This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

Demand-Side Management concepts have the potential to positively impact the financial as well as the environmental aspects of energy-intensive industries. More specifically, they allow reducing the energy cost for the industrial plants by dealing with energy-availability fluctuations. In this context, efficient frameworks for scheduling with energy awareness have been studied and showed potential to reduce the overall energy bill for energy-intensive industries, for instance stainless steel and paper plants. Those frameworks usually combine scheduling and energy optimization into one monolithic system. This work investigates the possibility of integrating the two systems by specific exchange of signals, while keeping the scheduling model separated from the energy-cost optimization model. Such integration means that the pre-existent schedulers and energy optimizers could be easily modified and reused without re-implementing the whole new system. Two industrial problems with different scheduling approaches are studied. The first problem is about pulp and paper production which uses the Resource Task Network (RTN) scheduling approach. The second one is about stainless steel production which is based on a bi-level heuristic implementation of an improved energy-aware scheduler. This work presents the decomposition methods that are available in literature and their application to the two industrial problems. Besides an improvement in the RTN approach for handling storages, this thesis describes a prototype implementation of the energy-aware RTN scheduler for paper and pulp production. Furthermore, this work investigates the performance of the application of different decomposition methods on different problem instances. The numerical case studies show that even though the decomposition decreases the solution quality compared to the monolithic system, it still gives good solutions within an acceptable duration with the advantage of having two separate pre-existent systems which are simply exchanging signals.

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Les exigences croissantes sur l'efficacité énergétique des bâtiments, l'évolution du {marché} énergétique, le développement technique récent ainsi que les particularités du poste de chauffage ont fait du MPC le meilleur candidat pour la régulation thermique des bâtiments à occupation intermittente. Cette thèse présente une méthodologie basée sur la commande prédictive distribuée visant un compromis entre l'optimalité, la simplicité et la flexibilité de l'implantation de la solution proposée. Le développement de l'approche est progressif : à partir du cas d'une seule zone, la démarche est ensuite étendue au cas multizone et / ou multisource, avec la prise en compte des couplages thermiques entre les zones adjacentes. Après une formulation quadratique du critère MPC pour mieux satisfaire les objectifs économiques du contrôle, la formulation linéaire est retenue. Pour répartir la charge de calcul, des méthodes de décomposition linéaire (comme Dantzig-Wolfe et Benders) sont employées. L'efficacité des algorithmes distribués proposés est illustrée par diverses simulations. / The increasing requirements on energy efficiency of buildings, the evolution of the energy market, the technical developments and the characteristics of the heating systems made of MPC the best candidate for thermal control of intermittently occupied buildings. This thesis presents a methodology based on distributed model predictive control, aiming a compromise between optimality, on the one hand, and simplicity and flexibility of the implementation of the proposed solution, on the other hand. The development of the approach is gradually. The mono-zone case is initially considered, then the basic ideas of the solution are extended to the multi-zone and / or multi-source case, including the thermal coupling between adjacent zones. Firstly we consider the quadratic formulation of the MPC cost function, then we pass towards a linear criterion, in order to better satisfy the economic control objectives. Thus, linear decomposition methods (such as Dantzig-Wolfe and Benders) represent the mathematical tools used to distribute the computational charge among the local controllers. The efficiency of the distributed algorithms is illustrated by simulations.

Cette thèse est dédiée à la résolution numérique tridimensionnelle des équations de Maxwell harmoniques, par des méthodes de décomposition de domaine couplant des résolutions par équations intégrales entre elles. Pour traiter les problèmes de diffraction d'ondes, la méthode des équations intégrales est un outil précieux. Elle consiste à paramétrer le champ électromagnétique solution par une source définie sur la surface de l'objet diffractant, solution d'une nouvelle équation linéaire (l'équation intégrale). Pour des applications à haute fréquence, le grand nombre d'inconnues (de l'ordre du million) nous oblige à utiliser un solveur itératif pour résoudre l'équation intégrale. Le problème du conditionnement des systèmes linéaires est alors crucial. De récents développements ont permis de construire une équation intégrale performante (la GCSIE) et de conditionnement stable avec la montée en fréquence. Cependant, la présence d'une cavité large et résonnante dans l'objet diffractant (telle que la cavité moteur d'un avion) dégrade le conditionnement de cette équation. Nous proposons deux méthodes de décomposition de domaine (DDM) afin de découpler le problème de la cavité du problème extérieur. La première (DDM en Y) s'exprime en fonction des opérateurs Dirichlet-to-Neumann Y, qui sont synthétisés via la résolution de problèmes métalliques par équations intégrales dans chaque sous-domaine. La seconde (DDM en S) s'exprime en fonction des opérateurs de scattering S, synthétisés par résolution de problèmes de type métal-impédant, donc bien posés à toute fréquence. La DDM en S permet ainsi de se débarrasser des phénomènes de résonance dans les cavités. Nous proposons dans un premier temps un préconditionneur analytique pour la DDM en Y, basé sur l'opérateur électromagnétique de simple couche. Nous calculons ensuite les modes guidés le long d'un cylindre infini tangent à la cavité près de l'interface, et nous diagonalisons les opérateurs Dirichlet-to-Neumann et scattering dans la base des traces de modes guidés sur l'interface. On extrait de cette étude deux préconditionneurs spectraux respectivement pour la DDM en Y et la DDM en S. Les résultats numériques confirment l'efficacité des préconditionneurs proposés / This work is dedicated to the numerical solution of the tridimensional harmonic Maxwell equations, using domain decomposition methods coupling integral equations between them. To deal with scattering problems, integral equations methods are a precious tool. They allow to look for the electromagnetic field by parameterizing it with a source only defined on the boundary of the scattering object, solution of a new linear equation (the integral equation). For applications at high frequency, the great number of unknowns forces the use of iterative methods. To accelerate the solution of integral equations, one moreover has to ensure the good condition number of the linear systems, or to propose well-suited preconditioners. An efficient method, the GCSIE, was developed in Onera. It is an intrinsically well-conditioned integral equation whose condition number remains stable whith the frequency increase. However, the existence of large and resonant cavities (such as air intakes) deteriorates the condition number. In order to circumvent this problem, we propose two domain decomposition methods (DDM) allowing to decouple the exterior problem from the problem of the cavities. The first one (Y-DDM) is based on Dirichlet-to-Neumann operators Y, which are built through the solution of metallic problems using integral equations in each subdomain. The second one (S-DDM) is based on scattering operators S, built through the solution of problems of metallic-impedant type, which are well-posed at any frequency. The S-DDM allows to avoid the resonance phenomena inside the cavities. First, we propose an analytic preconditioner for the Y-DDM, based on the electromagnetic single layer operator. We then calculate the modes guided along an artificial infinite cylinder, that is tangent to the cavity near the interface. We diagonalize the Dirichlet-to-Neumann and scattering operators in the basis of the traces of the guided modes on the interface. We deduce from this study two spectral preconditioners for the Y-DDM and the S-DDM. The numerical results confirm the efficiency of the employed preconditioners.

In this dissertation, we use the Natural transform decomposition method to obtain approximate analytical solution of fractional differential equations. This technique is a combination of decomposition methods and natural transform method. We use the Adomian decomposition, the homotopy perturbation and the Daftardar-Jafari methods as our decomposition methods. The fractional derivatives are considered in the Caputo and Caputo- Fabrizio sense. / Mathematical Sciences / M. Sc. (Applied Mathematics)

[pt] A programação estocástica dois estágios é uma abordagem matemática amplamente usada em aplicações da vida real, como planejamento da operação de sistemas de energia, cadeias de suprimentos, logística, gerenciamento de inventário e planejamento financeiro. Como a maior parte desses problemas não pode ser resolvida analiticamente, os tomadores de decisão utilizam métodos numéricos para obter uma solução quase ótima. Em algumas aplicações, soluções não convergidas e, portanto, sub-ótimas terminam sendo implementadas devido a limitações de tempo ou esforço computacional. Nesse contexto, os métodos existentes fornecem uma solução otimista sempre que a convergência não é atingida. As soluções otimistas geralmente geram altos níveis de arrependimento porque subestimam os custos reais na função objetivo aproximada. Para resolver esse problema, temos desenvolvido duas metodologias de solução conservadora para problemas de programação linear estocástica dois estágios com incerteza do lado direito e suporte retangular: Quando a verdadeira distribuição de probabilidade da incerteza é conhecida, propomos um problema DRO (Distributionally Robust Optimization) baseado em esperanças condicionais adaptadas à uma partição do suporte cuja complexidade cresce exponencialmente com a dimensionalidade da incerteza; Quando apenas observações históricas da incerteza estão disponíveis, propomos um problema de DRO baseado na métrica de Wasserstein a fim de incorporar ambiguidade sobre a real distribuição de probabilidade da incerteza. Para esta última abordagem, os métodos existentes dependem da enumeração dos vértices duais do problema de segundo estágio, tornando o problema DRO intratável em aplicações práticas. Nesse contexto, propomos esquemas algorítmicos para lidar com a complexidade computacional de ambas abordagens. Experimentos computacionais são apresentados para o problema do fazendeiro, o problema de alocação de aviões, e o problema do planejamento da operação do sistema elétrico (unit ommitmnet problem). / [en] Two-stage stochastic programming is a mathematical framework widely used in real-life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision-makers make use of numerical methods to obtain a near-optimal solution. Some applications rely on the implementation of non-converged and therefore sub-optimal solutions because of computational time or power limitations. In this context, the existing methods provide an optimistic solution whenever convergence is not attained. Optimistic solutions often generate high disappointment levels because they consistently underestimate the actual costs in the approximate objective function. To address this issue, we have developed two conservative-solution methodologies for two-stage stochastic linear programming problems with right-hand-side uncertainty and rectangular support: When the actual data-generating probability distribution is known, we propose a DRO problem based on partition-adapted conditional expectations whose complexity grows exponentially with the uncertainty dimensionality; When only historical observations of the uncertainty are available, we propose a DRO problem based on the Wasserstein metric to incorporate ambiguity over the actual data-generating probability distribution. For this latter approach, existing methods rely on dual vertex enumeration of the second-stage problem rendering the DRO problem intractable in practical applications. In this context, we propose algorithmic schemes to address the computational complexity of both approaches. Computational experiments are presented for the farmer problem, aircraft allocation problem, and the stochastic unit commitment problem.