Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average

AbstractFuzzy quality function deployment (QFD) has been extensively used for translating customer requirements (CRs) into product design requirements (DRs) in fuzzy environments. Existing approaches, however, for rating technical importance of DRs in fuzzy environments are found problematic, either incorrect or inappropriate. This paper investigates how the technical importance of DRs can be correctly rated in fuzzy environments. A pair of nonlinear programming models and two equivalent pairs of linear programming models are developed, respectively, to rate the technical importance of DRs. The developed models are examined and illustrated with two numerical examples

Nonparametric numerical approaches to probability weighting function construct for manifestation and prediction of risk preferences

Probability weighting function (PWF) is the psychological probability of a decision-maker for objective probability, which reflects and predicts the risk preferences of decision-maker in behavioral decisionmaking. The existing approaches to PWF estimation generally include parametric methodologies to PWF construction and nonparametric elicitation of PWF. However, few of them explores the combination of parametric and nonparametric elicitation approaches to approximate PWF. To describe quantitatively risk preferences, the Newton interpolation, as a well-established mathematical approximation approach, is introduced to task-specifically match PWF under the frameworks of prospect theory and cumulative prospect theory with descriptive psychological analyses. The Newton interpolation serves as a nonparametric numerical approach to the estimation of PWF by fitting experimental preference points without imposing any specific parametric form assumptions. The elaborated nonparametric PWF model varies in accordance with the number of the experimental preference points elicitation in terms of its functional form. The introduction of Newton interpolation to PWF estimation into decision-making under risk will benefit to reflect and predict the risk preferences of decision-makers both at the aggregate and individual levels. The Newton interpolation-based nonparametric PWF model exhibits an inverse S-shaped PWF and obeys the fourfold pattern of decision-makers’ risk preferences as suggested by previous empirical analyses. First published online 17 April 202

Meeting the cultural and service needs of Arabic international students by using QFD

Quality has become an important factor in global competition for many reasons. Intensive global competition and the demand for better quality by customers has led organizations to realize the benefits of providing quality products and services in order to successfully compete and survive. Higher education institutions are one example of these organisations. Higher education institutions work in an intensive competitive environment worldwide driven by increasing demands for learning by local and international students. As a result, the managers of these sectors have realized that improving the quality of services is important for achieving customer satisfaction which can help survival in an internationally competitive market. To do this, it is necessary for organizations to know their customers and identify their requirements. To this end, many higher education institutions have adopted principles of total quality management (TQM) to improve their education quality which leads to better performance through involvement of every department to achieve excellence in business. This chapter considers the importance of measuring quality in order to assist universities to proactively manage the design and improvement of the social and academic experiences of postgraduate international students, and plan management decision-making processes to deliver high-quality services in a globalized business of provision of higher education. Higher education institutions must operate effectively and efficiently and be able to deliver quality programs, by seeking to better understand the needs of their customers to be competitive in this market space

Factors Influencing ISO 14000 Implementation in Printed Circuit Board Manufacturing Industry in Hong Kong

The worldwide trend of environmentalconcern has been driving manufacturers to strive to implement competitive strategies in environmental management.A study has been carried out to evaluate the initiatives and benefits/costs of implementing ISO 14000 Environmental Management System (EMS) standard (1996) in the Hong Kong PCB (printed circuit board) industry. The study found that manufacturers intended to implement the ISO 14000 based EMS to improve their environmental performance, and sustain their competitive position in the global market place.

An expert product development system for plastic injection moulding parts

published_or_final_versionIndustrial and Manufacturing Systems EngineeringDoctoralDoctor of Philosoph

The use of OWA operator weights for cross-efficiency aggregation

Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). Existing approaches for cross-efficiency evaluation are mainly focused on the calculation of cross-efficiency matrix, but pay little attention to the aggregation of the efficiencies in the cross-efficiency matrix. The most widely used approach is to aggregate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each DMU and view it as the overall performance measurement of the DMU. This paper focuses on the aggregation process of the efficiencies in the cross-efficiency matrix and proposes the use of ordered weighted averaging (OWA) operator weights for cross-efficiency aggregation. The use of OWA operator weights for cross-efficiency aggregation allows the decision maker (DM)'s optimism level towards the best relative efficiencies, characterized by an orness degree, to be taken into consideration in the final overall efficiency assessment and particularly in the selection of the best DMU.Data envelopment analysis Cross-efficiency evaluation OWA operators Efficiency aggregation