes específico, e o algoritmo do volume incorporado ao método de variação do alvo. Este trabalho foi baseado no estudo desses algoritmos e de todos os conceitos envolvidos, em especial, análise convexa e otimização não diferenciável. Estudamos as principais diferenças teóricas desses métodos e realizamos comparações numéricas com problemas lineares e lineares inteiros, em particular, o corte máximo em grafos. / One way to solve large-scale linear programming problems is to exploit the Lagrangian relaxation of the difficult constraints and use subgradient methods. Such methods are popular as they give good approximations of dual solutions. Unfortunately, they do not directly yield primal solutions. Two alternatives to obtain primal solutions under reasonable computational cost are the construction of ergodic sequences and the use of the recently developed volume algorithm. While the convergence of ergodic sequences is well understood, the convergence properties of the volume algorithm is not well established in the original paper. This lead to some modifications of the original method to ease the proof of dual convergence. Three alternatives are the revised volume algorithm, a special case of the bundle method, and the volume algorithm incorporated by the variable target value method. The aim of this work is to study such algorithms and all related concepts, especially convex analysis and nondifferentiable optimization. We analysed the main theoretical differences among the methods and performed numerical experiments with linear and integer problems, in particular, the maximum cut problem on graphs.

Dans cette thèse, nous étudions le contrôle dynamique des systèmes de partage de ressources qui se posent dans divers domaines : réseaux de gestion des stocks, services de santé, réseaux de communication, etc. Nous visons à allouer efficacement les ressources disponibles entre des projets concurrents, selon certains critères de performance. Ce type de problème est de nature stochastique et peut être très complexe à résoudre. Nous nous concentrons donc sur le développement de méthodes heuristiques performantes. Dans la partie I, nous nous plaçons dans le cadre des Restless Bandit Problems, qui est une classe générale de problèmes d’optimisation dynamique stochastique. Relaxer la contrainte de trajectoire dans le problème d’optimisation permet de définir une politique d’index comme heuristique pour le modèle contraint d’origine, aussi appelée politique d’index de Whittle. Nous dérivons une expression analytique pour l’index de Whittle en fonction des probabilités stationnaires de l’état dans le cas où les bandits (ou projets) suivent un processus de naissance et de mort. D’une part, cette expression nécessite la vérification de plusieurs conditions techniques, d’autre part elle ne peut être calculée explicitement que dans certains cas spécifiques. Nous prouvons ensuite, que dans le cas particulier d’une file d’attente multi-classe avec abandon, la politique d’index de Whittle est asymptotiquement optimale aussi bien pour les régimes à faible trafic comme pour ceux à fort trafic. Dans la partie II, nous dérivons des heuristiques issues de l’approximation des systèmes stochastiques de partage de ressources par des modèles fluides déterministes. Nous formulons dans un premier temps une version fluide du problème d’optimisation relaxé que nous avons introduit dans la partie I, et développons une politique d’index fluide. L’index fluide peut toujours être calculé explicitement et surmonte donc les questions techniques qui se posent lors du calcul de l’index de Whittle. Nous appliquons les politiques d’index de Whittle et de l’index fluide à plusieurs cas : les fermes de serveurs éco-conscients, l’ordonnancement opportuniste dans les systèmes sans fil, et la gestion de stockage de produits périssables. Nous montrons numériquement que ces politiques d’index sont presque optimales. Dans un second temps, nous étudions l’ordonnancement optimal de la version fluide d’une file d’attente multi-classe avec abandon. Nous obtenons le contrôle optimal du modèle fluide en présence de deux classes de clients en concurrence pour une même ressource. En nous appuyant sur ces derniers résultats, nous proposons une heuristique pour le cas général de plusieurs classes. Cette heuristique montre une performance quasi-optimale lorsqu’elle est appliquée au modèle stochastique original pour des charges de travail élevées. Enfin, dans la partie III, nous étudions les phénomènes d’abandon dans le contexte d’un problème de distribution de contenu. Nous caractérisons une politique optimale de regroupement afin que des demandes issues d’utilisateurs impatients puissent être servies efficacement en mode diffusion. / In this thesis we study the dynamic control of resource-sharing systems that arise in various domains: e.g. inventory management, healthcare and communication networks. We aim at efficiently allocating the available resources among competing projects according to a certain performance criteria. These type of problems have a stochastic nature and may be very complex to solve. We therefore focus on developing well-performing heuristics. In Part I, we consider the framework of Restless Bandit Problems, which is a general class of dynamic stochastic optimization problems. Relaxing the sample-path constraint in the optimization problem enables to define an index-based heuristic for the original constrained model, the so-called Whittle index policy. We derive a closed-form expression for the Whittle index as a function of the steady-state probabilities for the case in which bandits (projects) evolve in a birth-and-death fashion. This expression requires several technical conditions to be verified, and in addition, it can only be computed explicitly in specific cases. In the particular case of a multi-class abandonment queue, we further prove that the Whittle index policy is asymptotically optimal in the light-traffic and heavy-traffic regimes. In Part II, we derive heuristics by approximating the stochastic resource-sharing systems with deterministic fluid models. We first formulate a fluid version of the relaxed optimization problem introduced in Part I, and we develop a fluid index policy. The fluid index can always be computed explicitly and hence overcomes the technical issues that arise when calculating the Whittle index. We apply the Whittle index and the fluid index policies to several systems: e.g. power-aware server-farms, opportunistic scheduling in wireless systems, and make-to-stock problems with perishable items. We show numerically that both index policies are nearly optimal. Secondly, we study the optimal scheduling control for the fluid version of a multi-class abandonment queue. We derive the fluid optimal control when there are two classes of customers competing for a single resource. Based on the insights provided by this result we build a heuristic for the general multi-class setting. This heuristic shows near-optimal performance when applied to the original stochastic model for high workloads. In Part III, we further investigate the abandonment phenomena in the context of a content delivery problem. We characterize an optimal grouping policy so that requests, which are impatient, are efficiently transmitted in a multi-cast mode.