Weight Vector Generation in Multi-Criteria Decision-Making with Basic Uncertain Information

Ya-Qiang Xu, Le-Sheng Jin, Zhen-Song Chen, Ronald R. Yager, Jana Špirková, Martin Kalina and Surajit Borkotokey ()
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Ya-Qiang Xu: Department of Engineering Management, School of Civil Engineering, Wuhan University, Wuhan 430072, China
Le-Sheng Jin: Business School, Nanjing Normal University, Nanjing 210023, China
Zhen-Song Chen: Department of Engineering Management, School of Civil Engineering, Wuhan University, Wuhan 430072, China
Ronald R. Yager: Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, USA
Jana Špirková: Faculty of Economics, Matej Bel University, Tajovského 10, Sk-975 90 Banská Bystrica, Slovakia
Martin Kalina: Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, Sk-810 05 Bratislava, Slovakia

Mathematics, 2022, vol. 10, issue 4, 1-11

Abstract: This paper elaborates the different methods to generate normalized weight vector in multi-criteria decision-making where the given information of both criteria and inputs are uncertain and can be expressed by basic uncertain information. Some general weight allocation paradigms are proposed in view of their convenience in expression. In multi-criteria decision-making, the given importance for each considered criterion may have different extents of uncertainty. Accordingly, we propose some special induced weight-allocation methods. The inputs can be also associated with varying uncertainty extents, and then we develop several induced weight-generation methods for consideration. In addition, we present some suggested and prescriptive weight allocation rules and analyze their reasonability.

Keywords: aggregation operators; basic uncertain information; bipolar preference; multi-criteria decision-making; induced ordered weighted averaging; weight allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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