Dissertations / Theses on the topic 'Hybrid programming models'

Increasingly tighter budgets, changes in government role/function, declines in staff resources, and demands for increased accountability in the transportation field have brought unprecedented challenges for state transportation officials at all management levels. Systematic methodologies for effective management of a specific type of infrastructure (e.g., pavement and bridges) as well as for holistically managing all types of infrastructure assets are being developed to approach these challenges. In particular, the intrinsic characteristics of highway system make the use of multi-objective optimization techniques particularly attractive for managing highway assets. Recognizing the need for effective tradeoff tools and the limitations of state-of-practice analytical models and tools in highway asset management, the main objective of this dissertation was to develop a performance-based asset management framework that uses multi-objective optimization techniques and consists of stand-alone but logically interconnected optimization models for different management levels. Based on a critical review of popular multi-objective optimization techniques and their applications in highway asset management, a synergistic integration of complementary multi-criteria optimization techniques is recommended for the development of practical and efficient decision-supporting tools. Accordingly, the dissertation first proposes and implements a probabilistic multi-objective model for performance-based pavement preservation programming that uses the weighting sum method and chance constraints. This model can handle multiple incommensurable and conflicting objectives while considering probabilistic constraints related to the available budget over the planning horizon, but is found more suitable to problems with small number of objective functions due to its computational intensity. To enhance the above model, a hybrid model that requires less computing time and systematically captures the decision maker's preferences on multiple objectives is developed by combining the analytic hierarchy process and goal programming. This model is further extended to also capture the relative importance existent within optimization constraints to be suitable for allocations of funding across multiple districts for a decentralized state department of transportation. Finally, as a continuation of the above proposed models for the succeeding management level, a project selection model capable of incorporating qualitative factors (e.g. equity, user satisfaction) into the decision making is developed. This model combines k-means clustering, analytic hierarchy process and integer linear programming. All the models are logically interconnected in a comprehensive resource allocation framework. Their feasibility, practicality and potential benefits are illustrated through various case studies and recommendations for further developments are provided.
Ph. D.

La thèse porte sur la résolution des problèmes de satisfaisabilité booléenne (SAT) dans un cadre massivement parallèle. Le problème SAT est largement utilisé pour résoudre des problèmes combinatoires de première importance comme la vérification formelle de matériels et de logiciels, la bio-informatique, la cryptographie, la planification et l’ordonnancement de tâches. Plusieurs contributions sont apportées dans cette thèse. Elles vont de la conception d’algorithmes basés sur les approches « portfolio » et « diviser pour mieux régner », à l’adaptation de modèles de programmation parallèle, notamment hybride (destinés à des architectures à mémoire partagée et distribuée), à SAT, en passant par l’amélioration des stratégies de résolution. Ce travail de thèse a donné lieu à plusieurs contributions dans des conférences internationales du domaine ainsi qu’à plusieurs outils (open sources) de résolution des problèmes SAT, compétitifs au niveau international
This thesis deals with propositional satisfiability (SAT) in a massively parallel setting. The SAT problem is widely used for solving several combinatorial problems (e.g. formal verification of hardware and software, bioinformatics, cryptography, planning, scheduling, etc.). The first contribution of this thesis concerns the design of efficient algorithms based on the approaches « portfolio » and « divide and conquer ». Secondly, an adaptation of several parallel programming models including hybrid (parallel and distributed computing) to SAT is proposed. This work has led to several contributions to international conferences and highly competitive distributed SAT solvers

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Nos últimos anos, as linguagens de script ganharam muita importância em diversas áreas da computação. Uma das áreas onde essas linguagens ainda são pouco exploradas é na área de computação paralela. A computação paralela sempre foi fortemente associada a computação científica, mas recentemente ela ganhou uma nova importância com a popularização de processadores multi-core. Com esse crescimento se torna necessário o surgimento de novos paradigmas de programação paralela para facilitar o desenvolvimento e dinamizar as aplicações, e linguagens de script podem ser usadas para isso, trazendo dinamismo, simplicidade e flexibilidade às aplicações. Esta dissertação visa estudar um modelo híbrido de programação entre duas linguagens de programação, Lua e Charm plus plus.
Recently, scripting languages have become very important in many fields of computer science. One area in which these languages have not been explored is paralel programming. Paralel programming has always been strongly associated with scientific usage, but recently, with the growth in popularity of multi core systems, it has gained a new field of action. With this change, the development of new programming paradigms of paralel programming become necessary in order to make development easier and applications more dynamic. Scripting languages may be used for this, bringing dynamics, flexibility and simplicity to aplications. This dissertation aims to study a hybrid programming model with two programming languages, Charm plus plus and Lua.

This thesis is concerned with different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems. The main goal is to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control. The concepts of multi-parametric programming and explicit model predictive control are presented in detail, and it is shown how the solution to explicit model predictive control may be efficiently computed using a combination of multi-parametric programming and dynamic programming. A novel algorithm for constrained dynamic programming of mixed-integer linear problems is proposed and illustrated with a numerical example that arises in the context of inventory scheduling. Based on the developments on constrained dynamic programming of mixed-integer linear problems, an algorithm for explicit model predictive control of hybrid systems with linear cost function is presented. This method is further extended to the design of robust explicit controllers for hybrid linear systems for the case when uncertainty is present in the model. The final part of the thesis is concerned with developments in nonlinear explicit model predictive control. By using suitable model reduction techniques, the model captures the essential nonlinear dynamics of the system, while the achieved reduction in dimensionality allows the use of nonlinear multi-parametric programming methods.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Nesta tese são apresentados três modelos de programação matemática que abordam os problemas de otimização relacionados ao gerenciamento da energia nos sistemas de distribuição de energia elétrica (SDEE), como: 1) Programação ótima das entregas e carregamento dos veículos elétricos (VEs) durante a navegação em um mapa de cidade, 2) Gerenciamento ótimo pelo lado da demanda considerando um sistema fotovoltaico híbrido (SFH) em uma residência em baixa tensão (RBT) no SDEE, e 3) O melhoramento do fator de carga (FC) do SDEE através do controle da demanda. O primeiro problema visa minimizar os custos relacionados com a manutenção e geração de horas extra durante a operação de uma frota de VEs, levando em conta um conjunto de entregas pre-especificadas, assim como, pontos de carregamento alocados ao longo de cada via urbana (principal e/ou secundária) pertencente ao mapa da cidade. No segundo problema, para uma residência em baixa tensão é planejado um perfil ótimo de consumo para o dia seguinte. Este perfil de consumo é obtido através de um programa de gerenciamento pelo lado da demanda (GLD) que considera uma estrutura tarifária e um esquema de operação que otimiza os recursos energéticos vindos de um SFH e o SDEE. Para cada problema de otimização é apresentado o seu correspondente modelo de programação não linear inteiro misto (PNLIM). O terceiro problema visa minimizar os custos por compra de energia (consumo e perdas de potência ativa) da concessionária, levando em conta, o controle da demanda dos usos-finais, presentes nas unidades consumidoras (residenciais, comerciais, e industriais) no SDEE. As incertezas na utilização dos usos-finais nas unidades consumidoras são simuladas através de um algoritmo Monte Carlo. Além disso, o modelo proposto PIMRQ é rodado dentro de um processo iterativo, que visa a melhoria do FC do SDEE. Por outro lado, através destes modelos não-lineares, a solução ótima global não é garantida, enquanto o uso de modelos equivalentes (para o primeiro e segundo problema, sendo um modelo aproximado para o terceiro) de programação linear inteira mista (PLIM) resolvidos por ferramentas de otimização clássica existentes garantem a convergência para a solução ótima global. Por conseguinte, para resolver este inconveniente, os seus modelos MILP equivalentes são obtidos e explicados em detalhe. Os modelos propostos foram implementados na linguagem de modelagem algébrica AMPL e resolvidos usando o solver comercial CPLEX. Além disso, algoritmos de simulação para representar as incertezas dos tempos de demora na operação dos VEs e os hábitos de utilização dos usos-finais durante o dia, são desenvolvidos. Um grafo unidirecional de 71 nós, uma rede elétrica IEEE de 34 nós, e 21 usos-finais (incluído um VE plug-in para o carregamento na residência) residenciais são utilizados para testar a precisão e a eficiência, assim como, também técnica de solução dos modelos propostos para cada problema.
This thesis presents three mathematical programming models to address the optimization problems related to the energy management in the electricity distribution systems (EDSs), such as: 1) Optimal delivery scheduling and charging of electric vehicles (EVs) in the navigation of a city map, 2) Optimal demand side management of an EDS considering a hybrid photovoltaic system (HPS) in a residential low voltage (RLV), and 3) Load factor improvement through the demand control in the EDS. The first problem aims at minimizing the costs related to the maintenance and generation of extra hours during the operation of a EVs fleet, taking into account a number of prespecified deliveries, as well as charging points allocated along each urban road (main or secondary) belongs to the city map. In the second problem, for a RLV, an optimal consumption profile of a day-ahead is planned. This consumption profile is obtained through a demand side management (DSM) program that considers a tariff structure and an operating scheme that optimizes the energy resources coming from HFS and EDS. The third problem aims at minimizing the costs of energy purchase (consumption and active energy losses) of the company, taking into account, the demand control of the end-uses, presents in the consumers units (residential, commercial, and industrial) in the EDS. Uncertainties in the use of the end-uses in the different consumer units are simulated through a Monte Carlo algorithm that determines a habitual consumption profile for EDSs. Based on this habitual profile, the proposed MIPRQ model determines an optimal profile for EDSs. This model uses an iterative process that aims to improve the load factor of the EDS. For each optimization problem the corresponding non-linear mixed integer programming (NLMIP) model is presented. On the other hand, via these nonlinear models, the global optimal solution is not guaranteed, while using the equivalent mixed-integer linear (MILP) models (for the first and second problems, being an approximate model for the third) and solving them by existing classical optimization tools ensures convergence to global optimal solution. Therefore, in order to address this drawback, their equivalent mixed integer linear programming (MILP) models are obtained and explained in detail. The proposed models are implemented in the algebraic modeling language AMPL and solved using the commercial CPLEX solver. Moreover, simulations algorithms to represent the uncertainties of delay times in the operation of EVs and usage habits of end-uses during the day, are developed. A multidirectional graph with 71 nodes, an electrical network IEEE 34 nodes, and a quantity of 21 residential end-uses (including an EV plug-in for residential charging) are used to test the precision and the efficiency, as well as the solution technique of the models proposed for each problem.
CNPq: 141462/2013- 2

Orientador: Marcos Júlio Rider Flores
Resumo: Nesta tese são apresentados três modelos de programação matemática que abordam os problemas de otimização relacionados ao gerenciamento da energia nos sistemas de distribuição de energia elétrica (SDEE), como: 1) Programação ótima das entregas e carregamento dos veículos elétricos (VEs) durante a navegação em um mapa de cidade, 2) Gerenciamento ótimo pelo lado da demanda considerando um sistema fotovoltaico híbrido (SFH) em uma residência em baixa tensão (RBT) no SDEE, e 3) O melhoramento do fator de carga (FC) do SDEE através do controle da demanda. O primeiro problema visa minimizar os custos relacionados com a manutenção e geração de horas extra durante a operação de uma frota de VEs, levando em conta um conjunto de entregas pre-especificadas, assim como, pontos de carregamento alocados ao longo de cada via urbana (principal e/ou secundária) pertencente ao mapa da cidade. No segundo problema, para uma residência em baixa tensão é planejado um perfil ótimo de consumo para o dia seguinte. Este perfil de consumo é obtido através de um programa de gerenciamento pelo lado da demanda (GLD) que considera uma estrutura tarifária e um esquema de operação que otimiza os recursos energéticos vindos de um SFH e o SDEE. Para cada problema de otimização é apresentado o seu correspondente modelo de programação não linear inteiro misto (PNLIM). O terceiro problema visa minimizar os custos por compra de energia (consumo e perdas de potência ativa) da concessionária, levando em conta, o co... (Resumo completo, clicar acesso eletrônico abaixo)
Doutor

The new emission regulations for new trucks was made to decrease the CO2 emissions by 30% from 2020 to 2030. One of the solutions is hybridizing the truck powertrain with 48V or 600V that can recover brake energy with electrical machines and batteries. The control of this hybrid powertrain is key to increase fuel efficiency. The idea behind this approach is to combine two different power sources, an internal combustion engine and a battery driven electric machine, and use both to provide tractive forces to the vehicle. This approach requires a HEV controller to operate the power flow within the systems. The HEV controller is the key to maximize fuel savings which contains an energy management strategy. It uses the knowledge of the road profile ahead by GPS and maps, and strongly interacts with the control of the cruise speed, automated gear shifts, powertrain modes and state of charge. In this master thesis, the dynamic programming strategy is used as predictive energy management for hybrid electric truck in forward- facing simulation environment. An analysis of predictive energy management is thus done for receding and full horizon length on flat and hilly drive cycle, where fuel consumption and recuperation energy will be regarded as the primary factor. Another important factor to consider is the powertrain mode of the vehicle with different penalty values. The result from horizon study indicates that the long receding horizon length has a benefit to store more recuperative energy. The fuel consumption is decreased for all drive cycle in the comparison with existing Volvo’s strategy.

The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control methods is Model Predictive Control (MPC). In each sampling time, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem, which is known to have a computational complexity which grows exponentially in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel. To estimate the information originally sent, a maximum likelihood problem involving binary variables can be solved. The process of simultaneously estimating the information sent by multiple users is called Multiuser Detection (MUD). In this thesis, the problem to efficiently solve MIQP problems originating from MPC and MUD is addressed. Four different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In numerical experiments, the algorithm is applied to unconstrained MPC problems with a mixture of real valued and binary valued control signals, and the result shows that the performance gain can be significant compared to solving the problem using branch and bound. The preprocessing algorithm has also been applied to the MUD problem, where simulations have shown that the bit error rate can be significantly reduced compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the proposed QP solver and MIQP solver have lower computational complexity compared to corresponding generic solvers. Third, the dual active set QP algorithm is enhanced using ideas from gradient projection methods. The performance of this enhanced algorithm is shown to be comparable with the existing commercial state-of-the-art QP solver \cplex for some random linear MPC problems. Fourth, an algorithm for efficient computation of the search directions in an SDP solver for a proposed alternative SDP relaxation applicable to MPC problems with binary control signals is presented. The SDP relaxation considered has the potential to give a tighter lower bound on the optimal objective function value compared to the QP relaxation that is traditionally used in branch and bound for these problems, and its computational performance is better than the ordinary SDP relaxation for the problem. Furthermore, the tightness of the different relaxations is investigated both theoretically and in numerical experiments.
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The automotive industry is required to deal with increasingly stringent legislationfor greenhouse gases. Hybrid Electric Vehicles, HEV, are gaining acceptance as thefuture path of lower emissions and fuel consumption. The increased complexityof multiple prime movers demand more advanced control systems, where futuredriving conditions also becomes interesting. For a plug-in Hybrid Electric Vehicle,PIHEV, it is important to utilize the comparatively inexpensive electric energybefore the driving cycle is complete, this for minimize the cost of the driving cycle,since the battery in a PIHEV can be charged from the grid. A strategy with lengthinformation of the driving cycle from a global positioning system, GPS, couldreduce the cost of driving. This by starting to blend the electric energy with fuelearlier, a strategy called blended driving accomplish this by distribute the electricenergy, that is charged externally, with fuel over the driving cycle, and also ensurethat the battery’s minimum level reaches before the driving cycle is finished. Astrategy called Charge Depleting Charge Sustaining, CDCS, does not need lengthinformation. This strategy first depletes the battery to a minimum State of Charge,SOC, and after this engages the engine to maintain the SOC at this level. In thisthesis, a variable SOC reference is developed, which is dependent on knowledgeabout the cycle’s length and the current length the vehicle has driven in the cycle.With assistance of a variable SOC reference, is a blended strategy realized. Thisis used to minimize the cost of a driving cycle. A comparison between the blendedstrategy and the CDCS strategy was done, where the CDCS strategy uses a fixedSOC reference. During simulation is the usage of fuel minimized; and the blendedstrategy decreases the cost of the driving missions compared to the CDCS strategy.To solve the energy management problem is a model predictive control used. Thedesigned control system follows the driving cycles, is charge sustaining and solvesthe energy management problem during simulation. The system also handlesmoderate model errors.
Fordonsindustrin måste hantera allt strängare lagkrav mot utsläpp av emissioneroch växthusgaser. Hybridfordon har börjat betraktas som den framtida vägenför att ytterligare minska utsläpp och användning av fossila bränslen. Den ökadekomplexiteten från flera olika motorer kräver mera avancerade styrsystem. Begränsningarfrån motorernas energikällor gör att framtida förhållanden är viktigaatt estimera. För plug-in hybridfordon, PIHEV, är det viktigt att använda denvvijämförelsevis billiga elektriska energin innan fordonet har nått fram till slutdestinationen.Batteriets nuvarande energimängd mäts i dess State of Charge, SOC.Genom att utnyttja information om hur långt det är till slutdestinationen från ettGlobal Positioning System, GPS, blandar styrsystemet den elektriska energin medbränsle från början, detta kallas för blandad körning. En strategi som inte hartillgång till hur långt fordonet ska köras kallas Charge Depleting Charge Sustaining,CDCS. Denna strategi använder först energin från batteriet, för att sedanbörja använda förbränningsmotorn när SOC:s miniminivå har nåtts. Strategin attanvända GPS informationen är jämförd med en strategi som inte har tillgång tillinformation om körcykelns längd. Blandad körning använder en variabel SOC referens,till skillnad från CDCS strategin som använder sig av en konstant referenspå SOC:s miniminivå. Den variabla SOC referensen beror på hur långt fordonethar kört av den totala körsträckan, med hjälp av denna realiseras en blandad körning.Från simuleringarna visade det sig att blandad körning gav minskad kostnadför de simulerade körcyklerna jämfört med en CDCS strategi. En modellbaseradprediktionsreglering används för att lösa energifördelningsproblemet. Styrsystemetföljer körcykler och löser energifördelningsproblemet för de olika drivkällorna undersimuleringarna. Styrsystemet hanterar även måttliga modellfel.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

Les architectures de calcul haute performance de nos jours sont des architectures hiérarchiques et hétérogènes: hiérarchiques car elles sont composées d'une hiérarchie de mémoire, une mémoire distribuée entre les noeuds et une mémoire partagée entre les coeurs d'un même noeud. Hétérogènes due à l'utilisation des processeurs spécifiques appelés Accélérateurs tel que le processeur CellBE d'IBM et les CPUs de NVIDIA. La complexité de maîtrise de ces architectures est double. D'une part, le problème de programmabilité: la programmation doit rester simple, la plus proche possible de la programmation séquentielle classique et indépendante de l'architecture cible. D'autre part, le problème d'efficacité: les performances doivent êtres proches de celles qu'obtiendrait un expert en écrivant le code à la main en utilisant des outils de bas niveau. Dans cette thèse, nous avons proposé une plateforme de développement pour répondre à ces problèmes. Pour cela, nous proposons deux outils : BSP++ est une bibliothèque générique utilisant des templates C++ et BSPGen est un framework permettant la génération automatique de code hybride à plusieurs niveaux de la hiérarchie (MPI+OpenMP ou MPI + Cell BE). Basée sur un modèle hiérarchique, la bibliothèque BSP++ prend les architectures hybrides comme cibles natives. Utilisant un ensemble réduit de primitives et de concepts intuitifs, BSP++ offre une simplicité d'utilisation et un haut niveau d' abstraction de la machine cible. Utilisant le modèle de coût de BSP++, BSPGen estime et génère le code hybride hiérarchique adéquat pour une application donnée sur une architecture cible. BSPGen génère un code hybride à partir d'une liste de fonctions séquentielles et d'une description de l'algorithme parallèle. Nos outils ont été validés sur différentes applications de différents domaines allant de la vérification et du calcul scientifique au traitement d'images en passant par la bioinformatique. En utilisant une large sélection d'architecture cible allant de simple machines à mémoire partagée au machines Petascale en passant par les architectures hétérogènes équipées d'accélérateurs de type Cell BE.

La Radio Logicielle (SDR) est une radio dont les transformations de la forme d'onde, modulation, démodulation des signaux d'un système radio sont mises en œuvre par du logiciel plutôt que par du matériel à fonctionnalité spécifique. Avec cette approche, l'adaptation du système à une autre norme de communication, ou même l'évolution vers une technologie plus récente peuvent être réalisés par mise à jour du logiciel sans remplacement du matériel qui serait long et coûteux. L'architecture de communication logicielle (Software Communication Architecture, SCA), est une architecture ouverte largement acceptée pour les projets de SDR. La spécification SCA minimise le coût de portage des applications en fournissant une couche d'abstraction qui rend transparentes les méthodes spécifiques de chaque système. Dans cette thèse, on s'intéresse au développement et à la programmation d'une plateforme SDR conforme à SCA.Les nouvelles plateformes de SDR sont en général implémentées sur des plateformes multiprocesseurs système sur puce (MPSoC) exploitant ses importantes ressources de calculs avec une bonne efficacité énergique. Les possibilités d'un rapide développement, déploiement et vérification des logiciels embarqués parallèles sur ces nouvelles plateformes MPSoC sont autant de points clés pour satisfaire les objectifs de performance tout en respectant les délais de mise à disposition sur le marché et le coût de développement.On a proposé un flot de conception pour la SDR avec l'exploration architecturale systématique et l'optimisation multi-objective utilisant le modèle de programmation hybride (distribué client/serveur + parallèle).On a étudié aussi la synthèse de topologie de réseau-sur-puce (PSTRP) qui est une partie du flot de conception. Le problème de la synthèse de la topologie du réseau-sur-puce peut se modéliser sous forme de programme linéaire en nombres entiers. Les résultats montrent que les contraintes d'implémentation, comme la hiérarchie du réseau sur puce, doivent être prises en compte pour obtenir un résultat à la fois mathématiquement optimisé et électroniquement réalisable.

Les systèmes de calcul actuels sont généralement des grappes de machines composés de nombreux processeurs à l'architecture fortement hiérarchique. Leur exploitation constitue le défi majeur des implémentations de modèles de programmation tels MPI ou OpenMP. Une pratique courante consiste à mélanger ces deux modèles pour bénéficier des avantages de chacun. Cependant ces modèles n'ont pas été pensés pour fonctionner conjointement ce qui pose des problèmes de performances. Les travaux de cette thèse visent à assister le développeur dans la programmation d'application de type hybride. Il s'appuient sur une analyse de la hiérarchie architecturale du système de calcul pour dimensionner les ressources d'exécution (processus et threads). Plutôt qu'une approche hybride classique, créant un processus MPI multithreadé par noeud, nous évaluons de façon automatique des solutions alternatives, avec plusieurs processus multithreadés par noeud, mieux adaptées aux machines de calcul modernes
Modern computing servers usually consist in clusters of computers with several multi-core CPUs featuring a highly hierarchical hardware design. The major challenge of the programming models implementations is to efficiently take benefit from these servers. Combining two type of models, like MPI and OpenMP, is a current trend to reach this point. However these programming models haven't been designed to work together and that leads to performance issues. In this thesis, we propose to assist the programmer who develop hybrid applications. We lean on an analysis of the computing system architecture in order to set the number of processes and threads. Rather than a classical hybrid approach, that is to say creating one multithreaded MPI process per node, we automatically evaluate alternative solutions, with several multithreaded processes per node, better fitted to modern computing systems

Les problèmes d’optimisation combinatoire sont devenus la cible de nombreuses recherches scientifiques pour leur importance dans la résolution de problèmes académiques et de problèmes réels rencontrés dans le domaine de l’ingénierie et dans l’industrie. La résolution de ces problèmes par des méthodes exactes ne peut être envisagée à cause des délais de traitement souvent exorbitants que nécessiteraient ces méthodes pour atteindre la (les) solution(s) optimale(s). Dans cette thèse, nous nous sommes intéressés au contexte algorithmique de résolution des problèmes combinatoires, et au contexte de modélisation de ces problèmes. Au niveau algorithmique, nous avons appréhendé les méthodes hybrides qui excellent par leur capacité à faire coopérer les méthodes exactes et les méthodes approchées afin de produire rapidement des solutions. Au niveau modélisation, nous avons travaillé sur la spécification et la résolution exacte des problématiques complexes de fouille des ensembles de motifs en étudiant tout particulièrement le passage à l’échelle sur des bases de données de grande taille. D'une part, nous avons proposé une première parallélisation de l'algorithme DGVNS, appelée CPDGVNS, qui explore en parallèle les différents clusters fournis par la décomposition arborescente en partageant la meilleure solution trouvée sur un modèle maître-travailleur. Deux autres stratégies, appelées RADGVNS et RSDGVNS, ont été proposées qui améliorent la fréquence d'échange des solutions intermédiaires entre les différents processus. Les expérimentations effectuées sur des problèmes combinatoires difficiles montrent l'adéquation et l'efficacité de nos méthodes parallèles. D'autre part, nous avons proposé une approche hybride combinant à la fois les techniques de programmation linéaire en nombres entiers (PLNE) et la fouille de motifs. Notre approche est complète et tire profit du cadre général de la PLNE (en procurant un haut niveau de flexibilité et d’expressivité) et des heuristiques spécialisées pour l’exploration et l’extraction de données (pour améliorer les temps de calcul). Outre le cadre général de l’extraction des ensembles de motifs, nous avons étudié plus particulièrement deux problèmes : le clustering conceptuel et le problème de tuilage (tiling). Les expérimentations menées ont montré l’apport de notre proposition par rapport aux approches à base de contraintes et aux heuristiques spécialisées
Combinatorial optimization problems have become the target of many scientific researches for their importance in solving academic problems and real problems encountered in the field of engineering and industry. Solving these problems by exact methods is often intractable because of the exorbitant time processing that these methods would require to reach the optimal solution(s). In this thesis, we were interested in the algorithmic context of solving combinatorial problems, and the modeling context of these problems. At the algorithmic level, we have explored the hybrid methods which excel in their ability to cooperate exact methods and approximate methods in order to produce rapidly solutions of best quality. At the modeling level, we worked on the specification and the exact resolution of complex problems in pattern set mining, in particular, by studying scaling issues in large databases. On the one hand, we proposed a first parallelization of the DGVNS algorithm, called CPDGVNS, which explores in parallel the different clusters of the tree decomposition by sharing the best overall solution on a master-worker model. Two other strategies, called RADGVNS and RSDGVNS, have been proposed which improve the frequency of exchanging intermediate solutions between the different processes. Experiments carried out on difficult combinatorial problems show the effectiveness of our parallel methods. On the other hand, we proposed a hybrid approach combining techniques of both Integer Linear Programming (ILP) and pattern mining. Our approach is comprehensive and takes advantage of the general ILP framework (by providing a high level of flexibility and expressiveness) and specialized heuristics for data mining (to improve computing time). In addition to the general framework for the pattern set mining, two problems were studied: conceptual clustering and the tiling problem. The experiments carried out showed the contribution of our proposition in relation to constraint-based approaches and specialized heuristics

This study presents a hybrid concurrent programming model combining the previously developed Fork-Join model (FJM) and Actor model (AM). With the advent of multi-core computers, there is a renewed interest in programming models that reduce the burden of reasoning about and writing efficient concurrent programs. The proposed hybrid model shows how the divide-and-conquer approach of the FJM and the no-shared mutable state and event-driven philosophy of the AM can be combined to solve certain classes of problems more efficiently and productively than either of the aforementioned models individually. The hybrid model adds actor creation and coordination to into the FJM, while also enabling parallelization within actors. This study uses the Habanero-Java and Scala programming languages as the base for the FJM and AM respectively, and provides an implementation of the hybrid model as an extension of the Scala language called Habanero-Scala. The hybrid model adds to the foundations of parallel programs, and to the tools available for the programmer to aid in productivity and performance while developing parallel software.

博士
國立交通大學
運輸與物流管理學系
104
The article addresses driver scheduling and rostering problems for mass rapid transit (MRT) systems. The driver scheduling problem is to find a minimum cost of duties to cover all tasks for each timetable, and the driver rostering problem is to generate a roster to ensure each duty in a rostering horizon can be assigned to a driver. These problems are very important for MRT systems because of the results will affect labor costs and operational safety such as driver fatigue. In real-world operations, the driver scheduling and rostering problems have to satisfy complex hard and soft rules. Such rules are complicated and difficult to follow through optimization methods alone. In this article, we propose a hybrid approach with constraint programming (CP) and mathematical programming (MP) models to solve these problems. The approach of the driver scheduling problem involves a CP model for duty generation, a set covering problem (SCP) model for duty optimization, and alternative ways to identify the final solution in different situation. For driver rostering problem, a two-phase heuristic approach is proposed to generate an acyclic roster. The first phase uses a goal programming (GP) model to solve the off-day scheduling. And the second phase applies a CP model to assign duties to drivers. We applied our models to solve a case problem for the KRTC. Case application results using real-work data showed that our scheduling approach is capable of reducing the number of duties from 29 to 27 for the weekday timetable, and duties for each timetable can be more centralized in 8 hour shifts and less overtime shifts. According to the duties, we can save two drivers and our rostering approach can generate more equitable rosters such as the number of weekend-off, very early shifts etc. Given the efficient and flexible models, the manager can test different situations quickly to find more suitable duties and a roster.

Tese de doutoramento em Sistemas de Energia Sustentável, apresentada ao Departamento de Engenharia Mecânica da Faculdade de Ciências e Tecnologia da Universidade de Coimbra
As relações entre o consumo de energia, o crescimento econômico e os impactos ambientais são determinantes para o processo de formulação de políticas. A Análise Input-Output (AIO) tem sido usada para estudar inter/intra-relações entre os diferentes setores económicos, bem como estendida para a análise dos sistemas energético e ambiental. Modelos de Programação Linear Multi-objetivo (PLMO) utilizando a estrutura Input-Output (IO) também vêm sendo desenvolvidos para estudar os compromissos (trade-offs) entre os sistemas económico, energético e ambiental. Os modelos IO-PLMO são capazes de captar a complexidade e natureza conflituosa dos problemas do mundo real, permitindo a obtenção de um informação relevante que não seria possível conseguir com uma aplicação separada de ambas as metodologias. Esta combinação de modelos de PLMO e AIO desempenha um papel complementar na compreensão das interações entre os sistemas económicos e energéticos, e os correspondentes impactos ambientais, oferecendo um quadro consistente para avaliar os efeitos de políticas distintas sobre estes sistemas. Um modelo de PLMO baseado em uma estrutura IO híbrida com unidades monetárias e físicas é apresentado nesta tese. Este modelo tem como objetivo avaliar os trade-offs entre objectivos económicos, energéticos, ambientais e sociais no sistema económico brasileiro. Primeiramente, as tabelas IO para o Brasil são reorganizadas para incluir o Balanço Energético Nacional, criando uma estrutura IO híbrida com unidades físicas e monetárias. Este quadro é estendido para avaliar diferentes Gases de Efeito Estufa (GEE), que são então agregados em um único indicador (CO2eq), e o nível de emprego. Duas versões do modelo de PLMO são definidas: uma versão com valores determinísticos e outra em que a programação intervalar é usada para tratar a incerteza em alguns coeficientes do modelo. A versão determinística do modelo tem 443 variáveis (incluindo a produção dos setores e produtos energéticos e várias variáveis econômicas) e um conjunto de 490 restrições (definidoras e limitadoras). As funções objetivo consideradas são a maximização dos níveis do PIB e do emprego, bem como a minimização do consumo de energia e emissões de GEE. Os métodos interativos STEM e o TRIMAP são aplicados para tratar a versão determinística do modelo, permitindo a exploração de soluções de compromisso de acordo com as informações de preferência emitidas pelo decisor. O STEM permitiu um processo de busca de solução interativa através da redução da região admissível baseada na especificação de quantidades de relação para as função objetivo com valores já satisfatórios, fornecendo informações sobre os trade-offs entre os objetivos conflitantes em diferentes regiões da região admissível. O método interativo TRIMAP ofereceu uma pesquisa flexível de soluções através de um ambiente gráfico amigável baseada na visualização das regiões de indiferença associadas a soluções vértices eficientes no diagrama paramétrico, permitindo uma exploração progressiva e seletiva de soluções de compromisso. Ambos os métodos forneceram informações de apoio à decisão relevante para um decisor hipotético, ajudando-o na compreensão dos trade-offs em jogo e na identificação de soluções de compromisso para os modelos de PLMO. A versão intervalar do modelo IO-PLMO híbrido, que inclui 518 restrições e 473 variáveis, é analisada com uma abordagem interativa que envolve a formulação de modelos determinísticos substitutos para o modelo de PLMO intervalar (baseado na minimização do pior desvio possível das funções objetivo intervalar em relação as suas soluções ideais intervalares correspondentes) e de uma fase interativa em que a sinergia entre o algoritmo (prestando informações ao decisor) e o decisor (processando as informações e fornecendo orientações para o processo de cálculo) facilitando um processo de pesquisa com base na proximidade dos valores da solução intervalar em relação à solução intervalar ideal. Perspectivas otimistas e pessimistas foram consideradas a fim de procurar soluções com diferentes alternativas de decisão. Finalmente, os impactos de diferentes processos de cultivo de cana de açúcar e de produção de bioetanol de primeira (1G) e segunda geração (2G) sobre o sistema económico brasileiro e a oferta doméstica de bioetanol em cenários prospetivos foram analisados com a versão determinística do modelo de PLMO. Coeficientes técnicos para diferentes configurações de usinas de bioetanol de produção combinada de 1G + 2G e sistemas de cultivo da cana foram estimados e introduzidos na matriz de coeficientes técnicos. As funções objetivo foram a maximização do PIB e do emprego, e a minimização do consumo de energia e das emissões totais de GEE. A maximização da produção total de bioetanol no país em cada cenário também foi considerado. Soluções não-dominadas foram calculadas através da minimização da distância de Tchebycheff para a solução ideal em cada cenário. A extensão da análise envolvendo todo o sistema económico veio complementar o desenho e a análise baseada em processos potenciais de produção de bioetanol, contribuindo para identificar efeitos indiretos que podem contrabalançar os benefícios. Esta tese fornece modelos, metodologias e conhecimento baseado na avaliação das soluções obtidas com diferentes processos de cálculo, que é essencial para o desenvolvimento de abordagens integradas para a análise prospetiva dos trade-offs económico-energético-ambiental em um país e em um setor específico.
The study and assessment of the relationships between energy consumption, economic growth and environmental impacts is determinant for the policy making process. Input-Output Analysis (IOA) has been used to study inter/intra-relationships among different sectors in the economic system and extended to account for energy and environmental impacts. Multi-objective Linear Programming (MOLP) models using the Input-Output (IO) framework has also been developed to study economic-energy-environment trade-offs. The IO-MOLP models are able to capture the complexity and conflicting nature of real world problems allowing obtaining insightful information that would not be possible to achieve with a separated application of both methodologies. This combination of multi-objective models with IOA plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies. A MOLP model based on a hybrid IO framework with monetary and physical units is presented in this thesis. This model aims at assessing the trade-offs between economic, energy, environmental and social objectives in the Brazilian economic system. Firstly, the IO tables for Brazil are reorganized to include the National Energy Balance, creating a hybrid IO framework. This framework is extended to assess different Greenhouse Gas (GHG) emissions, which are then aggregated into a single indicator (CO2eq), and the employment level. Two versions of the MOLP model are defined: a version with deterministic values and another one in which interval programming techniques are used for tackling the uncertainty in some coefficients of the model. The deterministic version of the model has 443 variables (including the total output of sectors and energy commodities and several economic variables) and a set of 490 (defining and bound) constraints. The objective functions considered are the maximization of GDP and employment levels, and the minimization of energy consumption and GHG emissions. The STEM and TRIMAP interactive methods are applied to the deterministic version of the model, allowing the exploration of compromise solutions according to the preference information issued by the decision maker (DM). STEM allows an interactive solution search process through the reduction of the feasible region based on the specification of relaxation quantities for the already satisfactory objective functions, thus providing information about the trade-offs that are at stake between the competing objectives in different regions of the search space. The TRIMAP interactive method offers a flexible search for solutions in a user-friendly graphical environment based on the display of indifference regions associated with vertex efficient solutions on the parametric diagram, allowing a progressive and selective exploration of compromise solutions. Both methods have provided relevant decision support information to a hypothetical DM assisting him/her in understanding the trade-offs at stake and identifying compromise solutions to the MOLP models. The interval version of the hybrid IO-MOLP model, which includes 518 constraints and 473 variables, is analyzed with an interactive approach involving the formulation of surrogate deterministic models for the interval MOLP model (based on the minimization of the worst possible deviation of the interval objective functions to their corresponding interval ideal solutions) and an interactive phase in which a synergy between the algorithm (providing information to the DM) and the DM (processing the information and providing guidelines for the computation process). This approach has allowed a reference point searching process based on the closeness of the values of the interval solution in relation to the ideal interval solution. Optimistic and pessimistic perspectives have been considered in order to search for solutions using different decision alternatives. Finally, impacts of different sugarcane cultivation and first-generation (1G) and second-generation (2G) bioethanol production processes on the Brazilian economic system and domestic bioethanol supply in prospective scenarios are analyzed with the deterministic version of the MOLP model. Technical coefficients for different configurations of combined 1G+2G bioethanol plants and sugarcane cultivation are estimated and introduced into the Brazilian technical coefficient matrix. The objective functions are the maximization of GDP and employment level, and the minimization of total energy consumption and GHG emissions. The maximization of the total bioethanol production in the country in each scenario is also considered. Non-dominated solutions are computed by minimizing a Tchebycheff distance to the ideal solution in each scenario. Extending the analysis to the whole economic system has complemented the process design and process-based analysis of prospective bioethanol production, contributing to identify indirect effects that can counterbalance the benefits. This thesis provides models, methodologies and knowledge based on the assessment of the solutions obtained in the different computation processes that is essential for the development of integrated approaches for prospective analysis of economic-energy-environmental trade-offs in a country and a specific sector.
FCT - SFRH/BD/42960/2008

碩士
靜宜大學
資訊工程學系碩士班
98
Current parallel hardware architectures commonly used to execute parallel code can be broadly categories into shared-memory (SM), distributed-memory (DM), and distributed shared-memory (DSM) systems. DM systems and clusters have been popular because of their price/performance ratio and scalability. Even the high performance computing machine is a multi-core node interconnected by a high speed network. To write a parallel FFT code on these machines, in this thesis, we implemented a hybrid MPI and OpenMP parallel programming for FFT. The parallel MPI FFT code uses mainly SPMD and master-worker programming styles at the first level of parallelism. OpenMP is used to exploit second level of parallelism within a node, most other implementation, at this level of parallelism, a naïve loop level parallelization by adding omp for directive of OpenMP has been used. We implemented this level of parallelism within a node using an OpenMP SPMD style instead. Hence, our implementation style is SPMD within SPMD. We present our implementation and discuss the experimental results. We compare the results with MPI version of FFTW. Our hybrid model show promising and it will have better performance when our pure MPI FFT code is further improved.

The mobility allowance shuttle transit (MAST) system is a hybrid transit system in which vehicles are allowed to deviate from a fixed route to serve flexible demand. A mixed integer programming (MIP) formulation for the static scheduling problem of a multi-vehicle Mobility Allowance Shuttle Transit (MAST) system is proposed in this thesis. Based on the MIP formulation, we analyze the impacts of time headways between consecutive transit vehicles on the performance of a two-vehicle MAST system. An analytical framework is then developed to model the performance of both one-vehicle and two-vehicle MAST systems, which is used to identify the critical demand level at which an increase of the fleet size from one to two vehicles would be appropriate. Finally, a sensitivity analysis is conducted to find out the impact of a key modeling parameter, w1, the weight of operations cost on the critical demand. In this paper, we develop an insertion heuristic for a multi-vehicle MAST system, which has never been addressed in the literature. The proposed heuristic is validated and evaluated by a set of simulations performed at different demand levels and with different control parameters. By comparing its performance versus the optimal solutions, the effectiveness of the heuristic is confirmed. Compared to its single-vehicle counterpart, the multiple-vehicle MAST prevails in terms of rejection rate, passenger waiting time and overall objective function, among other performance indices.

yes
This Workshop on Membrane Computing, at the Conference of Unconventional Computation and Natural Computation (UCNC), 12th July 2016, Manchester, UK, is the second event of this type after the Workshop at UCNC 2015 in Auckland, New Zealand*. Following the tradition of the 2015 Workshop the Proceedings are published as technical report. The Workshop consisted of one invited talk and six contributed presentations (three full papers and three extended abstracts) covering a broad spectrum of topics in Membrane Computing, from computational and complexity theory to formal verification, simulation and applications in robotics. All these papers – see below, but the last extended abstract, are included in this volume. The invited talk given by Rudolf Freund, “P SystemsWorking in Set Modes”, presented a general overview on basic topics in the theory of Membrane Computing as well as new developments and future research directions in this area. Radu Nicolescu in “Distributed and Parallel Dynamic Programming Algorithms Modelled on cP Systems” presented an interesting dynamic programming algorithm in a distributed and parallel setting based on P systems enriched with adequate data structure and programming concepts representation. Omar Belingheri, Antonio E. Porreca and Claudio Zandron showed in “P Systems with Hybrid Sets” that P systems with negative multiplicities of objects are less powerful than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov presented in “Extended Spiking Neural P Systems with States” new results regading the newly introduced topic of spiking neural P systems where states are considered. “Selection Criteria for Statistical Model Checker”, by Mehmet E. Bakir and Mike Stannett, presented some early experiments in selecting adequate statistical model checkers for biological systems modelled with P systems. In “Towards Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPU”, Raluca Lefticaru, Luis F. Macías-Ramos, Ionuţ M. Niculescu, Laurenţiu Mierlă presented some of the advatages of implementing kernel P systems simulations in FLAME. Andrei G. Florea and Cătălin Buiu, in “An Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots. *http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc- at-the-conference-on-unconventional-computation-natural-computation/