Journal
SUSTAINABILITY
Volume 12, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/su12062315
Keywords
public service outsourcing; double hierarchy hesitant fuzzy linguistic term set; weight-determination method; ELECTRE II; multi-criteria decision-making
Funding
- Humanities and Social Sciences Research Project of Ministry of Education of China [17YJA630065]
- Shandong Provincial Natural Science Foundation of China [ZR2017MG007]
- Project of Shandong Province Higher Educational Science and Technology Program [J16LN25]
- Special Funds of Taishan Scholars Project of Shandong Province [ts201511045]
- National Science Foundation of China [71771140]
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In present-day society, government public service outsourcing has become an irreversible trend due to the gradually increasing public pursuit of service quality and efficiency. To better meet the needs of the public and effectively improve the quality of service, it has been a crucial issue for government departments to choose the most desirable one from a series of public service outsourcers (PSOs) with distinct characteristics. In this paper, to deal with such decision problems, we propose the improved elimination and choice translating reality (ELECTRE) II method with unknown weight information under the double hierarchy hesitant fuzzy linguistic (DHHFL) environment to accurately and effectively select the best PSO. Firstly, aiming at the shortcomings of the original comparison method for double hierarchy hesitant fuzzy linguistic elements (DHHFLEs), we define the hesitant deviation degree (HDD) for DHHFLEs and, based on this, further propose a new comparison method for comparing DHHFLEs more reasonably. Secondly, inspired by the classical power average (PA) operator proposed by Yager, a new method is introduced to determine the weights of experts with respect to each attribute, based on the support degree between attributes. Afterwards, an improved ELECTRE II method is proposed to address the problem of PSO selection. A numerical case about e-government outsourcer selection is given to demonstrate the enforceability of the method. Finally, comparisons between previous methods and our method are carried out to illustrate the effectiveness and strengths of the proposed method.
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