Algoritmos de aprendizado semi-supervisionado ativo podem se configurar como ferramentas úteis em cenários práticos em que os dados são numerosamente obtidos, mas atribuir seus respectivos rótulos de classe se configura como uma tarefa custosa/difícil. A literatura em aprendizado ativo destaca diversos algoritmos, este trabalho partiu do tradicional Hierarchical Sampling estabelecido para operar sobre hierarquias de grupos. As características de tal algoritmo o coloca à frente de outros métodos ativos, entretanto o mesmo ainda apresenta algumas dificuldades. A fim de aprimorá-lo e contornar suas principais dificuldades, incluindo sua sensibilidade na escolha particular de uma hierarquia de grupos como entrada, este trabalho propôs estratégias que possibilitaram melhorar o algoritmo na sua forma original e diante de variantes propostas na literatura. Os experimentos em diferentes bases de dados reais mostraram que o algoritmo proposto neste trabalho é capaz de superar e competir em qualidade dentro do cenário de classificação ativa com outros algoritmos ativos da literatura. / Active semi-supervised learning can play an important role in classification scenarios in which labeled data are laborious and/or expensive to obtain, while unlabeled data are numerous and can be easily acquired. There are many active algorithms in the literature and this work focuses on an active semi-supervised algorithm that can be driven by clustering hierarchy, the well-known Hierarchical Sampling (HS) algorithm. This work takes as a starting point the original Hierarchical Sampling algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification.

Com a grande variedade de produtos e serviços disponíveis na Web, os usuários possuem, em geral, muita liberdade de escolha, o que poderia ser considerado uma vantagem se não fosse pela dificuldade encontrada em escolher o produto ou serviço que mais atenda a suas necessidades dentro do vasto conjunto de opções disponíveis. Sistemas de recomendação são sistemas que têm como objetivo auxiliar esses usuários a identificarem itens de interesse em um conjunto de opções. A maioria das abordagens de sistemas de recomendação foca em recomendar itens mais relevantes para usuários individuais, não levando em consideração o contexto dos usuários. Porém, em muitas aplicações é importante também considerar informações contextuais para fazer as recomendações. Por exemplo, um usuário pode desejar assistir um filme com a sua namorada no sábado à noite ou com os seus amigos durante um dia de semana, e uma locadora de filmes na Web pode recomendar diferentes tipos de filmes para este usuário dependendo do contexto no qual este se encontra. Um grande desafio para o uso de sistemas de recomendação sensíveis ao contexto é a falta de métodos para aquisição automática de informação contextual para estes sistemas. Diante desse cenário, neste trabalho é proposto um método para extrair informações contextuais do conteúdo de páginas Web que consiste em construir hierarquias de tópicos do conteúdo textual das páginas considerando, além da bag-of-words tradicional (informação técnica), também informações mais valiosas dos textos como entidades nomeadas e termos do domínio (informação privilegiada). Os tópicos extraídos das hierarquias das páginas Web são utilizados como informações de contexto em sistemas de recomendação sensíveis ao contexto. Neste trabalho foram realizados experimentos para avaliação do contexto extraído pelo método proposto em que foram considerados dois baselines: um sistema de recomendação que não considera informação de contexto e um método da literatura de extração de contexto implementado e adaptado para este mestrado. Além disso, foram utilizadas duas bases de dados. Os resultados obtidos foram, de forma geral, muito bons apresentando ganhos significativos sobre o baseline sem contexto. Com relação ao baseline que extrai informação contextual, o método proposto se mostrou equivalente ou melhor que o mesmo. / With the wide variety of products and services available on the web, it is difficult for users to choose the option that most meets their needs. In order to reduce or even eliminate this difficulty, recommender systems have emerged. A recommender system is used in various fields to recommend items of interest to users. Most recommender approaches focus only on users and items to make the recommendations. However, in many applications it is also important to incorporate contextual information into the recommendation process. For example, a user may want to watch a movie with his girlfriend on Saturday night or with his friends during a weekday, and a video store on the Web can recommend different types of movies for this user depending on his context. Although the use of contextual information by recommendation systems has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. For this reason, the acquisition of contextual information is a research area that needs to be better explored. In this scenario, this work proposes a method to extract contextual information of Web page content. This method builds topic hierarchies of the pages textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. By using two databases, experiments were conducted to evaluate the contextual information extracted by the proposed method. Two baselines were considered: a recommendation system that does not use contextual information (IBCF) and a method proposed in literature to extract contextual information (\\methodological\" baseline), adapted for this research. The results are, in general, very good and show significant gains over the baseline without context. Regarding the \"methodological\" baseline, the proposed method is equivalent to or better than this baseline.

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Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make the comparisons between the team and hierarchies meaningful, we explore parameter values determining how simulated human operators behave in teams and hierarchies to optimize the performance of the respective organizations. We show that simulation results generally support the hypothesis with respect to the effect of latency and bandwidth on organizational performance.

As widely argued [HG97; Sat96], transitive roles play an important role in the adequate representation of aggregated objects: they allow these objects to be described by referring to their parts without specifying a level of decomposition. In [HG97], the Description Logic (DL) ALCHR+ is presented, which extends ALC with transitive roles and a role hierarchy. It is argued in [Sat98] that ALCHR+ is well-suited to the representation of aggregated objects in applications that require various part-whole relations to be distinguished, some of which are transitive. However, ALCHR+ allows neither the description of parts by means of the whole to which they belong, or vice versa. To overcome this limitation, we present the DL SHI which allows the use of, for example, has part as well as is part of. To achieve this, ALCHR+ was extended with inverse roles. It could be argued that, instead of defining yet another DL, one could make use of the results presented in [DL96] and use ALC extended with role expressions which include transitive closure and inverse operators. The reason for not proceeding like this is the fact that transitive roles can be implemented more efficiently than the transitive closure of roles (see [HG97]), although they lead to the same complexity class (ExpTime-hard) when added, together with role hierarchies, to ALC. Furthermore, it is still an open question whether the transitive closure of roles together with inverse roles necessitates the use of the cut rule [DM98], and this rule leads to an algorithm with very bad behaviour. We will present an algorithm for SHI without such a rule. Furthermore, we enrich the language with functional restrictions and, finally, with qualifying number restrictions. We give sound and complete decision proceduresfor the resulting logics that are derived from the initial algorithm for SHI. The structure of this report is as follows: In Section 2, we introduce the DL SI and present a tableaux algorithm for satisfiability (and subsumption) of SI-concepts—in another report [HST98] we prove that this algorithm can be refined to run in polynomial space. In Section 3 we add role hierarchies to SI and show how the algorithm can be modified to handle this extension appropriately. Please note that this logic, namely SHI, allows for the internalisation of general concept inclusion axioms, one of the most general form of terminological axioms. In Section 4 we augment SHI with functional restrictions and, using the so-called pairwise-blocking technique, the algorithm can be adapted to this extension as well. Finally, in Section 5, we show that standard techniques for handling qualifying number restrictions [HB91;BBH96] together with the techniques described in previous sections can be used to decide satisfiability and subsumption for SHIQ, namely ALC extended with transitive and inverse roles, role hierarchies, and qualifying number restrictions. Although Section 5 heavily depends on the previous sections, we have made it self-contained, i.e. it contains all necessary definitions and proofs from scratch, for a better readability. Building on the previous sections, Section 6 presents an algorithm that decides the satisfiability of SHIQ-ABoxes.

The essence and management of competitive cooperation are defined in the master's dissertation. The main reason for the transition to competitive cooperation was identified. The tools of marketing management in competitive cooperation are grounded. The analysis of mutual relations of the companies in the industrial market is made. The creation of a competitive cooperation ALMA ceramica is justified. A method for analyzing the possible choice of alternatives has been developed. A model of competitive cooperation has been created. / В магистерской диссертации определены сущность и управление конкурентным сотрудничеством. Определена главная причина перехода к конкурентному сотрудничеству. Обоснованы инструменты управления маркетингом в конкурентном сотрудничестве. Произведен анализ взаимоотношений компаний на промышленном рынке. Обосновано создание конкурентного сотрудничества компании ALMA ceramica. Разработан метод анализа возможного выбора альтернатив. Создана модель конкурентного сотрудничества.

Decision support systems are interactive, computer-based information systems that provide data and analysis tools in order to better assist managers on different levels of organization in the process of decision making. Data warehouses (DWs) have been developed and deployed as an integral part of decision support systems. A data warehouse is a database that allows to store high volume of historical data required for analytical purposes. This data is extracted from operational databases, transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process. DW data can be dynamically manipulated using on-line analytical processing (OLAP) systems. DW and OLAP systems rely on a multidimensional model that includes measures, dimensions, and hierarchies. Measures are usually numeric additive values that are used for quantitative evaluation of different aspects about organization. Dimensions provide different analysis perspectives while hierarchies allow to analyze measures on different levels of detail. Nevertheless, currently, designers as well as users find difficult to specify multidimensional elements required for analysis. One reason for that is the lack of conceptual models for DW and OLAP system design, which would allow to express data requirements on an abstract level without considering implementation details. Another problem is that many kinds of complex hierarchies arising in real-world situations are not addressed by current DW and OLAP systems. In order to help designers to build conceptual models for decision-support systems and to help users in better understanding the data to be analyzed, in this thesis we propose the MultiDimER model - a conceptual model used for representing multidimensional data for DW and OLAP applications. Our model is mainly based on the existing ER constructs, for example, entity types, attributes, relationship types with their usual semantics, allowing to represent the common concepts of dimensions, hierarchies, and measures. It also includes a conceptual classification of different kinds of hierarchies existing in real-world situations and proposes graphical notations for them. On the other hand, currently users of DW and OLAP systems demand also the inclusion of spatial data, visualization of which allows to reveal patterns that are difficult to discover otherwise. The advantage of using spatial data in the analysis process is widely recognized since it allows to reveal patterns that are difficult to discover otherwise. However, although DWs typically include a spatial or a location dimension, this dimension is usually represented in an alphanumeric format. Furthermore, there is still a lack of a systematic study that analyze the inclusion as well as the management of hierarchies and measures that are represented using spatial data. With the aim of satisfying the growing requirements of decision-making users, we extend the MultiDimER model by allowing to include spatial data in the different elements composing the multidimensional model. The novelty of our contribution lays in the fact that a multidimensional model is seldom used for representing spatial data. To succeed with our proposal, we applied the research achievements in the field of spatial databases to the specific features of a multidimensional model. The spatial extension of a multidimensional model raises several issues, to which we refer in this thesis, such as the influence of different topological relationships between spatial objects forming a hierarchy on the procedures required for measure aggregations, aggregations of spatial measures, the inclusion of spatial measures without the presence of spatial dimensions, among others. Moreover, one of the important characteristics of multidimensional models is the presence of a time dimension for keeping track of changes in measures. However, this dimension cannot be used to model changes in other dimensions. Therefore, usual multidimensional models are not symmetric in the way of representing changes for measures and dimensions. Further, there is still a lack of analysis indicating which concepts already developed for providing temporal support in conventional databases can be applied and be useful for different elements composing a multidimensional model. In order to handle in a similar manner temporal changes to all elements of a multidimensional model, we introduce a temporal extension for the MultiDimER model. This extension is based on the research in the area of temporal databases, which have been successfully used for modeling time-varying information for several decades. We propose the inclusion of different temporal types, such as valid and transaction time, which are obtained from source systems, in addition to the DW loading time generated in DWs. We use this temporal support for a conceptual representation of time-varying dimensions, hierarchies, and measures. We also refer to specific constraints that should be imposed on time-varying hierarchies and to the problem of handling multiple time granularities between source systems and DWs. Furthermore, the design of DWs is not an easy task. It requires to consider all phases from the requirements specification to the final implementation including the ETL process. It should also take into account that the inclusion of different data items in a DW depends on both, users' needs and data availability in source systems. However, currently, designers must rely on their experience due to the lack of a methodological framework that considers above-mentioned aspects. In order to assist developers during the DW design process, we propose a methodology for the design of conventional, spatial, and temporal DWs. We refer to different phases, such as requirements specification, conceptual, logical, and physical modeling. We include three different methods for requirements specification depending on whether users, operational data sources, or both are the driving force in the process of requirement gathering. We show how each method leads to the creation of a conceptual multidimensional model. We also present logical and physical design phases that refer to DW structures and the ETL process. To ensure the correctness of the proposed conceptual models, i.e., with conventional data, with the spatial data, and with time-varying data, we formally define them providing their syntax and semantics. With the aim of assessing the usability of our conceptual model including representation of different kinds of hierarchies as well as spatial and temporal support, we present real-world examples. Pursuing the goal that the proposed conceptual solutions can be implemented, we include their logical representations using relational and object-relational databases.

This project explores, identifies, and interrogates how Swedish online mass media represent the issue of 'racisms' between people with migrant background/migrant groups and the implications of these representations on their lives. The materials are analyzed using the WPR approach (Bacchi 1999) and content media analysis. The theoretical background draws from social psychological theory of intergroup relations, postcolonial theory and critical race theory in order to establish an overview of the concept of ‘racism’ and how it can be used in relation to migrant groups. Where the media representations maintained that migrants can produce 'racism', led to further stigmatization and exclusion of migrant groups. Internalizing racism by migrants can result in self-hatred and practicing migrant respectability in order to differentiate themselves from undesirable migrants as represented in the materials. This project contributes in exploring the system of hierarchies which can deepen our understanding of how tensions and conflicts work between migrant groups.

O número de documentos textuais disponíveis em formato digital tem aumentado incessantemente. Técnicas de Mineração de Textos são cada vez mais utilizadas para organizar e extrair conhecimento de grandes coleções de documentos textuais. Para o uso dessas técnicas é necessário que os documentos textuais estejam representados em um formato apropriado. A maioria das pesquisas de Mineração de Textos utiliza a abordagem bag-of-words para representar os documentos da coleção. Essa representação usa cada palavra presente na coleção de documentos como possível atributo, ignorando a ordem das palavras, informa ções de pontuação ou estruturais, e é caracterizada pela alta dimensionalidade e por dados esparsos. Por outro lado, a maioria dos conceitos são compostos por mais de uma palavra, como Inteligência Articial, Rede Neural, e Mineração de Textos. As abordagens que geram atributos compostos por mais de uma palavra apresentam outros problemas além dos apresentados pela representação bag-of-words, como a geração de atributos com pouco signicado e uma dimensionalidade muito maior. Neste projeto de mestrado foi proposta uma abordagem para representar documentos textuais nomeada bag-of-related-words. A abordagem proposta gera atributos compostos por palavras relacionadas com o uso de regras de associação. Com as regras de associação, espera-se identicar relações entre palavras de um documento, além de reduzir a dimensionalidade, pois são consideradas apenas as palavras que ocorrem ou que coocorrem acima de uma determinada frequência para gerar as regras. Diferentes maneiras de mapear o documento em transações para possibilitar a geração de regras de associação são analisadas. Diversas medidas de interesse aplicadas às regras de associação para a extração de atributos mais signicativos e a redução do número de atributos também são analisadas. Para avaliar o quanto a representação bag-of-related-words pode auxiliar na organização e extração de conhecimento de coleções de documentos textuais, e na interpretabilidade dos resultados, foram realizados três grupos de experimentos: 1) classicação de documentos textuais para avaliar o quanto os atributos da representação bag-of-related-words são bons para distinguir as categorias dos documentos; 2) agrupamento de documentos textuais para avaliar a qualidade dos grupos obtidos com a bag-of-related-words e consequentemente auxiliar na obtenção da estrutura de uma hierarquia de tópicos; e 3) construção e avaliação de hierarquias de tópicos por especialistas de domínio. Todos os resultados e dimensionalidades foram comparados com a representação bag-of-words. Pelos resultados dos experimentos realizados, pode-se vericar que os atributos da representação bag-of-related-words possuem um poder preditivo tão bom quanto os da representação bag-of-words. A qualidade dos agrupamentos de documentos textuais utilizando a representação bag-of-related-words foi tão boa quanto utilizando a representação bag-of-words. Na avaliação de hierarquias de tópicos por especialistas de domínio, a utilização da representação bag-of-related-words apresentou melhores resultados em todos os quesitos analisados / The amount of textual documents available in digital format is incredibly large. Text Mining techniques are becoming essentials to manage and extract knowledge in big textual document collections. In order to use these techniques, the textual documents need to be represented in an appropriate format to allow the construction of a model that represents the embedded knowledge in these textual documents. Most of the researches on Text Mining uses the bag-of-words approach to represent textual document collections. This representation uses each word in a collection as feature, ignoring the order of the words, structural information, and it is characterized by the high dimensionality and data sparsity. On the other hand, most of the concepts are compounded by more than one word, such as Articial Intelligence, Neural Network, and Text Mining. The approaches which generate features compounded by more than one word to solve this problem, suer from other problems, as the generation of features without meaning and a dimensionality much higher than that of the bag-of-words. An approach to represent textual documents named bag-of-related-words was proposed in this master thesis. The proposed approach generates features compounded by related words using association rules. We hope to identify relationships among words and reduce the dimensionality with the use of association rules, since only the words that occur and cooccur over a frequency threshold will be used to generate rules. Dierent ways to map the document into transactions to allow the extraction of association rules are analyzed. Dierent objective interest measures applied to the association rules to generate more meaningful features and to the reduce the feature number are also analyzed. To evaluate how much the textual document representation proposed in this master project can aid the managing and knowledge extraction from textual document collections, and the understanding of the results, three experiments were carried out: 1) textual document classication to analyze the predictive power of the bag-of-related-words features, 2) textual document clustering to analyze the quality of the cluster using the bag-of-related-words representation 3) topic hierarchies building and evaluation by domain experts. All the results and dimensionalities were compared to the bag-of-words representation. The results presented that the features of the bag-of-related-words representation have a predictive power as good as the features of the bag-of-words representation. The quality of the textual document clustering also was as good as the bag-of-words. The evaluation of the topic hierarchies by domain specialists presented better results when using the bag-of-related-words representation in all the questions analyzed

Au cours de la dernière décennie, le trafic dans les réseaux a connu une croissance explosive en double environ tous les trente trois mois. Les sources à l'origine de cette croissance proviennent de nombreuses applications à grande vitesse qui impliquent la transmission de données dans des groupes de multicast. Pour réaliser la multicast optique, les routeurs optiques peuvent avoir des répartiteurs de lumière spéciaux pour diviser des signaux lumineux et des convertisseurs de longueur d'onde pour modifier les longueurs d'onde où c'est nécessaire. Cependant, la division réduit l'énergie du signal qui nécessite alors une amplification ou une régénération qui nécessitent du matériel coûteux. Les convertisseurs de longueurs d'onde aussi ne sont pas suffisamment matures pour être largement déployés dans les technologies optiques actuelles. Par conséquent, dans les réseaux tout-optique, les unités de transformation des routeurs sont souvent hétérogènes et les algorithmes de routage doivent en tenir compte tout en parvenant à des solutions de compromis coût-performances qui satisfassent les exigences de bande passante et les contraintes optiques.Dans cette thèse, nous étudions les problème de routage multicast tout-optique (AOMR) dans les réseaux tout-optique hétérogènes. L'hétérogénéité provient principalement de l'absence / présence de séparateurs de lumière et de convertisseurs de longueur d'onde et de la répartition inégale des longueurs d'onde dans les liens du réseau. En général, les problèmes de AOMR sont NP-difficiles. L'objectif de la thèse est d'analyser et de formuler les problèmes sous différentes contraintes optiques, pour rechercher des solutions optimales ou proposer des heuristiques efficaces. Les deux contextes possibles, la demande unique ou multiple de multicast, sont examinés. Toutes les propositions présentées dans la thèse sont validées par des simulations approfondies. Les principales contributions peuvent être résumées comme il suit.1) Nous identifions les structures des routes optimales pour les problèmes de l'AOMR dans les réseaux WDM hétérogènes. Comme indiqué dans la thèse, les solutions optimales ne sont plus basées sur des arbres de lumière classiques, mais sur une structure arborescente plus générale appelée hiérarchie. Certaines formes de hiérarchie pour la multicast WDM sont des parcours optiques, des hiérarchies optiques, des hiérarchies-araignée optiques ou encore des ensembles de ces routes optiques. Les algorithmes exacts et les heuristiques proposés dans la thèse sont principalement basés sur les hiérarchies.2) Dans le cas du problème de multicast avec une seule demande dans des réseaux partiellement équipés de diviseurs de lumière, nous proposons une heuristique efficace dont les résultats font le compromis entre la consommation de longueur d'onde, le coût total, et le délai de bout-en-bout.3) Dans le même cas mais dans des réseaux non équipés de diviseurs de lumière, nous prouvons la NP-difficulté, exprimons les problèmes au moyen d'un programme linéaire (ILP) pour trouver les solutions exactes et proposons plusieurs heuristiques pour calculer de bonnes solutions.4) Pour le cas de demandes multicast multiples, nous nous concentrons sur les modèles de trafic statiques dans des réseaux partiellement équipés de diviseurs mais sans convertisseurs de longueur d'onde. Tout d'abord nous proposons une formulation ILP sur la base de hiérarchies optiques afin de rechercher la solution optimale. Ensuite, utilisant un modèle de graphe en couches, nous développons plusieurs heuristiques adaptatives pour calculer des hiérarchies optiques de solutions approximatives. Ces algorithmes adaptatifs surpassent les techniques de routage existants pour minimiser la probabilité de blocage.Dans l'ensemble, la thèse souligne que les solutions optimales pour les problèmes de l'AOMR considérés correspondent à des hiérarchies, que ce soit pour une seule demande ou des demandes multiples. / Over the past decade, network traffic levels experienced an explosive growth at about double amount in approximately every thirty months. The sources accounting for this growth come from numerous high-speed applications (e.g., video-on-demand, high-definition television) which involve the data transmission in multicast groups. To realize optical multicasting, optical routers should have light splitters to split light signals and wavelength converters to change the wavelengths wherever needed. However, the splitting reduces the energy of the output signal which in turn requires the costly power amplification or regeneration. Wavelength converters are also immature to be deployed widely in current optical technologies. Consequently, in all-optical networks, routers are often heterogeneous in their processing units, which challenges the routing. Therefore, it is crucial to design efficient multicast routing strategies at the backbone optical networks, in order to achieve cost-performance tradeoff solutions while satisfying the ever-increasing bandwidth demands and optical constraints.In this thesis, we investigate the all-optical multicast routing (AOMR) problems in heterogeneous optical networks. The heterogeneity mainly comes from the absence/presence of light splitters and wavelength converters and the uneven distribution of wavelengths in the network links. In general, AOMR problems are often NP-hard. The objective of the thesis is to analyze and formulate the problems, to search for the optimal solutions, and to propose efficient heuristics to solve the problems under different optical constraints. Both possible contexts, i.e., single-multicast request and multiple-multicast requests, are examined. All the reported results in the thesis are supported by extensive and careful simulations. The major contributions can be summarized as follows.1) We identify the optimal route structures for AOMR problems under heterogeneous mesh WDM networks. As shown in the thesis, the optimal solutions are no longer based on conventional light-trees, but a more general tree-like structure called hierarchy. Some forms of hierarchy realized for WDM multicasting are light-trails, light-hierarchies, light-spider hierarchies and a set of these light-structures. The exact and heuristic algorithms proposed in the thesis are mainly based on hierarchy. 2) For single-multicast with sparse-splitting case, we propose an efficient heuristic algorithm to produce a good tradeoff solution among wavelength consumption, channel total cost and end-to-end delay.3) For single-multicast with non-splitting case, we prove the NP-hardness, identify the optimal solution as a set of light-spider hierarchies, formulate the problems by means of Integer Linear Program (ILP) formulations to find the exact solution, and propose several cost-effective heuristic algorithms to compute the approximate solutions. 4) For the case with multiple-multicast requests, we focus on static traffic patterns under sparse-splitting without wavelength conversion case. First, an ILP formulation based on light-hierarchies is proposed to search for the optimal solution. By applying the layered graph model, we then develop several adaptive heuristic algorithms to compute light-hierarchies for approximate solutions. These adaptive algorithms outperform the existing fixed routing ones in minimizing the blocking probability. Overall, the thesis points out that the optimal solutions for heterogeneously constrained AOMR problems correspond to hierarchies, regardless of request multiplicity consideration.