Journal article
A Multiple Criteria Decision-Making Method With Heterogeneous Linguistic Expressions
IEEE transactions on engineering management
18 May 2021
Abstract
Due to the personalized and diversified expression habits, the evaluation information in multiple criteria decision-making (MCDM) problems, especially those with multiple experts, may appear in heterogeneous forms. Converting heterogeneous expressions to a single representation would lead to information loss. To solve this challenge, this article aims to address the MCDM problems with heterogeneous linguistic expressions by unifying the operations of heterogeneous linguistic representations. To do so, we propose to use expectation values and entropy measures to describe the meaning of linguistic evaluation information. The expectation functions of ten kinds of linguistic representations are respectively defined based on the semantics of linguistic terms. The entropy measures of these representations are developed to reflect the inherent uncertainty of evaluations. Afterward, a computation model for heterogeneous linguistic representations is presented based on their expectation values and entropy. On this basis, an MCDM framework with personalized heterogeneous linguistic information is constructed. A numerical example about selecting the best green logistics for a business-to-customer e-commerce enterprise shows the advantages of the proposed method in modeling personalized linguistic evaluations and retaining uncertain information in the computation process.
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Details
- Title
- A Multiple Criteria Decision-Making Method With Heterogeneous Linguistic Expressions
- Creators
- Xingli Wu - Sichuan UniversityHuchang Liao - Sichuan UniversityBenjamin Lev - Drexel UniversityEdmundas K Zavadskas - Vilnius Gediminas Technical University
- Publication Details
- IEEE transactions on engineering management
- Publisher
- IEEE
- Grant note
- 71971145 71771156 / National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000732673300001
- Scopus ID
- 2-s2.0-85106738508
- Other Identifier
- 991019169548504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Business
- Engineering, Industrial
- Management