Priority Weights Of Alternatives Research Articles

A new group decision-making method with incomplete intuitionistic multiplicative preference relations

PurposeConsistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.Design/methodology/approachA mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.FindingsThe case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.Originality/valueFirst, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

A novel group decision making method for interval-valued pythagorean fuzzy preference relations

Interval-valued Pythagorean fuzzy preference relation (IVPFPR) plays an important role in representing the complex and uncertain information. The application of IVPFPRs gives better solutions in group decision making (GDM). In this paper, we investigate a new method to solve GDM problems with IVPFPRs. Firstly, novel multiplicative consistency and consensus measures are proposed. Subsequently, the procedure for improving consistency and consensus levels are put forward to ensure that every individual IVPFPR is of acceptable multiplicative consistency and consensus simultaneously. In the context of minimizing the deviations between the individual and collective IVPFPRs, the objective experts’ weights are decided according to the optimization model and the aggregated IVPFPR is derived. Afterwards, a programming model is built to derive the normalized Pythagorean fuzzy priority weights, then the priority weights of alternatives are identified as well. An algorithm for GDM method with IVPFPRs is completed. Finally, an example is cited and comparative analyses with previous approaches are conducted to illustrate the applicability and effectiveness of the proposed method.

Group decision making with incomplete interval‐valued q‐rung orthopair fuzzy preference relations

In this paper, we focus on group decision making (GDM) with incomplete interval-valued q-rung orthopair fuzzy preference relations (IV q-ROFPRs). On the basis of interval-valued q-rung orthopair fuzzy set (IV q-ROFS), the novel concepts of IV q-ROFPR, incomplete IV q-ROFPR, additive consistent IV q-ROFPR, and acceptably additive consistent IV q-ROFPR are proposed. Subsequently, two optimization models are established for deriving the complete IV q-ROFPRs and acceptably additive consistent IV q-ROFPRs. Inspired by the Markov model, the weight-generating method is designed to obtain the weights of decision makers. For the purpose of improving the consensus level, a modification process is constructed under the consideration of minimizing the deviations between the adjusted IV q-ROFPRs and the original ones. Afterward, a goal programming model is constructed to derive the normalized q-rung orthopair fuzzy ( q-ROF) priority weights. The interval-valued q-rung orthopair fuzzy (IV q-ROF) priority weights of alternatives are further determined. Thus, an algorithm for the GDM method with IV q-ROFPRs is completed. Finally, the applications to examples and comparisons with previous approaches are conducted to illustrate the validity and effectiveness of the proposed method.

Evaluation of Continuous Improvement Techniques using Hybrid MCDM Technique under Fuzzy Environment

Abstract In this paper, an attempt is made to evaluate the continuous improvement techniques using hybrid MCDM techniques under fuzzy environment. The objective of the paper is to demonstrate the selection of best continuous improvement technique using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and VIKOR. Lean Continuous, 5S, Kaizen, Poka-Yoke, Jidoka (Automation), and Kanban (Pull System) are considered as six alternative continuous improvement techniques. Number of Defects, Level of waste elimination, Cost, effective use of resources, Lead time between order and product delivery, and Quality of product are considered as six main evaluation criteria. Under each main criterion, two sub criteria are considered. The weights of priority of main criteria as well as the sub-criteria are computed using Fuzzy-AHP whereas the evaluation ratings/ weights of priority of alternatives with respect to each sub criteria are determined using Fuzzy-VIKOR. Finally, by using the weights of priority of main criteria and the alternatives’ evaluation ratings, the overall priority weights of alternatives are determined. Based on these overall priority weights, the alternatives are ranked. From the findings of the research, it is concluded that the results are more practical and realistic.

D-AHP method with different credibility of information

Multi-criteria decision making (MCDM) has attracted wide interest due to its extensive applications in practice. In our previous study, a method called D-AHP (AHP method extended by D numbers preference relation) was proposed to study the MCDM problems based on a D numbers extended fuzzy preference relation, and a solution for the D-AHP method has been given to obtain the weights and ranking of alternatives from the decision data, in which the results obtained by using the D-AHP method are influenced by the credibility of information. However, in previous study the impact of information’s credibility on the results is not sufficiently investigated, which becomes an unsolved issue in the D-AHP. In this paper, we focus on the credibility of information within the D-AHP method and study its impact on the results of a MCDM problem. Information with different credibilities including high, medium and low, respectively, is taken into consideration. The results show that the credibility of information in the D-AHP method slightly impacts the ranking of alternatives, but the priority weights of alternatives are influenced in a relatively obvious extent.

A novel method for group decision making with interval-valued Atanassov intuitionistic fuzzy preference relations

This paper investigates the group decision making (GDM) problems with interval-valued Atanassov intuitionistic fuzzy preference relations (IV-AIFPRs) and develops a novel method for solving such problems. A new consistency index of an Atanassov intuitionistic fuzzy preference relation (AIFPR) is introduced to judge the consistency of an AIFPR and then a convergent iterative Algorithm I is designed to repair the consistency of an AIFPR with unacceptable consistency. Subsequently, the consistency and acceptable consistency of an IV-AIFPR are defined through separating an IV-AIFPR into two AIFPRs. Based on Algorithm I, a new iterative Algorithm II is devised to repair the consistency of an IV-AIFPR with unacceptable consistency. Afterwards, to determine decision makers’ (DMs’) weights objectively, an optimization model is established by minimizing the deviations between each individual IV-AIFPR and the collective one. This model is skillfully transformed into a linear goal program to resolve sufficiently considering different principles of decision making. A linear program is built to derive interval-valued Atanassov intuitionistic fuzzy (IVAIF) priority weights of alternatives. Then, a TOPSIS (technique for order performance by similarity to an ideal solution) based approach is proposed to rank such IVAIF priority weights. Thereby, a method for GDM with IV-AIFPRs is put forward. At length, a practical example of a virtual enterprise partner selection is provided to illustrate the feasibility and validity of the proposed method.

Multi-criteria approach to assess stakeholders preferences for selection of biodiesel feedstock in Vietnam

Biodiesel has been proposed as an alternative to petroleum diesel fuel in Vietnam but Vietnam is a net importer of vegetable oil. Decision-makers face challenges in crafting a long-term policy for sustainable development of biodiesel that balances social, technological, economic and environmental aspects. The analytic hierarchy process (AHP) was applied in this study to find the most appropriate feedstock for biodiesel production in Vietnam among three possible options: jatropha oil, fish fat and waste cooking oil. The judgments of different Vietnamese stakeholders, such as academics, heads of biodiesel projects, managers in Petrovietnam Corporation, and engineers were incorporated. The stakeholders showed different preferences as reflected in the priority weights of criteria and alternatives. The priority weights of alternatives under the judgments of multiple stakeholders indicate that waste cooking oil is the most preferred feedstock to produce biodiesel in Vietnam followed by jatropha oil and fish fat.

An approach to group decision making with heterogeneous incomplete uncertain preference relations

For practical group decision making problems, decision makers tend to provide heterogeneous uncertain preference relations due to the uncertainty of the decision environment and the difference of cultures and education backgrounds. Sometimes, decision makers may not have an in-depth knowledge of the problem to be solved and provide incomplete preference relations. In this paper, we focus on group decision making (GDM) problems with heterogeneous incomplete uncertain preference relations, including uncertain multiplicative preference relations, uncertain fuzzy preference relations, uncertain linguistic preference relations and intuitionistic fuzzy preference relations. To deal with such GDM problems, a decision analysis method is proposed. Based on the multiplicative consistency of uncertain preference relations, a bi-objective optimization model which aims to maximize both the group consensus and the individual consistency of each decision maker is established. By solving the optimization model, the priority weights of alternatives can be obtained. Finally, some illustrative examples are used to show the feasibility and effectiveness of the proposed method.