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On consistency and priority weights for interval probabilistic linguistic preference relations

Recently, Feng, Xiangqian, Associate Professor, Department of Technology Economics and Management in Business School, published an academic paper in “Fuzzy Optimization and Decision Making” (an international SCI journal).

 

The title of his paper is On consistency and priority weights for interval probabilistic linguistic preference relations. When expressing preferences with different probability weights for different linguistic terms, only partial assessment information is usually to be provided. Then the probability information can be normalized to the interval probability, hence, using interval probabilistic linguistic term sets (IPLTs) is more appropriate. Considering this situation, interval probabilistic linguistic preference relation (IPLPR) is proposed. To measure the consistency of IPLPR, the consistency definition of IPLPR is put forward. For the consistent IPLPR, from which an expected consistent PLPR can be obtained, we can obtain interval weights as the final priorities by using the pairs of linear programming models. We also create the probabilistic linguistic geometric consistency index (PLGCI) of PLPRs to judge whether the IPLPR is satisfactorily consistent. For an unsatisfied consistency IPLPR, the adjusting algorithm is proposed. Probability information is firstly considered to be adjusted. If it is not possible to achieve satisfactory consistency through the adjustment of probability information, then the linguistic terms will be adjusted. In addition to examples of different situations, such as the consistency, satisfactory consistency and consistency improvement, the application example is also given to show the practicability of the proposed methods.

 

Fuzzy Optimization and Decision Making covers all aspects of the theory and practice of fuzzy optimization and decision making in the presence of uncertainty. It examines theoretical, empirical, and experimental work related to fuzzy modeling and associated mathematics, solution methods, and systems. The journal publishes papers in the following areas: modeling, theoretical developments, algorithmic developments, systems development and applications.

 

This journal promotes research and the development of fuzzy technology and soft-computing methodologies to enhance our ability to address complicated optimization and decision making problems involving non-probabilistic uncertainty. It helps foster the understanding, development, and practice of fuzzy technologies for solving economic, engineering, management, and societal problems. The journal provides a forum for authors and readers in the fields of business, economics, engineering, mathematics, management science, operations research, and systems.

 

Xiangqian Feng, Associate Professor at Nanjing Normal University Business School, graduated from Nanjing University of Aeronautics and Astronautics in 2008. In the international journal “Applied soft computing”, “Knowledge-Based Systems”, “International Journal of Fuzzy System Applications”, “International Journal of Machine Learning and Cybernetics” et al. published papers and acted as anonymous reviewers of the journal. The main research areas include decision making, information fusion, multiple criterion decision making, linguistic decision making, technical and economic evaluation.