Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

In this dissertation, we focus on the problem of self-management in distributed systems. In this context, we propose a new methodology for reactive self-management based on multiple criteria decision analysis (MCDA). The general structure of the proposed methodology is extracted from the commonalities of the former well-established approaches that are applied in other problem domains. The main novelty of this work, however, lies in the usage of MCDA during the reaction processes in the context of the two problems that the proposed methodology is applied to. In order to provide a detailed analysis and assessment of this new approach, we have used the proposed methodology to design distributed autonomous agents that can provide self-management in two outstanding problems. These two problems also represent the two distinct ways in which the methodology can be applied to self-management problems. These two cases are: 1) independent self management, and 2) coordinated self-management. In the simulation case study regarding independent self-management, the methodology is used to design and implement a distributed resource consolidation manager for clouds, called IMPROMPTU. In IMPROMPTU, each autonomous agent is attached to a unique physical machine in the cloud, where it manages resource consolidation independently from the rest of the autonomous agents. On the other hand, the simulation case study regarding coordinated self-management focuses on the problem of adaptive routing in mobile ad hoc networks (MANET). The resulting system carries out adaptation through autonomous agents that are attached to each MANET node in a coordinated manner. In this context, each autonomous node agent expresses its opinion in the form of a decision regarding which routing algorithm should be used given the perceived conditions. The opinions are aggregated through coordination in order to produce a final decision that is to be shared by every node in the MANET. Although MCDA has been previously considered within the context of artificial intelligence---particularly with respect to algorithms and frameworks that represent different requirements for MCDA problems, to the best of our knowledge, this dissertation outlines a work where MCDA is applied for the first time in the domain of these two problems that are represented as simulation case studies. / Graduate

In several types of embedded systems the applications are deployed both as software and as hardware components. For such systems, the partitioning decision is highly important since the implementation in software or hardware heavily influences the system properties. In the industry, it is rather common practice to take deployment decisions in an early stage of the design phase and based on a limited number of aspects. Often such decisions are taken based on hardware and software designers‟ expertise and do not account the requirements of the entire system and the project and business development constraints. This approach leads to several disadvantages such as redesign, interruption, etc. In this scenario, we see the need of approaching the partitioning process from a multiple decision perspective. As a consequence, we start by presenting an analysis of the most important and popular Multiple Criteria Decision Analysis (MCDA) methods and tools. We also identify the key requirements on the partitioning process. Subsequently, we evaluate all of the MCDA methods and tools with respect to the key partitioning requirements. By using the key partitioning requirements the methods and tools that the best suits the partitioning are selected. Finally, we propose two MCDA-based partitioning processes and validate their feasibility thorough an industrial case study.

L’aide multicritère à la décision (AMCD) vise à faciliter et améliorer la qualité du processus de prise de décision. Les méthodes d’AMCD permettent de traiter les problèmes de choix, rangement et classification. Ces méthodes impliquent généralement la construction d’un modèle. Déterminer les valeurs des paramètres de ces modèles n’est pas aisé. Les méthodes d’apprentissage indirectes permettent de simplifier cette tâche en apprenant les paramètres du modèle de décision à partir de jugements émis par un décideur tels que “l’alternative a est préférée à l’alternative b” ou “l’alternative a doit être classifiée dans la meilleure catégorie”. Les informations données par le décideur sont généralement parcimonieuses. Le modèle d’AMCD est appris au cours d’un processus interactif entre le décideur et l’analyste. L’analyste aide le décideur à formuler et revoir ses jugements si nécessaire. Le processus s’arrête une fois qu’un modèle satisfaisant les préférences du décideur a été trouvé. Le “preference learning” (PL) est un sous domaine du “machine learning” qui s’intéresse à l’apprentissage des préférences. Les algorithmes de ce domaine sont capables de traiter de grands jeux de données et sont validés au moyen de jeux de données artificiels et réels. Les jeux de données traités en PL sont généralement collectés de différentes sources et sont entachés de bruit.Contrairement à l’AMCD, il existe peu ou pas d’interaction avec l’utilisateur en PL. Le jeu de données fourni en entrée à l’algorithme est considéré comme un échantillon éventuellement bruité d’une “réalité” ou “vérité de terrain”. Les algorithmes utilisés dans ce domaine ont des propriétés statistiques fortes leur permettant de s’affranchir du bruit dans ces jeux de données. Dans cette thèse, nous développons des algorithmes d’apprentissage permettant d’apprendre lesparamètres de modèles d’AMCD. Plus précisément, nous développons une métaheuristique afin d’apprendre les paramètres d’un modèle appelé MR-Sort (“majority rule sorting”). Cette métaheuristique est testée sur des jeux de donnéesartificiels et réels utilisés dans le domaine du PL. Nous utilisons cet algorithme afin de traiter un problème concret dans le domaine médical. Ensuite nous modifions la métaheuristique afin d’apprendre les paramètres d’un modèle plus expressif appelé NCS (“non-compensatory sorting”). Finalement, nous développons un nouveau type de règle de veto pour les modèles MR-Sort et NCS qui permet de prendre les coalitions de critères en compte. La dernière partie de la thèse introduit les méthodes d’optimisation semi-définie positive (SDP) dans le contexte de l’aide multicritère à la décision. Précisément, nous utilisons l’optimisation SDP afin d’apprendre les paramètres d’un modèle de fonction de valeur additive. / Multiple-criteria decision analysis (MCDA) aims at providing support in order to make a decision. MCDA methods allow to handle choice, ranking and sorting problems. These methods usually involve the elicitation of models. Eliciting the parameters of these models is not trivial. Indirect elicitation methods simplify this task by learning the parameters of the decision model from preference statements issued by the decision maker (DM) such as “alternative a is preferred to alternative b” or “alternative a should be classified in the best category”. The information provided by the decision maker are usually parsimonious. The MCDA model is learned through an interactive process between the DM and the decision analyst. The analyst helps the DM to modify and revise his/her statements if needed. The process ends once a model satisfying the preferences of the DM is found. Preference learning (PL) is a subfield of machine learning which focuses on the elicitation of preferences. Algorithms in this subfield are able to deal with large data sets and are validated withartificial and real data sets. Data sets used in PL are usually collected from different sources and aresubject to noise. Unlike in MCDA, there is little or no interaction with the user in PL. The input data set is considered as a noisy sample of a “ground truth”. Algorithms used in this field have strong statistical properties that allow them to filter noise in the data sets.In this thesis, we develop learning algorithms to infer the parameters of MCDA models. Precisely, we develop a metaheuristic designed for learning the parameters of a MCDA sorting model called majority rule sorting (MR-Sort) model. This metaheuristic is assessed with artificial and real data sets issued from the PL field. We use the algorithm to deal with a real application in the medical domain. Then we modify the metaheuristic to learn the parameters of a more expressive model called the non-compensatory sorting (NCS) model. After that, we develop a new type of veto rule for MR-Sort and NCS models which allows to take criteria coalitions into account. The last part of the thesis introduces semidefinite programming (SDP) in the context of multiple-criteria decision analysis. We use SDP to learn the parameters of an additive value function model.

A Internet das coisas é constituída de objetos que possuem pequenos sensores e atuadores capazes de interagir com o ambiente. Tais objetos ou coisas estão interconectados entre si e com acesso à Internet por meio de redes com e sem fio. A combinação entre os dispositivos embarcados com sensores e o acesso à Internet possibilita a comunicação dos recursos do mundo físico com o espaço cibernético, desempenhando um papel fundamental na resolução de muitos desafios encontrados na sociedade atual. Porém, a maioria das aplicações existentes são dedicadas a resolver problemas específicos utilizando tais recursos apenas em redes internas, limitando a real capacidade da Internet das Coisas. Diversos trabalhos na literatura propõem a reutilização de tais recursos em forma de serviço por meio de modelos como Dados como Serviço e Sensoriamento como Serviço. Neste contexto, em que potencialmente milhares de recursos podem transferir dados semelhantes de aplicações diferentes, a utilização de técnicas que possam selecionar recursos de forma sensível a contexto torna-se imprescindível. Nesta tese são propostos um conjunto de métodos para melhorar a relação custo-benefício na seleção de recursos em ambientes IoT, auxiliando na tomada de decisão durante a seleção dos recursos que serão ofertados como serviço. Os resultados obtidos por meio de estudos de caso, permitiram a comparação da qualidade da solução e do custo computacional das técnicas aplicadas na seleção de recursos em ambientes IoT, bem como o desenvolvimento de duas novas técnicas para a seleção de recursos, denominadas Elimination Sort e Fast Elimination Sort. / The Internet of Things is composed of objects which have small sensors and actuators capable of interacting with the environment. Such objects or things are interconnected with each other and has access to the Internet through wired and wireless networks. The combination of embedded devices with sensors and access to the Internet become it possible to communicate the resources of the physical world with the cyberspace, playing a key role in solving many challenges found in todays society. However, most existing applications solves a specific problem using its resources just for own purpose, limiting the actual ability of the Internet of Things. Several works propose the reuse of such resources through service models such as Data as Service and Sensing as a Service. In this context, where thousands of resources can transfer similar data from different applications, the use of techniques that can select these features in a context-sensitive way becomes essential. In this thesis, a set of methods to improve the cost-benefit of the process of selection of resources in IoT environments is proposed to support the decision making during resource selection that will be offered as a service. The results obtained through a case study allowed the comparison of the solution quality and the computational cost of the techniques applied for resource selection in IoT environments, as well as the development of two new techniques for the selection of resources called Elimination Sort and Fast Elimination Sort.

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational framework. Such integrative research to link different knowledge domains faces several practical challenges. The complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. This thesis aims to overcome some of these challenges, and to demonstrate how new modeling approaches can provide successful integrative water resources research. It focuses on the development of new integrated modeling approaches which allow integration of not only physical processes but also socio-economic and environmental issues and uncertainties inherent in water resources systems. To achieve this goal, two new approaches are developed in this thesis. At first, a Bayesian network (BN)-based decision support tool is developed to conceptualize hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. The method demonstrates the value of combining different commonly used integrated modeling approaches. Coupled component models are applied to simulate the nonlinearity and feedbacks of strongly interacting groundwater-agricultural hydrosystems. Afterwards, a BN is used to integrate the coupled component model results with empirical knowledge and stakeholder inputs. In the second part of this thesis, a fuzzy-stochastic multiple criteria decision analysis tool is developed to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrates physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approaches are applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structures. The results show the effectiveness of the proposed methods. The first method can aid in the impact assessment of alternative management interventions on sustainability of aquifer systems while accounting for economic (agriculture) and societal interests (employment in agricultural sector) in the study area. Results from the second method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. The new approaches can be applied to address the complexities and uncertainties inherent in water resource systems to support management decisions, while serving as a platform for stakeholder participation.

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The objective of this thesis is the definition and development of a comprehensive methodology of energy planning for areas isolated from the mains, considering not only the energy context of the country and its development towards a sustainable scenario, but also studying the potential of renewable generation in the remote area under study, the ability for demand management and the socio-economic aspects involved in the final decision on what renewable energy solution would be the most appropriate in accordance with the characteristics of the location. The research work is organized into three major phases. The first one defines the algorithm of analysis of the context energy of the country and its evolution towards a future energy scenario based on renewable energies. A second phase which analyzes the best configurations of hybrid renewable systems capable of responding to energy needs in the area, sorting them based on their net present value. And a third one introducing the method of multi-criteria analysis which allows to select, from among all possible configurations identified in the previous stage, the most appropriate to the needs and characteristics of the area to study, taking into account not only economic or technical aspects, but also sociological, political, and environmental criteria. Finally, the developed methodology is applied to a case concrete as example of its potential. An isolated community in the Democratic Republic of the Congo has been selected since 90% of the population living in areas isolated from the mains, and being one of the African countries with the greatest potential for renewable energy generation. / El objetivo de esta tesis es la definición y desarrollo de una metodología integral de planificación energética para zonas aisladas de la red eléctrica que considere no solo el contexto energético del país y su desarrollo hacia un escenario sostenible, sino también el estudio del potencial de generación renovable en la zona remota a estudiar, la capacidad de gestión de la demanda y los aspectos socio-económicos que intervienen en la decisión final sobre qué solución energética renovable sería la más apropiada de acuerdo con las características de la ubicación. El trabajo de investigación se organiza en tres grandes etapas. La primera donde se define el algoritmo de análisis del contexto energético del país y su evolución hacia un escenario energético futuro basado en energías renovables. Una segunda fase donde se analizan las mejores configuraciones de sistemas renovables híbridos capaces de responder a las necesidades energéticas de la zona, clasificándolas en base a su valor neto actual. Y una tercera donde se describe el método de análisis multi-criterio que permite seleccionar, de entre todas las posibles configuraciones identificadas en la etapa anterior, la más adecuada para las necesidades y características de la zona a estudiar, teniendo en cuenta no solo aspectos económicos o técnicos, sino también criterios sociológicos, políticos y medioambientales. Finalmente, se aplica la metodología a un caso concreto en la República Democrática del Congo como ejemplo de su aplicación. Para el análisis del caso de estudio, se ha seleccionado una comunidad aislada en la República Democrática del Congo ya que el 90% de la población vive en zonas aisladas de la red eléctrica, y es uno de los países de África con mayor potencial de generación con energías renovables. / L'objectiu d'aquesta tesi és la definició i desenvolupament d'una metodologia integral de planificació energètica per a zones aïllades de la xarxa elèctrica que considere no solament el context energètic del país i el seu desenvolupament cap a un escenari sostenible, sinó també l'estudi del potencial de generació renovable en la zona remota a estudiar, la capacitat de gestió de la demanda i els aspectes soci-econòmics que intervenen en la decisió final sobre quina solució energètica renovable seria la més apropiada d'acord amb les característiques de la ubicació. El treball de recerca s'organitza en tres grans etapes. La primera on es defineix l'algorisme d'anàlisi del context energètic del país i la seua evolució cap a un escenari energètic futur basat en energies renovables. Una segona fase on s'analitzen les millors configuracions de sistemes renovables híbrids capaços de respondre a les necessitats energètiques de la zona, classificant-les sobre la base del seu valor net actual. I una tercera on es descriu el mètode d'anàlisi multi-criteri que permet seleccionar, d'entre totes les possibles configuracions identificades en l'etapa anterior, la més adequada per a les necessitats i característiques de la zona a estudiar, tenint en compte no sol aspectes econòmics o tècnics, sinó també criteris sociològics, polítics i mediambientals. Finalment, s'aplica la metodologia a un cas concret en la República Democràtica del Congo com a exemple de la seua aplicació. Per a l'anàlisi del cas d'estudi, s'ha seleccionat una comunitat aïllada en la República Democràtica del Congo ja que el 90% de la població viu en zones aïllades de la xarxa elèctrica, i és un dels països d'Àfrica amb major potencial de generació amb energies renovable. / Peñalvo López, E. (2017). Metodología de Evaluación y Optimización de Sistemas Renovables Híbridos para Electrificación de Zonas Aisladas de la Red [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/82308