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Purpose – The purpose of this paper is to describe and implements an analytic hierarchy process (AHP)-QFD model for selecting the best location from an organization point of view which picks the site with the best opportunity requirements. Integration of AHP-QFD process gives us a new approach to assist organizations through observing various factors and selecting the best location among different alternatives. This approach uses AHP method to match the preferences required by decision makers and these preferences are applied to the characteristics of QFD. The model fundamental requirement are perfect potential locales and the areas are contrasted and both quantitative and qualitative elements to permit directors to join managerial experience and judgment in the answer process. The AHP-QFD model is also applied on a case study to illustrate the solution process. Design/methodology/approach – The integration of AHP and QFD is used to analyze available options and select the best alternative. This can be done by ranking each criterion through a pairwise comparison. Given collected data, the QFD approach is used to find the capability of each criterion. Findings – Integration of AHP-QFD is used to select the best alternative in facility location. This integrated approach can be best used in dealing with facility location problems. Originality/value – The developed AHP-QFD model in facility location problems, facilitates the inclusion of market criteria and decision maker opinion into the traditional cost function, which has been mainly distance base in the literature.

Effective teaching concerns students' learning styles and its diversity. An adopted teaching strategy must be then aligned with students' learning traits. In this paper, to provide an effective in-classroom learning environment for most students, we propose to develop appropriate instructional presentation methods to match diversity of learning styles. We demonstrate how to align in-class instructions, such as quizzes and other in-class learning activities, with students' learning styles. Our discussion, which primarily focuses on quantitative topics, relies on research in education and applied psychology, and our teaching experience. Here, we utilize the Fleming's VARK learning style model. We then address some shortcomings of identifying students' learning styles through a generic questionnaire, and propose a scheme to dynamically infer them. This automatic student modeling approach can be easily adopted in distance education.

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