A method to weight three categories of adaptive thermal comfort

The adaptive thermal comfort theory considers people as active rather than passive recipients in response to ambient physical thermal stimuli, in contrast with conventional, heat-balance-based, thermal comfort theory. Occupants actively interact with the environments they occupy by means of utilizing adaptations in terms of physiological, behavioural and psychological dimensions to achieve ‘real world’ thermal comfort. This paper introduces a method of quantifying the physiological, behavioural and psychological portions of the adaptation process by using the analytic hierarchy process (AHP) based on the case studies conducted in the UK and China. Apart from three categories of adaptations which are viewed as criteria, six possible alternatives are considered: physiological indices/health status, the indoor environment, the outdoor environment, personal physical factors, environmental control and thermal expectation. With the AHP technique, all the above-mentioned criteria, factors and corresponding elements are arranged in a hierarchy tree and quantified by using a series of pair-wise judgements. A sensitivity analysis is carried out to improve the quality of these results. The proposed quantitative weighting method provides researchers with opportunities to better understand the adaptive mechanisms and reveal the significance of each category for the achievement of adaptive thermal comfort.

An assessment tool for sub-catchment to catchment scale indicators of sustainability - a case study in south west Victoria, Australia

The research reported in this paper represents an attempt to produce a practical, indicator-based sustainability assessment tool incorporating all these elements is based on relationships between indicators determined considering spatial influences. Through the use of an existing sustainability indicator set and data currently available, relationships will be determined using Arcview Geographic Information Systems (GIS), correlation analysis and Principal Component Analysis (PCA). Indicator interactions will be identified at two spatial scales and compared to determine impacts of changing spatial scale. Further PCA and multiple regression analyses will then be used to reduce the complexity of the indicator set. These findings will be incorporated into a practical indicator-based assessment tool through the adoption of the Analytic Hierarchy Process (AHP) combined with GIS techniques that will then be validated. Once validated the tool can be used to aid in guiding planning and decision-making regarding sustainable development in the Glenelg Hopkins catchment, Victoria; while also moving towards producing a standard set of procedures for assessing sustainability.

Marketing strategy for small application service providers in a new market

Small to medium-sized enterprises (SMEs) including small application service providers (ASPs) are playing an increasingly important role in the development of global economies particularly in developing countries like China. This paper studies marketing strategies of small application service providers (ASP) with a focus on what the important factors are to establish a new ASP business in China. An analytical hierarchy process (AHP) method is used to analyse critical factors of the ASP industry. The research surveyed CEOs or senior managers who are working in ASP firms, to identify how a marketing strategy can be developed for an ASP firm to start business in China. It is found that the localisation of middle level managers, the localisation of products and services, the protection of intellectual property (IP), and infrastructure and transportation system are the most important factors for small ASP firms to do business in China.

A decision-making model to choose business intelligence platforms for organizations

In recent time, technology applications in different fields, especially Business Intelligence (BI) have been developed rapidly and considered to be one of the most significant uses of information technology with special position reserved. The application of BI systems provides organizations with a sense of superiority in the competitive environment. Despite many advantages, the companies applying such systems may also encounter problems in decision-making process because of the highly diversified interactions within the systems. Hence, the choice of a suitable BI platform is important to take the great advantage of using information technology in all organizational fields. The current research aims at addressing the problems existed in the organizational decision-making process, proposing and implementing a suitable BI platform using Iranian companies as case study. The paper attempts to present a solitary model based on studying different methods in BI platform choice and applying the chosen BI platform for different decisionmaking processes. The results from evaluating the effectiveness of subsequently implementing the model for Iranian Industrial companies are discussed.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Seleção de projetos de P&D em empresas públicas do setor elétrico : o caso da Eletrosul e proposta de metodologia

Uma das formas de acesso à inovação tecnológica é a pesquisa e desenvolvimento (P&D) de novos produtos. Entre projetos de P&D propostos, a empresa precisa selecionar aqueles em que vai realizar investimentos. Esta dissertação é do tipo "proposta de planos ou programas", e analisa a seleção de projetos de P&D. Muitos gerentes de P&D não acreditam que os métodos disponíveis para seleção de projetos melhorem a qualidade das suas decisões. Algumas das fraquezas identificadas em métodos de seleção de projetos são o tratamento inadequado de múltiplos critérios, às vezes interdependentes, o tratamento inadequado do risco e da incerteza, a dificuldade em reconhecer e tratar aspectos não-monetários, e a percepção pelos gerentes de P&D que os modelos são desnecessariamente difíceis de entender e utilizar. O objetivo deste estudo é analisar os benefícios proporcionados pela utilização da teoria de opções reais, do alinhamento dos projetos de P&D à estratégia da empresa e do método de apoio à decisão Analytic Hierarchy Process (AHP) para o processo de seleção de projetos de P&D em empresas do setor elétrico brasileiro, e em especial será analisado o caso da Eletrosul. A teoria de opções reais e o alinhamento com a estratégia da empresa proporcionam uma considerável ampliação da visão gerencial necessária para decidir quais projetos devem ser executados. A análise dos projetos à luz da estratégia da empresa facilita a efetiva implantação dela, conduz a uma reflexão sobre a estratégia e possibilita que universidades e centros de pesquisa alinhem suas propostas a ela. O método AHP mostrou-se adequado para apoiar o processo de seleção de projetos de P&D, subdividindo uma decisão complexa em comparações duas a duas e a seguir sintetizando-as para chegar a um resultado sobre a decisão complexa. A facilidade de compreensão e a transparência do funcionamento são pontos fortes do método AHP.

Measuring the efficiency of outsourcing: An illustrative case study from the aerospace industry

Outsourcing is related to the action which an organization deals with its suppliers through a kind of business contract where a specific activity or service has been hired to be made. The outsourcing of some activities has become a common practice in the industry, nowadays. It reduces costs, significantly, in the production process and, at the same time, adds some values to the business organization. However it is necessary to measure the performance of these activities. Data Envelopment Analysis (DEA) is a non-parametric method useful to measure comparative performance. It has a wide range of applications measuring comparative efficiency. The Analytic Hierarchy Process (AHP) is a multiple criteria decision-making method that uses hierarchic structures to represent a decision problem and then develops priorities for the alternatives based on the decision-maker's judgments. This paper presents an integrated application based on DEA and AHP to evaluate the efficiency of subcontracted companies in a Brazilian aerospace factory. © 2007 Springer-Verlag London Limited.

Proposta para indicação de áreas para a implantação de aterro sanitário no município de Bauru-SP, utilizando análise multi critério de decisão e técnicas de geoprocessamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação multicritério de prestadores de serviços logísticos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)