Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making
| Autor: | Samarjit Kar, Sanjay Kumar Tyagi, Kattur Soundarapandian Ravichandran, M. Ifjaz Ahmed, Raghunathan Krishankumar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
analytic hierarchy process
consistency measure group decision-making probabilistic linguistic preference relation 0209 industrial biotechnology Physics and Astronomy (miscellaneous) Computer science General Mathematics Analytic hierarchy process Context (language use) 02 engineering and technology Machine learning computer.software_genre Consistency (database systems) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) business.industry lcsh:Mathematics Probabilistic logic Missing data lcsh:QA1-939 Preference Group decision-making Ranking Chemistry (miscellaneous) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Symmetry, Vol 11, Iss 1, p 2 (2018) Symmetry; Volume 11; Issue 1; Pages: 2 |
| ISSN: | 2073-8994 |
| Popis: | With trending competition in decision-making process, linguistic decision-making is gaining attractive attention. Previous studies on linguistic decision-making have neglected the occurring probability (relative importance) of each linguistic term which causes unreasonable ranking of objects. Further, decision-makers’ (DMs) often face difficulties in providing apt preference information for evaluation. Motivated by these challenges, in this paper, we set our proposal on probabilistic linguistic preference relation (PLPR)-based decision framework. The framework consists of two phases viz., (a) missing value entry phase and (b) ranking phase. In phase (a), the missing values of PLPR are filled using a newly proposed automatic procedure and consistency of PLPR is ensured using a consistency check and repair mechanism. Following this, in phase (b), objects are ranked using newly proposed analytic hierarchy process (AHP) method under PLPR context. The practicality of the proposal is validated by using two numerical examples viz., green supplier selection problem for healthcare and the automobile industry. Finally, the strength and weakness of the proposal are discussed by comparing with similar methods. |
| Databáze: | OpenAIRE |
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