Xiao-Dong Lai

Bio: Xiao-Dong Lai is an academic researcher from Jiangxi University of Finance and Economics. The author has contributed to research in topics: Multiple-criteria decision analysis & Decision matrix. The author has an hindex of 1, co-authored 1 publications receiving 126 citations.

An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information

Abstract: A hybrid group decision making approach is proposed for material selection.Uncertain and vague information is handled by interval-valued intuitionistic fuzzy sets.A maximizing optimization model is established for determining criteria weights.An extended group decision making method is used to rank alternative materials.The applicability and effectiveness are illustrated with two application examples. In engineering design, selecting the most suitable material for a particular product is a typical multiple criteria decision making (MCDM) problem, which generally involves several feasible alternatives and conflicting criteria. In this paper, we aim to propose a novel approach based on interval-valued intuitionistic fuzzy sets (IVIFSs) and multi-attributive border approximation area comparison (MABAC) for handling material selection problems with incomplete weight information. First, individual evaluations of experts concerning each alternative are aggregated to construct the group interval-valued intuitionistic fuzzy (IVIF) decision matrix. Consider the situation where the criteria weight information is partially known, a linear programming model is established for determining the criteria weights. Then, an extended MABAC method within the IVIF environment is developed to rank and select the best material. Finally, two application examples are provided to demonstrate the applicability and effectiveness of the proposed IVIF-MABAC approach. The results suggest that for the automotive instrument panel, polypropylene is the best, for the hip prosthesis, Co-Cr alloys-wrought alloy is the optimal option. Finally, based on the results, comparisons between the IVIF-MABAC and other relevant representative methods are presented. It is observed that the obtained rankings of the alternative materials are good agreement with those derived by the past researchers.

Pythagorean fuzzy set: state of the art and future directions

Abstract: Pythagorean fuzzy set, generalized by Yager, is a new tool to deal with vagueness considering the membership grade $$\mu $$ and non-membership $$ u $$ satisfying the condition $$\mu ^2+ u ^2\le 1$$ . It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Pythagorean fuzzy set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as decision making, aggregation operators, and information measures. Because of such a growth, we present an overview on Pythagorean fuzzy set with aim of offering a clear perspective on the different concepts, tools and trends related to their extension. In particular, we provide two novel algorithms in decision making problems under Pythagorean fuzzy environment. It may be served as a foundation for developing more algorithms in decision making.

Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers

Abstract: This paper presents a new approach for the treatment of uncertainty which is based on interval-valued fuzzy-rough numbers (IVFRN). It is shown that by integrating the rough approach with the traditional fuzzy approach, the subjectivity that exists when defining the borders of fuzzy sets is eliminated. IVFRN make decision making possible using only the internal knowledge in the operative data available to the decision makers. In this way objective uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of IVFRN, the structure of the given data is used. On this basis an original multi-criteria model was developed based on an IVFRN approach. In this multi-criteria model the traditional steps of the BWM (Best–Worst method) and MABAC (Multi-Attributive Border Approximation area Comparison) methods are modified. The model was tested and validated on a study of the optimal selection of fire fighting helicopters. Testing demonstrated that the model based on IVFRN enabled more objective expert evaluation of the criteria in comparison with traditional fuzzy and rough approaches. A sensitivity analysis of the IVFRN BWM-MABAC model was carried out by means of 57 scenarios, the results of which showed a high degree of stability. The results of the IVFRN model were validated by comparing them with the results of the fuzzy and rough extension of the MABAC, COPRAS and VIKOR models.

Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method

Abstract: Materials selection, as a essential link for manufacturing enterprises, has an important driving-force to comprehensively upgrade material properties and service life, especially in building and decoration fields. To qualitatively select the optimal green decoration materials, a hybrid multi-criteria decision making (MCDM) approach integrating analytical hierarchy process (AHP) and grey correlation technique for order performance by similarity to ideal solution (GC-TOPSIS) is proposed. The weights vector of hierarchical index structure, which is established based on interior environmental characteristics, i.e., physiological comfort, psychological satisfaction and interior environmental effect, is determined by AHP. GC-TOPSIS is applied to obtain the final ranking of green decoration materials to select the optimal one. A case study, i.e., 10 kinds of solid woods, is illustrated to validate the proposed method. Additionally, a sensitivity analysis of nine experiments is carried out to monitor the robustness of solution ranking to changes. The results proved that this method furnishes an rational and efficient decision support tool for performance assessment of green decoration materials.

Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function

Abstract: In this paper, we initiate a new axiomatic definition of single-valued neutrosophic distance measure and similarity measure, which is expressed by single-valued neutrosophic number that will reduce the information loss and remain more original information. Meanwhile, a novel score function is proposed. Then, the objective weights of various attributes are determined via gray system theory. Moreover, we present the combined weights, which can show both the subjective information and the objective information. Later, we present three algorithms to deal with multi-attribute decision-making problem based on revised Technique for Order Preference by Similarity to an Ideal Solution, Multi-Attributive Border Approximation area Comparison and similarity measure. Finally, the effectiveness and feasibility of approaches are demonstrated by two numerical examples.