Integration of Green Quality Function Deployment and Fuzzy Multi-Attribute Utility Theory-Based Cost Estimation for Environmentally Conscious Product Development

There are increasing global demands for environmental friendly products. Green Quality Function Deployment - III (GQFD - III) is an innovative tool aiding in the development of environmentally conscious products and processes. An improved version of GQFD - III, Green Quality Function Deployment - IV (GQFD - IV) has been developed in this study. Its improvement over GQFD - III is that the life cycle cost is estimated using the Fuzzy Multi-Attribute Utility Theory (FMAUT) method. FMAUT costing is an excellent cost estimation method at the early design stage in product development. It is more effective than other traditional methods because it does not require detailed data on manufacturing processes of the product and it can handle attributes with uncertainty and incompleteness in nature. In a case study, life cycle costs of coffeemakers were estimated with errors of less than 7% using this new cost estimation model. In GQFD - IV, with the considerations of quality, environment and cost, analytical hierarchy process (AHP) is used for product concept selection and is found to be effective

A methodology of integrating marketing with engineering for defining design specifications of new products

In the fuzzy front end stage of new product development, it is quite common that marketing personnel and product engineers have different goals and concerns. Their two sets of goals and concerns are always addressed in isolation from one another. This isolation typically would not result in optimal design decisions as two sets of goals and concerns are always interrelated. Therefore, it is important to integrate the concerns of marketing personnel with those of engineers in defining design specifications. Perceptual mapping is a very common technique used by marketing personnel to understand market positions of competitive products and help define new product opportunities. Although quite a few research works have been attempted to integrate marketing with engineering concerns for new product design, perceptual mapping was not considered in defining design specifications of a new product in the previous related studies. In this paper, a methodology of integrating marketing with engineering for defining design specifications of new products is described, which mainly involves generation of perceptual maps, generation of fuzzy regression models for relating customer requirements and design attributes, formulation of an optimisation model and solving the model based on genetic algorithms. A case study of defining design specifications of a new packing machine was used to illustrate the proposed methodology

Varying Spread Fuzzy Regression for Affective Quality Estimation

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state of art and the recently developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria, which address both predictions of magnitudes and fuzziness

Advancements in QFD optimization methods

Ph.DDOCTOR OF PHILOSOPH

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model.To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods

Machine Learning na previsão de Cancro Colorretal em função de alterações metabólicas

No mundo atual, a quantidade de informação disponível nos mais variados setores é cada vez maior. É o caso da área da saúde, onde a recolha e tratamento de dados biomédicos procuram melhorar a tomada de decisão no tratamento a aplicar a um doente, recorrendo a ferramentas baseadas em Machine Learning. Machine Learning é uma área da Inteligência Artificial em que através da aplicação de algoritmos a um conjunto de dados é possível prever resultados ou até descobrir relações entre estes que seriam impercetíveis à primeira vista. Com este projeto pretende-se realizar um estudo em que o objetivo é investigar diversos algoritmos e técnicas de Machine Learning, de modo a identificar se o perfil de acilcarnitinas pode constituir um novo marcador bioquímico para a predição e prognóstico do Cancro Colorretal. No decurso do trabalho, foram testados diferentes algoritmos e técnicas de pré-processamento de dados. Foram realizadas três experiências distintas com o objetivo de validar as previsões dos modelos construídos para diferentes cenários, nomeadamente: prever se o paciente tem Cancro Colorretal, prever qual a doença que o paciente tem (Cancro Colorretal e outras doenças metabólicas) e prever se este tem ou não alguma doença. Numa primeira análise, os modelos desenvolvidos apresentam bons resultados na triagem de Cancro Colorretal. Os melhores resultados foram obtidos pelos algoritmos Random Forest e Gradient Boosting, em conjunto com técnicas de balanceamento dos dados e Feature Selection, nomeadamente Random Oversampling, Synthetic Oversampling e Recursive Feature SelectionIn today´s world, the amount of information available in various sectors is increasing. That is the case in the healthcare area, where the collection and treatment of biochemical data seek to improve the decision-making in the treatment to be applied to a patient, using Machine Learning-based tools. Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. This project’s main objective is to study several algorithms and techniques of Machine Learning to identify if the acylcarnitine profile may constitute a new biochemical marker for the prediction and prognosis of rectal cancer. In the course of the work, different algorithms and data preprocessing techniques were tested. Three different experiments were carried out to validate the predictions of the models built for different scenarios, namely: predicting whether the patient has Colorectal Cancer, predicting which disease the patient has (Colorectal Cancer and other metabolic diseases) and predicting whether he has any disease. As a first analysis, the developed models showed good results in Colorectal Cancer screening. The best results were obtained by the Random Forest and Gradient Boosting algorithms, together with data balancing and feature selection techniques, namely Random Oversampling, Synthetic Oversampling and Recursive Feature Selectio

Fuzzy Regression for Perceptual Image Quality Assessment

Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in current methodologies for predicting subjective IQA, where the fuzziness indicates assessment uncertainty. In this article, we propose a fuzzy regression method that accounts for fuzziness introduced through human judgment and the limitations of widely-used psychometric quality scales. We demonstrate how fuzzy regression models provide fuzziness information regarding subjective IQA. We benchmark the fuzzy regression method against the commonly used explicit modeling method for subjective IQA namely statistical regression by considering three real situations involving subjective image quality experiments where: (a) the number of participants is insufficient; (b) an insufficient amount of data is used for modelling; and (c) variant fuzziness is caused by human judgment. Results indicate that fuzzy regression models achieve more effective data fitting and better generalization capability when predicting subjective IQA under different types and levels of image distortion

Studies on Product Design using Ergonomic Considerations

Embedding ergonomic consideration into product/machine/equipment/component design as well as work environment taking into account both psychological and physical needs of user helps to enhance user efficiency, satisfaction and productivity. It is vital to find best design elements to visualize the product which possesses the characteristics not only to satisfy the users but also reduces fatigue and injury during prolonged use. Although subjective and objective product characteristics are important during product design, user comfort becomes a vital factor that can be quantified by the analysis on continuous physical interaction between product and user. Beside above influential factors, ergonomic design of product also considers cognitive and behavioral information during the design stage with a view to improve the comfort level of the user and aesthetic look of the product. To address above issues, an integrated approach using statistical and artificial intelligence techniques has been proposed in this thesis to effectively handle subjective and objectives characteristics during design phase. The statistical method is used to assess various user requirements and their significance whereas artificial intelligence method determines the relationship between user requirements and product characteristics. Since most of the psychological needs of users are difficult to express quantitatively, combined approach of statistical and artificial intelligence method can handle the subjectivity and uncertainty in an effective manner. The approach has been demonstrated with the help of design of office chair. Keeping view with the physical interaction between human soft tissue and product as a measuring factor of comfort sensation in an office environment, a numerical analysis of human soft tissue-chair seat model has been introduced into current work. In order to evaluate superior ergonomically designed product (office chair), suitable multi-attribute decision making (MADM) approach based on few important features has been chosen to address the usability of product improving satisfaction level of customer. The study also analyses a kinematic model of human upper arm extremity to diagnose comfort arm posture that allows the operator to have a comfort work zone within which possible postures can be accepted

Logistics service providers (LSPs) evaluation and selection: Literature review and framework development

Purpose – The purpose of this paper is to provide an insight to the outsourcing decision-making through investigating if the old evaluation/selection criteria and methods still fit with current business priorities or not and, therefore, to identify the appropriate criteria and methods to develop a new selection framework. Since the economic recession of 2008, logistics outsourcing decisions have become more prominent to avoid high fixed costs and heavy investment requirements and to achieve competitive advantages. Design/methodology/approach – This is a focused literature review prepared after analyzing 56 articles related to the logistics service provider (LSP) evaluation and selection methods and criteria during 2008-2013. The academic articles are analyzed based on research focus/area, evaluation and selection methodology/methods and evaluation and selection criteria. Then reviewed result is compared with previous literature studies for the periods (1991-2008) to identify any possible shifts. Findings – The review reveals that: several problems in current LSPs literature have been identified; the reviewed papers can be categorized into seven groups, the usage and importance of evaluation and selection criteria fluctuate during different periods; 12 crucial criteria have been identified, increasing the importance of specific selection methods and the integrated models and fuzzy logic in logistics literature. Then, a comprehensive LSPs’ evaluation and selection framework has been developed. Originality/value – To the best of our knowledge, this is the first focused logistics outsourcing study that reviews the 2008-2013 period in detail, comparing results with previous literature studies, identifies current LSPs literature problems/gaps, new trends and shifts in the way that LSPs are evaluated and selected, identifies crucial selection criteria and proposes a new holistic LSPs evaluation and selection framework. In addition, it identifies important issues for future research. Keywords Supplier or partner selection, Evaluation and selection methods and criteria, Logistics outsourcing, Logistics service provider, LSP framewor