Open AccessThe Cooperation Partner Selection of Private Sector under Public-Private-Partnership Projects: An Improved Approach under Group Decision-Making Based on FRS, SAW, and Integrated Objective/Subjective Attributes
Author(s) -
Jiawu Gan,
Yingying Zhang,
Yanan Hu,
Sen Liu
Publication year - 2018
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2018/4261026
Subject(s) - bidding , private sector , fuzzy logic , group decision making , consistency (knowledge bases) , selection (genetic algorithm) , defuzzification , public sector , computer science , government (linguistics) , operations research , value (mathematics) , process (computing) , economics , mathematics , fuzzy set , fuzzy number , artificial intelligence , machine learning , microeconomics , psychology , social psychology , linguistics , philosophy , economy , economic growth , operating system
With the intensification of market competition, the choice of partners in the private sector plays a vital role in the success or failure of the government’s public sector in the bidding process. Based on FRS (factor risk scoring system), SAW (simple additive weighted), and the integrated subjective or objective fuzzy group decision-making methods, the private sector partner selection is carried out under the PPP (public-private partnership) model in this paper. First of all, a decision committee is established to select attributes and identify potential partners. Secondly, we determine the important decision of the consistency of the manufacturer, introduce linguistic variables, calculate and collect the fuzzy weighted personal attributes, evaluate the importance of the attributes, and get the comprehensive fuzzy evaluation. Thirdly, we establish fuzzy value matrix based on fuzzy evaluation and get the total fuzzy vector by multiplying respective weighted vectors and fuzzy evaluation matrix, then calculate the defuzzification value of each total score, and choose the alternative with the maximum score. Finally, an example is given to demonstrate the rationality of the algorithm.