Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection

Abstract

Low-carbon tourism plays an important role in carbon emission reduction and environmental protection. Low-carbon tourism destination selection often involves multiple conflicting and incommensurate attributes or criteria and can be modelled as a multi-attribute decision-making problem. This paper develops a framework to solve multi-attribute group decision-making problems, where attribute evaluation values are provided as linguistic terms and the attribute weight information is incomplete. In order to obtain a group risk preference captured by a linguistic term set with triangular fuzzy semantic information, a nonlinear programming model is established on the basis of individual risk preferences. We first convert individual linguistic-term-based decision matrices to their respective triangular fuzzy decision matrices, which are then aggregated into a group triangular fuzzy decision matrix. Based on this group decision matrix and the incomplete attribute weight information, a linear program is developed to find an optimal attribute weight vector. A detailed procedure is devised for tackling linguistic multi-attribute group decision making problems. A low-carbon tourism destination selection case study is offered to illustrate how to use the developed group decision-making model in practice.

An OWA Distance-Based, Single-Valued Neutrosophic Linguistic TOPSIS Approach for Green Supplier Evaluation and Selection in Low-Carbon Supply Chains

“…To date, several authors have investigated methods for low-carbon selection in such uncertain situations. Lin and Wang [ 65 ] and Tong and Wang [ 66 ] explored low-carbon supplier selection problems based on the linguistic and IFS MADM approaches, respectively. Liu et al [ 67 ] developed a novel aggregation method for selecting a low-carbon supplier in an intuitionistic linguistic (IL) environment.…”

“…With the help of practitioners and managers in low-carbon supply chain management, three experts are invited to assess and select an appropriated low-carbon supplier as a manufacturer from among four potential suppliers, ( ), in accordance with the following four attributes: low-carbon technology ( ), cost ( ), risk factor ( ) and capacity ( ) (adapted from [ 65 ]). The evaluation provided by the experts against the above-given four attributes is formed into SVNL decision information under the linguistic term set { = extremely poor, = very poor, = poor, = fair, = good, = very good, = extremely good}, as shown in Table 1 , Table 2 and Table 3 .…”

Fuzzy Group Consensus Decision Making and Its Use in Selecting Energy-Saving and Low-Carbon Technology Schemes in Star Hotels

“…In constructing fuzzy-based decision support systems, we need to assign a set of fuzzy numbers corresponding to a linguistic term set [ 26 , 27 ]. Different triangular fuzzy assignment models (also called triangular fuzzy scales) have been proposed in the literature [ 28 , 29 , 30 , 31 ]. Recently, Centobelli et al [ 32 , 33 ] used trapezoidal fuzzy numbers to assign two linguistic term sets respectively characterizing formalization and sharing degrees of knowledge management tools and knowledge management practices.…”

Special Issue “Decision Models in Green Growth and Sustainable Development”

“…Considering the hesitancy of decision-making problems and the priority of evaluation criteria, Qi et al [ 50 ] adopted a fuzzy GDM method to deal with the evaluation of complex emergency response solutions. Lin and Wang [ 51 ] constructed a linguistic multi-attribute GDM linear model based on risk preference with an application to the selection of low-carbon tourist destinations. Based on multiplicative and additive consistency, Gong and Wang [ 52 ] studied the measurement of individual consistency and group consensus of fuzzy preference relations.…”