The research objective of this dissertation is to create and demonstrate methods for parameterizing the Pareto frontiers of continuous multi-attribute design problems using barycentric coordinates, and in doing so, to enable intuitive exploration of optimal trade spaces. This work is enabled by two observations about Pareto frontiers that have not been previously addressed in the engineering design literature. First, the observation that the mapping between non-dominated designs and Pareto efficient response vectors is a bijection almost everywhere suggests that points on the Pareto frontier can be inverted to find their corresponding design variable vectors. Second, the observation that certain common classes of Pareto frontiers are topologically equivalent to simplices suggests that a barycentric coordinate system will be more useful for parameterizing the frontier than the Cartesian coordinate systems typically used to parameterize the design and objective spaces. By defining such a coordinate system, the design problem may be reformulated from y = f(x) to (y,x) = g(p) where x is a vector of design variables, y is a vector of attributes and p is a vector of barycentric coordinates. Exploration of the design problem using p as the independent variables has the following desirable properties: 1) Every vector p corresponds to a particular Pareto efficient design, and every Pareto efficient design corresponds to a particular vector p. 2) The number of p-coordinates is equal to the number of attributes regardless of the number of design variables. 3) Each attribute y_i has a corresponding coordinate p_i such that increasing the value of p_i corresponds to a motion along the Pareto frontier that improves y_i monotonically. The primary contribution of this work is the development of three methods for forming a barycentric coordinate system on the Pareto frontier, two of which are entirely original. The first method, named "non-domination level coordinates," constructs a coordinate system based on the (k-1)-attribute non-domination levels of a discretely sampled Pareto frontier. The second method is based on a modification to an existing "normal boundary intersection" multi-objective optimizer that adaptively redistributes its search basepoints in order to sample from the entire frontier uniformly. The weights associated with each basepoint can then serve as a coordinate system on the frontier. The third method, named "Pareto simplex self-organizing maps" uses a modified a self-organizing map training algorithm with a barycentric-grid node topology to iteratively conform a coordinate grid to the sampled Pareto frontier.

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.

Le Problème de Tournées de Véhicules (PTV) est une clé importante pour gérér efficacement des systèmes logistiques, ce qui peut entraîner une amélioration du niveau de satisfaction de la clientèle. Ceci est fait en servant plus de clients dans un temps plus court. En terme général, il implique la planification des tournées d'une flotte de véhicules de capacité donnée basée à un ou plusieurs dépôts. Le but est de livrer ou collecter une certain quantité de marchandises à un ensemble des clients géographiquement dispersés, tout en respectant les contraintes de capacité des véhicules. Le PTV, comme classe de problèmes d'optimisation discrète et de grande complexité, a été étudié par de nombreux au cours des dernières décennies. Étant donné son importance pratique, des chercheurs dans les domaines de l'informatique, de la recherche opérationnelle et du génie industrielle ont mis au point des algorithmes très efficaces, de nature exacte ou heuristique, pour faire face aux différents types du PTV. Toutefois, les approches proposées pour le PTV ont souvent été accusées d'être trop concentrées sur des versions simplistes des problèmes de tournées de véhicules rencontrés dans des applications réelles. Par conséquent, les chercheurs sont récemment tournés vers des variantes du PTV qui auparavant étaient considérées trop difficiles à résoudre. Ces variantes incluent les attributs et les contraintes complexes observés dans les cas réels et fournissent des solutions qui sont exécutables dans la pratique. Ces extensions du PTV s'appellent Problème de Tournées de Véhicules Multi-Attributs (PTVMA). Le but principal de cette thèse est d'étudier les différents aspects pratiques de trois types de problèmes de tournées de véhicules multi-attributs qui seront modélisés dans celle-ci. En plus, puisque pour le PTV, comme pour la plupart des problèmes NP-complets, il est difficile de résoudre des instances de grande taille de façon optimale et dans un temps d'exécution raisonnable, nous nous tournons vers des méthodes approcheés à base d’heuristiques. / The Vehicle Routing Problem (VRP) is an important key to efficient logistics system management, which can result in higher level of customer satisfaction because more customers can be served in a shorter time. In broad terms, it deals with designing optimal delivery or collection routes from one or several depot(s) to a number of geographically scattered customers subject to side constraints. The VRP is a discrete optimization and computationally hard problem and has been extensively studied by researchers and practitioners during the past decades. Being complex problems with numerous and relevant potential applications, researchers from the fields of computer science, operations research and industrial engineering have developed very efficient algorithms, both of exact and heuristic nature, to deal with different types of VRPs. However, VRP research has often been criticized for being too focused on oversimplified versions of the routing problems encountered in real-life applications. Consequently, researchers have recently turned to variants of the VRP which before were considered too difficult to solve. These variants include those attributes and constraints observed in real-life planning and lead to solutions that are executable in practice. These extended problems are called Multi-Attribute Vehicle Routing Problems (MAVRPs). The main purpose of this thesis is to study different practical aspects of three multi-attribute vehicle routing problems which will be modeled in it. Besides that, since the VRP has been proved to be NP-hard in the strong sense such that it is impossible to optimally solve the large-sized problems in a reasonable computational time by means of traditional optimization approaches, novel heuristics will be designed to efficiently tackle the created models.

Dans cette thèse, nous proposons plusieurs nouvelles techniques pour la classification d'images hyperspectrales basées sur l'apprentissage d'ensemble. Le cadre proposé introduit des innovations importantes par rapport aux approches précédentes dans le même domaine, dont beaucoup sont basées principalement sur un algorithme individuel. Tout d'abord, nous proposons d'utiliser la Forêt de Rotation (Rotation Forest) avec différentes techiniques d'extraction de caractéristiques linéaire et nous comparons nos méthodes avec les approches d'ensemble traditionnelles, tels que Bagging, Boosting, Sous-espace Aléatoire et Forêts Aléatoires. Ensuite, l'intégration des machines à vecteurs de support (SVM) avec le cadre de sous-espace de rotation pour la classification de contexte est étudiée. SVM et sous-espace de rotation sont deux outils puissants pour la classification des données de grande dimension. C'est pourquoi, la combinaison de ces deux méthodes peut améliorer les performances de classification. Puis, nous étendons le travail de la Forêt de Rotation en intégrant la technique d'extraction de caractéristiques locales et l'information contextuelle spatiale avec un champ de Markov aléatoire (MRF) pour concevoir des méthodes spatio-spectrale robustes. Enfin, nous présentons un nouveau cadre général, ensemble de sous-espace aléatoire, pour former une série de classifieurs efficaces, y compris les arbres de décision et la machine d'apprentissage extrême (ELM), avec des profils multi-attributs étendus (EMaPS) pour la classification des données hyperspectrales. Six méthodes d'ensemble de sous-espace aléatoire, y compris les sous-espaces aléatoires avec les arbres de décision, Forêts Aléatoires (RF), la Forêt de Rotation (RoF), la Forêt de Rotation Aléatoires (Rorf), RS avec ELM (RSELM) et sous-espace de rotation avec ELM (RoELM), sont construits par multiples apprenants de base. L'efficacité des techniques proposées est illustrée par la comparaison avec des méthodes de l'état de l'art en utilisant des données hyperspectrales réelles dans de contextes différents. / In this thesis, we propose several new techniques for the classification of hyperspectral remote sensing images based on multiple classifier system (MCS). Our proposed framework introduces significant innovations with regards to previous approaches in the same field, many of which are mainly based on an individual algorithm. First, we propose to use Rotation Forests with several linear feature extraction and compared them with the traditional ensemble approaches, such as Bagging, Boosting, Random subspace and Random Forest. Second, the integration of the support vector machines (SVM) with Rotation subspace framework for context classification is investigated. SVM and Rotation subspace are two powerful tools for high-dimensional data classification. Therefore, combining them can further improve the classification performance. Third, we extend the work of Rotation Forests by incorporating local feature extraction technique and spatial contextual information with Markov random Field (MRF) to design robust spatial-spectral methods. Finally, we presented a new general framework, Random subspace ensemble, to train series of effective classifiers, including decision trees and extreme learning machine (ELM), with extended multi-attribute profiles (EMAPs) for classifying hyperspectral data. Six RS ensemble methods, including Random subspace with DT (RSDT), Random Forest (RF), Rotation Forest (RoF), Rotation Random Forest (RoRF), RS with ELM (RSELM) and Rotation subspace with ELM (RoELM), are constructed by the multiple base learners. The effectiveness of the proposed techniques is illustrated by comparing with state-of-the-art methods by using real hyperspectral data sets with different contexts.

Le travail de recherche présenté dans cette thèse s’inscrit dans la continuité des travaux de l’aide à la décision multi-critère de groupe (décideurs), particulièrement dans le champ de sélection de la localisation des centres de distribution. Dans un environnement certain, si la décision de sélection de la localisation des centres de distribution a donné lieu à plusieurs travaux de recherche, elle n’a jamais été l’objet, à notre connaissance, d’une décision prise par plusieurs décideurs. À cet égard, le premier objectif de cette thèse est de proposer une méthode d’aide à la décision multi-attribut et multi-acteur (MAADM) pour résoudre le problème posé. Pour se faire, nous avons adapté et étendu la méthode ELECTRE I. Dans un environnement incertain, au vu de l’incertitude inhérente et l’imprécision du processus décisionnel humain ainsi que les comportements futurs du marché et des entreprises, le deuxième objectif de cette thèse est de développer une méthode floue d’aide à la décision multi-attribut et multi-acteur (FMAADM) pour traiter le problème en question. Pour cela, nous avons couplé la méthode MAADM avec la théorie des ensembles flous. Pour la validation des deux contributions, nous avons conçu un système d’aide à la décision (S-DSS) pour implémenter les algorithmes de la méthode MAADM et la méthode FMAADM. Sur la base du S-DSS, deux études expérimentales ont été menées. Nous avons, aussi, appliqué une analyse de sensibilité pour vérifier la sensibilité de la solution retenue vis-à-vis aux variations de poids des critères d’évaluation. Les résultats obtenus prouvent que les deux méthodes proposées répondent à l’objectif recherché et ainsi retenues pour la sélection de la meilleure localisation dans un contexte certain/incertain de multi-attribut et multi-acteur. / The research work presented in this thesis is part of the works’ continuity on multi-criteria group (decision-makers) decision-making, particularly in the field of the distribution centers’ location selection. Under certain environment, although the decision to select the location of the distribution centers has given rise in several research works, it has never been the object, to our knowledge, of a decision taken by several decision makers. In this regard, the first objective of this thesis is to develop a multi-attribute and multi-actor decision-making method (MAADM) to resolve the posed problem. For this purpose, we have adapted and extended the ELECTRE I method. Under uncertain environment, In view of the inherent uncertainty and inaccuracy of human decision-making, the future behavior of the market and companies, the second objective of this thesis is to propose a fuzzy multi-attribute and multi-actor decision-making method (FMAADM) to treat the problem in question. To this end, we have coupled the MAADM method with the fuzzy set theory. To validate the two contributions, we designed a decision support system (S-DSS) to implement the MAADM method and the FMAADM method. Based on the S-DSS, two experimental studies were conducted. We also applied a sensitivity analysis to verify the sensitivity of the solution retained vis-a-vis to weights’ variations of evaluation criteria. The obtained results prove that the MAADM method and the FMAADM method meet the desired objective and thus retained for the selection of the best location under certain/uncertain context of multi-attribute and multi-actor.

Problems in the public policy decision-making environments are typically complex and continuously evolve. In a resource-constrained environment, several alternatives, criteria, and conflicting objectives must be considered. As a result, solutions to these types of problems cannot be modelled solely using single-criteria techniques. It has been observed that most techniques used to shape energy policy and planning either produce sub-optimal solutions or use strong assumptions about the preferences of decision-maker(s). This difficulty creates a compelling need to develop novel techniques that can handle several alternatives, multiple criteria and conflicting objectives to support public sector decision-making processes. First, the study presents a novel scenario-based multi-objective optimisation framework based on the augmented Chebychev goal programming (GP) technique linked to a value function for analysing a decision environment underlying energy choice among low-income households in isolated rural areas and informal urban settlements in South Africa. The framework developed includes a multi-objective optimisation technique that produced an approximation of a Pareto front linked to an a priori aggregation function and a value function to select the best alternatives. Second, the study used this model to demonstrate the benefits of applying the framework to a previously unknown subject in public policy: a dynamic multi-technology decision problem under uncertainty involving multiple stakeholders and conflicting objectives. The results obtained suggest that while it is cost-optimal to pursue electrification in conjunction with other short-term augmentation solutions to meet South Africa's universal electrification target, sustainable energy access rates among low-income households can be achieved by increasing the share of clean energy generation technologies in the energy mix. This study, therefore, challenges the South African government's position on pro-poor energy policies and an emphasis on grid-based electrification to increase energy access. Instead, the study calls for a portfolio-based intervention. The study advances interventions based on micro-grid electrification made up of solar photovoltaics (PV), solar with storage, combined cycle gas turbine (CCGT) and wind technologies combined with either bioethanol fuel or liquid petroleum gas (LPG). The study has demonstrated that the framework developed can benefit public sector decision-makers in providing a balanced regime of technical, financial, social, environmental, public health, political and economic aspects in the decision-making process for planning energy supply interventions for low-income households. The framework can be adapted to a wide range of energy access combinatorial problems and in countries grappling with similar energy access challenges.

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關於供應商評選模式的探討與研究，是現今於供應鏈管理中非常重要的議題，而對於國內外供應商評選的相關性研究中，往往只考量供應商本身績效，而缺乏整體性的考量，有鑑於此，當企業在評選供應商時，不僅是針對單一供應商績效的考量，而對於供應商本身上下游廠商的整體績效的考量，也是相當重要的評選因素。本研究運用資料包絡分析法(Data Envelopment Analysis, DEA)與簡易多屬性評等技術(Simple Multi-Attribute Rating, SMART)，以建構較完整的供應商評選模式，協助企業的決策者選擇最適的供應商。 在進行供應商與供應商所屬的供應鏈之績效評估時，利用資料包絡分析法中的交叉模式計算供應商與供應商所屬的供應鏈之平均效率，再藉由平均效率值以排序供應商與供應商所屬的供應鏈之優先順位，但因兩者之順序會產生不一致的情形，利用SMART法中的排序加總法計算兩者之權重值，最後以加總法計算綜合效率值，依據綜合效率值加以排序以評選最適的供應商，而此兩種方法的結合， 則需透過驗證的方式以確定運用之正確性，利用系統動力學(System Dynamics, SD)以建構供應鏈之整體廠商的經營環境，模擬供應鏈整體廠商之績效評估模式，以產生模擬驗證排序，最後再將綜合排序與驗證排序，利用Spearman等級相關係數驗證其相關性，而最終證明兩排序具有高度相關性，也證實本研究所提出的供應商評選模式富有參考價值。 / The research of supplier selection model is a very important issue in supply chain management (SCM). Even though the research on supplier selection is abundant, the most works usually only consider the performance of the supplier. This situation can result in a lack of integral deliberation. Therefore, the decision-maker wants to select his vendor in the company, it is important that the decision-maker not only focus on the business unit but also add to the overall organization of the supply chain. In this paper the Data Envelopment Analysis (DEA) and the Simple Multi-Attribute Rating (SMART) are developed to construct the effective supplier evaluation model depends upon the constituent parts of the supply chain, which may be helpful for selecting the appropriate vendor. When evaluating the performance of the supplier and the supplier’s supply chain, the cross-efficiency model of DEA is applied to compute average efficiency both the supplier and the supply chain. According to average efficiency, we can arrange the priority order that may be have the inconsistent order for the suppliers and their supply chains. The rank sum weighting of SMART is employed to determine the weights of suppliers and their chains and then the weighted method is used to calculate overall efficiency that ranking of the suppliers is obtained. Above of two methods, we must be verify the model. The System Dynamics (SD) is designed to implement the whole components of the supply chain for business environment and simulate the performance model. Based on this performance model, we can acquire a confirmation list. In the end, the Spearman’s rank correlation coefficient is provided to testify the correlation between the overall order and the confirmation list. The result of the statistic analysis is illustrated the strength of the association. The model can be tailored and applied by firms that are making decisions on supplier selection.