Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)

Develop the Pythagorean fuzzy TODIM approach to multi-criteria decision making.The developed approach can portray the uncertainty and risk simultaneously.Conduct the simulation tests to verify risk attitude's influence on the ranking orders.Provide a case study to show the practicality and effectiveness of the proposed approach.Demonstrate the superiority of our approach by comparing it with the existing ones. Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach, which can characterize the decision makers' psychological behaviours under risk, has been introduced to handle multi-criteria decision making (MCDM) problems. Moreover, Pythagorean fuzzy set is an effective tool for depicting uncertainty of the MCDM problems. In this paper, based on the prospect theory, we first extend the TODIM approach to solve the MCDM problems with Pythagorean fuzzy information. Then, we conduct simulation tests to analyze how the risk attitudes of the decision makers exert the influence on the results of MCDM under uncertainty. Finally, a case study on selecting the governor of Asian Infrastructure Investment Bank is made to show the applicability of the proposed approach.

Pythagorean fuzzy TODIM approach to multi-criteria decision making

An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment

Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching

Nowadays, societal and technological trends demand the management of large scale of decision makers in group decision-making (GDM) contexts. In a large-scale GDM, decision makers often have individual concerns and satisfactions, and also they will use heterogeneous preference representation structures to express their preferences. Meanwhile, it is difficult to set the numerical consensus threshold to judge whether a consensus degree can be acceptable or not in the consensus reaching process in a large-scale GDM. This study proposes a novel consensus reaching model for the heterogeneous large-scale GDM with the individual concerns and satisfactions. In this consensus reaching model, a selection process is proposed to obtain the individual preference vectors, to divide decision makers into different clusters, and to yield the preference vector of the large group. Following this, a consensus measure method that considers the individual concerns on alternatives is defined for measuring the consensus degree, and a linguistic approach is developed to measure the individual and collective satisfactions regarding the consensus degree. Finally, a feedback adjustment process is proposed and utilized to help decision makers adjust their preferences. A practical example and a simulation analysis are presented to demonstrate the validity of the proposed consensus reaching model.

Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions

TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) is a method for solving the multiple attribute decision making (MADM) problem considering decision maker's (DM's) behavior, in which the attribute values are in the format of crisp numbers. It cannot be used to handle hybrid MADM problems with various formats of attribute values. In this paper, an extended TODIM method is proposed to solve the hybrid MADM problem. First, three formats of attribute values (crisp numbers, interval numbers and fuzzy numbers) are expressed in the format of random variables with cumulative distribution functions. Then, according to the concept of the classical TODIM method, the gain and loss matrices concerning each attribute are constructed by calculating the gain and loss of each alternative relative to the others. Further, by calculating the dominance degree of each alternative over the others, the overall value of each alternative can be obtained to rank the alternatives. Finally, two numerical examples are used to illustrate the use of the proposed method.

Extended TODIM method for hybrid multiple attribute decision making problems

A method for large group decision-making based on evaluation information provided by participators from multiple groups

Multiple attribute decision making considering aspiration-levels: A method based on prospect theory

A method for stochastic multiple attribute decision making based on concepts of ideal and anti-ideal points

Nowadays several labeling schemes are proposed to facilitate XML query processing, in which structural relationships among nodes could be quickly determined without accessing original XML documents. However, previous node indexing often encounters some troublesome problems when updates take place, such as a large amount of labels requiring re-labeling, huge space requirements for the updated labels, and inefficient determination of structural relationships. In this paper, we propose a novel labeling scheme that not only completely avoids re-labeling but also improves the performance of determining the structural relationships when XML documents are frequently updated at arbitrary positions. The fundamental difference between our scheme and previous ones is that, the gain in update performance of our labeling scheme does not come at the expense of the label size and the query performance. In particular, instead of completely assigning new labels for inserted nodes, the deleted labels are reused in our labeling scheme for encoding newly inserted nodes, which could effectively lower the label size. Moreover, we formally analyze the effectiveness of our proposed labeling scheme. Finally, we complement our analysis with experimental results on a range of real XML data.

Xiao Zhang

Papers

Extended TODIM method for hybrid multiple attribute decision making problems

A method for large group decision-making based on evaluation information provided by participators from multiple groups

Multiple attribute decision making considering aspiration-levels: A method based on prospect theory

A method for stochastic multiple attribute decision making based on concepts of ideal and anti-ideal points

Dynamic labeling scheme for XML updates