2018 3rd International Conference on Business and Economics
Jul. 25-27, 2018, Chengdu, China
0Days to the Submission Deadline
Important Dates
Submission:Apr. 10, 2018
   (Extended to Jun. 25, 2018)
Conference:Jul. 25-27, 2018
20-40 days after the submission
30-60 days after the final edition
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Contact Information
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Keynote Speakers

The information about the Keynote Speakers of BIZECON2018 is as follows, which will be updated regularly.

Dr. Tien-Chin Wang, Professor
Department of International Business, National Kaohsiung University of Science and Technology, Taiwan.

Biography: Dr. Tien-Chin Wang received his B.A. degree in Physics from Kaohsiung Normal University and MBA degree from National Chiao Tung University in Taiwan in 1984. He received his PhD degree in Management from University of the West of Scotland, UK, in 1998. In 1986, he participated in the opening preparation of I-Shou University (ISU) for four years. Then, from 1990 to 2009, he served as Lecturer, Associate Professor, Professor and Board Trustee Member at this University. Now, Dr. Wang is a Professor in the Department of International Business, National Kaohsiung University of Science and Technology, Taiwan. His research concentrates on decision-making analysis, applications of fuzzy sets theory, rough sets theory, and fuzzy multi criteria decision-making. He has published papers in Information Sciences, OMEGA, European Journal of Operational Research, Expert Systems with Applications, Information Fusion, and other journals. Professor Wang created four new fuzzy methods in the past decade -- Fuzzy Linguistic Preference Relations (Fuzzy LinPreRa), Fuzzy VIKOR, Fuzzy PROMETHEE and Incomplete Linguistic Preference Relations (InLinPreRa). Professor Wang also created more than forty technical analyses of mathematics methods to use in the investment of stock and fund markets.

Topic: Introduction to Fuzzy Linguistic Preference Relations

Abstract: The lack of consistency in decision making can lead to inconsistent conclusions. In fuzzy analytic hierarchy process (fuzzy AHP) method, it is difficult to ensure a consistent pairwise comparison. Furthermore, establishing a pairwise comparison matrix requires n(n-1)/2 judgments for a level with n criteria. The number of comparisons increases as the number of criteria increases. Therefore, the decision-makers judgments will most likely be inconsistent. To alleviate inconsistencies, this study applies fuzzy linguistic preference relations (Fuzzy LinPreRa) to construct a pairwise comparison matrix with additive reciprocal property and consistency. This study reveals that the proposed method yields consistent decision rankings from only n-1 pairwise comparisons. The presented Fuzzy LinPreRa method is an easy and practical way to provide a mechanism for improving consistency in fuzzy AHP method.

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