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Grey Best-Worst Method for Multiple Experts Multiple Criteria Decision Making Under Uncertainty
Volume 31, Issue 2 (2020), pp. 331–357
Pub. online: 17 April 2020      Type: Research Article      Open accessOpen Access
Received
1 June 2019
Accepted
1 March 2020
Published
17 April 2020

Abstract

In practice, the judgments of decision-makers are often uncertain and thus cannot be represented by accurate values. In this study, the opinions of decision-makers are collected based on grey linguistic variables and the data retains the grey nature throughout all the decision-making process. A grey best-worst method (GBWM) is developed for multiple experts multiple criteria decision-making problems that can employ grey linguistic variables as input data to cover uncertainty. An example is solved by the GBWM and then a sensitivity analysis is done to show the robustness of the method. Comparative analyses verify the validity and advantages of the GBWM.

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Biographies

Mahmoudi Amin
pmp.mahmoudi@gmail.com

A. Mahmoudi has received BS and MS degrees in industrial engineering. He is currently a candidate of PhD degree at the Southeast University, Nanjing, China. He is one the founders of Ordinal Priority Approach (OPA) in multiple attribute decision making. He has published several papers in various journals by leading brands like Elsevier, Springer, IEEE, John Wiley, and Emerald Insight. He authored two books entitled A Practical Guide to Microsoft Projects 2013 and Project Time Management (CPM-PERT-CC-ECM), which were published in 2013 and 2016. His areas of interest include multiple criteria decision making, mathematical modelling, project management, fuzzy systems, and grey data analysis.

Mi Xiaomei
mixiaomei2017@163.com

X.M. Mi received the bachelor’s degree in industrial engineering from Sichuan University, China, in 2018, where she is currently also pursuing her master’s degree in industrial engineering. She has published several peer-reviewed papers, many in high quality international journals including Omega and IEEE Transactions on Fuzzy Systems. Her current research interests include group decision making, information fusion, and multiple criteria decision making under uncertainty.

Liao Huchang
liaohuchang@163.com

H.C. Liao is a research fellow at the Business School, Sichuan University, Chengdu, China. He received his PhD degree in management science and engineering from the Shanghai Jiao Tong University, Shanghai, China, in 2015. He has published 3 monographs, 1 chapter, and more than 200 peer-reviewed papers, many in high-quality international journals including European Journal of Operational Research, Omega, IEEE Transactions on Fuzzy Systems, IEEE Transaction on Cybernetics, Information Sciences, Information Fusion, Knowledge-Based Systems, Fuzzy Sets and Systems, Expert Systems with Applications, International Journal of Production Economics, etc. He is a highly cited researcher since 2019. His current research interests include multiple criteria decision analysis under uncertainty, business intelligence and data science, cognitive computing, fuzzy set and systems, healthcare management, evidential reasoning theory with applications in big data analytics, etc. Prof. Liao is the Senior Member of IEEE since 2017. He is the editor-in-chief, associate editor, guest editor or editorial board member for 30 international journals, including Information Fusion (SCI, impact factor: 10.716), Applied Soft Computing (SCI, impact factor: 4.873), Technological and Economic Development of Economy (SSCI, impact factor: 4.344), International Journal of Strategic Property Management (SSCI, impact factor: 1.694), Computers & Industrial Engineering (SCI, impact factor: 3.518), International Journal of Fuzzy Systems (SCI, impact factor: 3.058), Journal of Intelligent & Fuzzy Systems (SCI, impact factor: 1.637) and Mathematical Problems in Engineering (SCI, impact factor: 1.179). Prof. Liao has received numerous honours and awards, including the thousand talents plan for young professionals in Sichuan Province, the candidate of academic and technical leaders in Sichuan Province, the outstanding scientific research achievement award in higher institutions (first class in Natural Science in 2017; second class in Natural Science in 2019), the outstanding scientific science research achievement award in Sichuan Province (second class in Social Science in 2019), and the 2015 endeavour research fellowship award granted by the Australia Government.

Feylizadeh Mohammad Reza
feylizadeh@iaushiraz.ac.ir

M.R. Feylizadeh received his BS, MS and the PhD degrees in industrial engineering in 1996, 2000 and 2009, respectively. He is currently a faculty member of Department of Industrial Engineering (2004∼) and an assistant professor at Department of Industrial Engineering (2009∼). The academic aspects and his research interests are fuzzy sets and systems and its applications in industrial engineering, Z-number, fuzzy multiple criteria decision making (MCDM) and fuzzy data envelopment analysis (DEA). Also, he was the dean of Department of Industrial Engineering (from 2010 for 3 years). He has completed several research projects by grants from universities, and published several international journal papers, and international conference papers with some international researchers along with his direction of research. Also, he had some lectures and a workshop at some international universities in 2017. Also, he has been chosen as the best researcher professor in his college in 2019.

Turskis Zenonas
zenonas.turskis@vgtu.lt

Z. Turskis is professor of technical sciences at the Department of Construction Management and Real Estate, chief research fellow at the Laboratory of Operational Research, Research Institute of Sustainable Construction, Vilnius Gediminas Technical University, Lithuania. Research interests: building technology and management, decision making theory, computer-aided automation in design, expert systems. He is the author of more than 120 research papers, which are referred in the Web of Science database.


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© 2020 Vilnius University
Open access article under the CC BY license.

Keywords
grey best-worst method grey group best-worst method multiple experts multiple criteria decision making grey system theory pairwise comparison

Funding
This study was supported by the National Natural Science Foundation of China (NSFC-71771052 and 71372199).

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