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Ranking range models under incomplete attribute weight information in the selected six MADM methods
Author(s) -
Liu Yating,
Tang Huali,
Liang Haiming,
Zhang Hengjie,
Li CongCong,
Dong Yucheng
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12696
Subject(s) - electre , ranking (information retrieval) , computer science , range (aeronautics) , topsis , rank (graph theory) , data mining , complete information , multiple criteria decision analysis , measure (data warehouse) , mathematics , mathematical optimization , artificial intelligence , operations research , materials science , mathematical economics , combinatorics , composite material
Multiple attribute decision making (MADM) is used to rank the alternatives according to evaluation information based on multiple attributes, and many MADM methods have been studied to deal with the MADM problems. In existing MADM methods, when setting different attribute weights, the ranking of alternatives are different. And ranking range can be used to measure a lower bound and an upper bound of rankings of alternatives with the change of the attribute weights. Also, in some real MADM problems, the information on attribute weights may be unknown or partially known, which is called incomplete attribute weight information. Then, this study investigates the ranking range models (RRMs) under incomplete attribute weight information in the selected six MADM methods: Weighted geometric averaging (WGA), Ordered weighted geometric averaging (OWGA), TOPSIS, VIKOR, PROMETHEE and ELECTRE. Particularly, we can construct several 0‐1 mathematical programming models to compute the ranking range of alternatives under incomplete attribute weight information for the selected six MADM methods. Then, two case studies on project investment and Academic Ranking of World Universities (ARWU) are used to justify the validity of the RRMs under incomplete attribute weight information in the selected six MADM methods.