Paper Info
Title
A novel probabilistic linguistic decision-making method with consistency improvement algorithm and DEA cross-efficiency
Abstract
Probabilistic linguistic term set (PLTS) is highly useful for decision-makers (DMs) to describe qualitative and uncertain information in the decision-making process. This paper proposes a novel probabilistic linguistic decision-making method with consistency improvement algorithm and data envelopment analysis (DEA) cross-efficiency. Firstly, we put forward the concept of order consistency of probabilistic linguistic preference relation (PLPR). The order consistency is helpful for DMs to make quick and efficient decision in certain situations. Then, based on the defined multiplicative consistency of PLPR, we develop a consistency improvement algorithm to transform the unacceptable multiplicative consistent PLPRs into the acceptable ones. Furthermore, a DEA model is established to derive the priority weight vector of alternatives from the acceptable multiplicative consistent PLPR. Meanwhile, for the alternatives that have equal priority weights, we use a DEA cross-efficiency model to further differentiate and obtain the final ranking of alternatives. Finally, a numerical example of emergency logistics distribution selection is given to illustrate the effectiveness and applicability of the proposed method.
Year
DOI
Venue
2021
10.1016/j.engappai.2020.104108
Engineering Applications of Artificial Intelligence
Keywords
DocType
Volume
Decision-making method,Probabilistic linguistic preference relation,Multiplicative consistency,Consistency improvement algorithm,Priority weight vector derivation,Data envelopment analysis cross-efficiency
Journal
99
ISSN
Citations 
PageRank 
0952-1976
1
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jinpei Liu163.14
Yun Zheng25911.91
Ligang Zhou3162.36
Feifei Jin441.71
Huayou Chen5204.70