Les réseaux électriques doivent absorber une production d'énergie renouvelable croissante, de façon décentralisée. Leur gestion optimale amène à des problèmes spécifiques. Nous étudions dans cette thèse la formulation mathématique de tels problèmes en tant que problèmes d'optimisation stochastique multi-pas de temps. Nous analysons plus spécifiquement la décomposition en temps et en espace de tels problèmes. Dans la première partie de ce manuscrit, Décomposition temporelle pour l'optimisation de la gestion de microgrid domestique, nous appliquons les méthodes d'optimisation stochastique à la gestion de microgrid de petite taille. Nous comparons différents algorithmes d'optimisation sur deux exemples: le premier considère une microgrid domestique équipée avec une batterie et une centrale de micro-cogénération; le deuxième considère quant à lui une autre microgrid domestique, cette fois équipée avec une batterie et des panneaux solaires. Dans la seconde partie, Décomposition temporelle et spatiale de problèmes d'optimisation de grande taille, nous étendons les études précédentes à des microgrids de plus grandes tailles, avec différentes unités et stockages connectés ensemble. La résolution frontale de tels problèmes de grande taille par Programmation Dynamique s'avère impraticable. Nous proposons deux algorithmes originaux pour pallier ce problème en mélangeant une décomposition temporelle avec une décomposition spatiale --- par les prix ou par les ressources. Dans la dernière partie, Contributions à l'algorithme Stochastic Dual Dynamic Programming, nous nous concentrons sur l'algorithme emph{Stochastic DualDynamic Programming} (SDDP) qui est actuellement une méthode de référence pour résoudre des problèmes d'optimisation stochastique multi-pas de temps. Nous étudions un nouveau critère d'arrêt pour cet algorithme basé sur une version duale de SDDP, qui permet d'obtenir une borne supérieure déterministe pour le problème primal / New energy systems are designed to absorb a large share of renewableenergy in a decentralized fashion. Their optimized management raises specificissues. We study mathematical formulation as large scale multistagestochastic optimization problems. We focus on time and space decompositionmethods in a stochastic setting.In the first part of this manuscript, Time decomposition inoptimization and management of home microgrids, we apply stochasticoptimization algorithms to the management of small scale microgrids. We compare different optimization algorithms on two examples:a domestic microgrid equipped with a microCombined Heat and Power generator and a battery;a domestic microgrid equipped with a battery and solar panels.In the second part, Mixing time and spatial decomposition inlarge-scale optimization problems, we extend the previous studies tolarger microgrids, where different units and storage devices are connected together. As a direct resolution by Dynamic Programming of such large scale systemsis untractable, we propose original algorithms mixing time decomposition on the one hand, and price and resource spatial decomposition on the other hand.In the third part, Contributions to Stochastic Dual Dynamic Programming,we focus on the Stochastic Dual Dynamic Programming (SDDP) algorithm,a well-known algorithm to solve multistage stochastic optimizationproblems. We present a new stopping criteria based on a dual versionof SDDP which gives a deterministic upper-bound for the primal problem

The fuel usage of a hybrid electric vehicle can be reduced by strategically combining the usage of the combustion engine with the electric motor. One method to determine an optimal split between the two is to use dynamic programming. However, the amount of computations grows exponentially with the amount of states which makes its usage difficult on sequential hardware. This thesis project explores the usage of FPGAs for speeding up the required computations to possibly allow the optimisation to run in real time in the vehicle. A tool to convert a vehicle model to a hardware description language was developed and evaluated. The current version does not run fast enough to run in real time, but some optimisations which would allow that are proposed.

Esta tese é dedicada ao estudo de modelos de otimização de carteiras de investimento multi-período. Daremos ênfase a um modelo com restrições intermediárias formulado como um problema de controle ótimo e resolvido utilizando técnicas de programação dinâmica. Serão tratados aspectos teóricos e práticos desta classe de problemas. Primeiramente faremos uma revisão das principais hipóteses dos modelos de otimização de carteiras e o caso uni-período. Analisaremos a seguir as generalizações para o caso multi-período, onde os modelos utilizam apenas restrições para o valor esperado e/ou para a variância da carteira no instante final do período analisado. Apresentaremos então o principal resultado proposto neste trabalho onde consideramos o problema de seleção ótima de ativos multi-período no qual podemos incorporar ao modelo restrições intermediárias para o valor esperado e variância da carteira durante o período de análise. A grande vantagem desta técnica é permitir o controle do valor esperado e/ou da variância da carteira ao longo de todo o horizonte de análise. Faremos uma comparação o entre as formulações apresentadas e realizaremos experimentos numéricos com o modelo proposta nesta tese. Os principais resultados originais desta tese encontram-se no Capítulo 5. No Capítulo 6 apresentamos as simulações numéricas realizadas com o modelo proposto. / The subject of this thesis is the study of multi-period portfolio optimization problems. We focus on a model with intermediate constraints formulated as an optimal control problem and solved by using dynamic programming techniques. Both theoretical and practical issues are addressed. Firstly we will analyze the main hypothesis of portfolio optimization models and the single period case. Then we will present the generalization for the multi-period case, where the models use only constraints for the expected value and variance at the final period. The main result proposed in this work considers the multi-period portfolio selection problem with intermediate constraints on the expected value and variance of the portfolio taken into account in the optimization problem. The main advantage of this technique is that it is possible to control the intermediate expected value or variance of the portfolio during the time horizon considered. Comparison between the presented formulations and numerical experiments of the proposed model will be exposed. The main original results of this thesis can be found in Chapter 5. In Chapter 6 we present numerical simulations with the proposed model.

Cette thèse articule plusieurs questions importantes dans les opérations durables et la gestion de la chaîne d'approvisionnement, non seulement afin de fournir des idées pour améliorer la performance des entreprises, mais aussi pour inciter ces dernières à adopter les moyens appropriés pour un meilleur environnement de notre société. Le lien entre le niveau de l'entreprise et le niveau de la société est que l'amélioration de la performance écologique par une meilleure gestion des opérations dans les entreprises et les chaînes d'approvisionnement est un élément indispensable pour améliorer l'environnement dans notre société. Prenons la Chine comme exemple. Depuis quelques années, le gouvernement a commencé à favoriser toutes les initiatives pour résoudre les problèmes de pollution de l'air. Un moyen important et utile est de mettre en place une réglementation stricte et de surveiller les efforts des entreprises qui seront passibles d'amendes sérieuses si certaines normes ne sont pas respectées par des inspections aléatoires. Par conséquent, les entreprises doivent coopérer pour améliorer leur rentabilité et, plus important encore, leurs impacts environnementaux. Grâce à cet effort prolongé, malgré le fait que la situation future est incertaine, la qualité de l'air s'est progressivement améliorée en Chine. Cette thèse, dans un cadre plus général, vise à fournir aux entreprises des informations importantes afin qu'elles soient non seulement en mesure de respecter la réglementation, mais aussi en mesure d'apporter véritablement leur contribution à la construction d'un environnement meilleur pour les générations futures. Notre objectif fondamental est d'obtenir une compréhension approfondie des compromis auxquels les entreprises sont confrontées, de modéliser les problèmes de recherche de solutions possibles et d'aider les entreprises/chaînes d'approvisionnement à améliorer leur performance d'un point de vue théorique. Ensuite, la thèse aidera indirectement les entreprises à réaliser l'importance du développement de moyens de gestion durable des opérations et de la chaîne d'approvisionnement sur notre société. La thèse est organisée comme la structure suivante. Le chapitre 3 est le premier essai, Environmental Collaboration and Process Innovation in Supply Chain Management with Coordination. Le chapitre 4 comprend le contenu du deuxième essai, Remanufacturing of Multi-Component Systems with Product Substitution, et le troisième essai, Joint Dynamic Pricing and Return Quality Strategies Under Demand Cannibalization , est présenté au chapitre 5. Le chapitre 6 donne les remarques finales générales des trois essais, suivies de la liste de référence, et les annexes. / This thesis articulates several important issues in sustainable operations and supply chain management not only to provide insights for enhancing the performance of firms but also to appeal to the enterprises to adopt appropriate means for a better environment of our society. The link from firm level to society level is that, to improve the green performance through better operations management efficiency in firms and supply chains, is an indispensable element to ameliorate the environment in our society. Taking China as an example. Since a few years ago (The Straitstimes, 2017; Stanway & Perry, 2018), the government started to spare no effort in resolving the air pollution problems. An important and useful means is to put strict regulations and monitoring the efforts of firms which will face serious fine if certain standards are not met by random inspection. Therefore, firms have to cooperate for the betterment of its profitability and, more importantly, the environmental impacts. Throughout the endeavor, despite the uncertain future situation, the air quality has gradually improved in China (Zheng, 2018). This thesis, in a more general setting, aims to provide important insights to firms so that they are not only able to meet the regulations but genuinely to make contributions to building a better environment for our future generations. Basically, our goal is to obtain deep understanding of the trade-offs with which companies are faced, and to model the problems for seeking possible solutions and helping firms/supply chains to enhance their performance from a theoretical point of view. Then, indirectly, the work will help firms to realize the importance of developing sustainable operations and supply chain management means on our society. The structure of the thesis is organized as follows. Chapter 2 introduces the thesis in French. Chapter 3 is the first essay, Environmental Collaboration and Process Innovation in Supply Chain Management with Coordination. Chapter 4 includes the contents of the second essay, Remanufacturing of Multi-Component Systems with Product Substitution , and the third essay, Joint Dynamic Pricing and Return Quality Strategies Under Demand Cannibalization, is introduced in Chapter 5. Chapter 6 gives the general concluding remarks of the three essays which is followed by the reference list and the appendices.

by Lo Kam Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 107-111). / Chapter 1 --- Introduction --- p.7 / Chapter 1.1 --- The Goal --- p.8 / Chapter 1.2 --- The Work of the Thesis --- p.9 / Chapter 1.3 --- Layout of Thesis --- p.9 / Chapter 2 --- The Unit Commitment Problem --- p.11 / Chapter 2.1 --- What is UCP? --- p.11 / Chapter 2.1.1 --- Why is UCP difficult? --- p.12 / Chapter 2.1.2 --- Costs --- p.12 / Chapter 2.1.3 --- Constraints --- p.13 / Chapter 2.2 --- Mathematical Formulation --- p.15 / Chapter 2.3 --- Literature Review --- p.19 / Chapter 2.3.1 --- Exhaustive Enumeration --- p.19 / Chapter 2.3.2 --- Priority List --- p.20 / Chapter 2.3.3 --- Langragian Relaxation --- p.21 / Chapter 2.3.4 --- Neural Network --- p.21 / Chapter 2.3.5 --- Genetic Algorithms --- p.22 / Chapter 2.3.6 --- Dynamic Programming --- p.22 / Chapter 3 --- Genetic Algorithms --- p.24 / Chapter 3.1 --- Introduction --- p.24 / Chapter 3.1.1 --- Outline of Traditional GA --- p.25 / Chapter 3.2 --- Basic elements --- p.26 / Chapter 3.2.1 --- Coding --- p.26 / Chapter 3.2.2 --- Fitness Function --- p.26 / Chapter 3.2.3 --- Selection and Reproduction --- p.26 / Chapter 3.2.4 --- Mutation --- p.28 / Chapter 3.2.5 --- Replacement --- p.29 / Chapter 3.2.6 --- Epistasis --- p.29 / Chapter 3.2.7 --- A Simple Example --- p.30 / Chapter 3.3 --- Exploration vs Exploitation --- p.33 / Chapter 3.4 --- Constraints Handlings --- p.34 / Chapter 3.4.1 --- Penalty Function --- p.35 / Chapter 3.4.2 --- Proper Encoding --- p.36 / Chapter 3.4.3 --- Repair Algorithms --- p.36 / Chapter 4 --- Dynamic Programming --- p.37 / Chapter 4.1 --- Introduction --- p.37 / Chapter 4.1.1 --- Decomposition --- p.38 / Chapter 4.2 --- Mathematical Formulation --- p.43 / Chapter 4.3 --- A Simple Example --- p.44 / Chapter 5 --- DP Crossover Operator (DPX) --- p.50 / Chapter 5.1 --- Why DP is chosen as the crossover operator --- p.50 / Chapter 5.2 --- What is DPX? --- p.51 / Chapter 5.2.1 --- A Simple Example --- p.51 / Chapter 5.2.2 --- Mechanism of DPX --- p.58 / Chapter 5.3 --- Properties of DPX --- p.63 / Chapter 5.3.1 --- Number of parents --- p.63 / Chapter 5.3.2 --- Crossover Sites --- p.65 / Chapter 5.3.3 --- Perservation of Feasibility --- p.66 / Chapter 6 --- Implementation --- p.69 / Chapter 6.1 --- GA Construction --- p.69 / Chapter 6.1.1 --- Coding --- p.69 / Chapter 6.1.2 --- Fitness Function --- p.70 / Chapter 6.1.3 --- Selection --- p.72 / Chapter 6.1.4 --- Crossover --- p.76 / Chapter 6.1.5 --- Mutation Rate --- p.76 / Chapter 6.1.6 --- Replacement --- p.77 / Chapter 6.2 --- Algorithm --- p.77 / Chapter 6.3 --- Optimal Power Generations for Fuel Costs --- p.80 / Chapter 6.3.1 --- The Simple Scheduling Method --- p.80 / Chapter 7 --- Experimental Results --- p.84 / Chapter 7.1 --- Experiment Details --- p.84 / Chapter 7.2 --- Problem A --- p.86 / Chapter 7.2.1 --- Data Results --- p.86 / Chapter 7.2.2 --- Graphical Results --- p.90 / Chapter 7.2.3 --- Analysis --- p.90 / Chapter 7.3 --- Problem B --- p.92 / Chapter 7.3.1 --- Data Results --- p.92 / Chapter 7.3.2 --- Graphical Results --- p.94 / Chapter 7.3.3 --- Analysis --- p.96 / Chapter 8 --- Conclusion and Future Work --- p.97 / Chapter 8.1 --- Conclusion --- p.97 / Chapter 8.2 --- Future Work --- p.98 / Chapter A --- Problems Parameters --- p.100 / Chapter A.1 --- Problem A --- p.100 / Chapter A.1.1 --- Parameters of Generating Units --- p.101 / Chapter A.2 --- Problem B --- p.103 / Chapter A.2. --- 1 Parameters of Generating Units --- p.104

To Chi Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 50-52). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Structure of thesis --- p.3 / Chapter 2 --- Literature Review --- p.4 / Chapter 3 --- Problem description and formulation --- p.7 / Chapter 3.1 --- Notation --- p.9 / Chapter 3.2 --- Cost Structure --- p.11 / Chapter 3.3 --- Assumptions --- p.12 / Chapter 3.4 --- Problem Formulation --- p.14 / Chapter 4 --- Stations with deterministic demand --- p.15 / Chapter 4.1 --- Greedy Algorithm --- p.15 / Chapter 4.2 --- Example --- p.16 / Chapter 4.3 --- Properties --- p.17 / Chapter 5 --- Stations with stochastic demand --- p.21 / Chapter 5.1 --- Decision planned before arrival --- p.26 / Chapter 5.2 --- Decision made after vehicle arrival --- p.29 / Chapter 6 --- Numerical example --- p.38 / Chapter 6.1 --- Comparing decision made before and after arrival of sta- tion --- p.39 / Chapter 6.2 --- Relation between K and li --- p.40 / Chapter 6.3 --- Relation between unit penalty / cost value with K . . . --- p.40 / Chapter 7 --- Conclusion --- p.47

Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.

Uma revisão dos algoritmos e conjuntos de instâncias presentes na literatura do Problema da Mochila com Repetições (PMR) é apresentada nessa dissertação de mestrado. Os algoritmos e conjuntos de instâncias usados são brevemente descritos nesse trabalho, afim de que o leitor tenha base para entender as discussões. Algumas propriedades bem conhecidas e específicas do PMR, como a dominância e a periodicidade, são explicadas com detalhes. O PMR é também superficialmente estudado no contexto de problemas de avaliação gerados pela abordagem de geração de colunas aplicada na relaxação contínua do Bin Packing Problem (BPP) e o Cutting Stock Problem (CSP). Múltiplos experimentos computacionais e comparações são realizadas. Para os conjuntos de instâncias artificiais mais recentes da literatura, um simples algoritmo de programação dinâmica, e uma variante do mesmo, parecem superar o desempenho do resto dos algoritmos, incluindo aquele que era estado-da-arte. O modo que relações de dominância é aplicado por esses algoritmos de programação dinâmica têm algumas implicações para as relações de dominância previamente estudadas na literatura. O autor dessa dissertação defende a tese de que a escolha dos conjuntos de instâncias artificiais definiu o que foi considerado o melhor algoritmo nos trabalhos anteriores. O autor dessa dissertação disponibilizou publicamente todos os códigos e conjuntos de instâncias referenciados nesse trabalho. / A review of the algorithms and datasets in the literature of the Unbounded Knapsack Problem (UKP) is presented in this master's thesis. The algorithms and datasets used are brie y described in this work to provide the reader with basis for understanding the discussions. Some well-known UKP-speci c properties, such as dominance and periodicity, are described. The UKP is also super cially studied in the context of pricing problems generated by the column generation approach applied to the continuous relaxation of the Bin Packing Problem (BPP) and Cutting Stock Problem (CSP). Multiple computational experiments and comparisons are performed. For the most recent arti cial datasets in the literature, a simple dynamic programming algorithm, and its variant, seems to outperform the remaining algorithms, including the previous state-of-the-art algorithm. The way dominance is applied by these dynamic programming algorithms has some implications for the dominance relations previously studied in the literature. In this master's thesis we defend that choosing sets of arti cial instances has de ned what was considered the best algorithm in previous works. We made available all codes and datasets referenced in this master's thesis.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by Joseph A. Amram. / B.S.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: p. 254-259. / by Asher Reuveni. / Ph.D.