期刊
IEEE ACCESS
卷 7, 期 -, 页码 20-46出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2882508
关键词
Multi-attribute group decision making; intuitionistic uncertain linguistic variable; linguistic scale function; Hamy mean operator; health-care waste management
资金
- Shandong Provincial Natural Science Foundation of China [ZR2017MG007]
- Humanities and Social Sciences Research Project of Ministry of Education of China [17YJA630065]
- Special Funds of Taishan Scholars Project of Shandong Province [ts201511045]
- Project of Shandong Province Higher Educational Science and Technology Program [J16LN25]
- National Natural Science Foundation of China [71771140, 61773192]
How to select an appropriate and effective health-care waste treatment technology (HCW-TT) is an especially important task in the management of health-care waste (HCW), which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the selection of HCW-TT, the expression of evaluation information given by decision makers (DMs) under uncertain decision-making environment and the reflection of interrelationships among multi-attributes are two critical issues. In response, a new MAGDM technique is presented for the selection of HCW-TT based on the intuitionistic uncertain linguistic Hamy mean with linguistic scale functions (LSFs). First, considering the lack of closeness and flexibility of existing operations, some new operations of intuitionistic uncertain linguistic variables (IULVs) are redefined by combining with LSFs. New expected value and accuracy function are also presented to compare IULVs. Then, based on the new operations of IULVs, the intuitionistic uncertain linguistic Hamy mean and its weighted version (IULWHAM) are proposed to aggregate IULVs. The proposed operators can simultaneously model the interrelationship among multi-inputs and handle the semantic translation requirements of different DMs. Meanwhile, several attractive properties and special cases of these two operators are studied and analyzed. Subsequently, a MAGDM method (IUL-MAGDM) is presented based on the proposed IULWHAM. Finally, a numerical example is given to demonstrate the proposed IUL-MAGDM method, and results indicate that the proposed IUL-MAGDM can effectively and flexibly handle the selection of HCW management by comparing with other IUL-MAGDM methods.
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