Multi-criteria Group Decision-making Approach Research Articles

Pythagorean fuzzy combinative distance-based assessment with pure linguistic information and its application to financial strategies of multi-national companies

This article addresses the issue of selecting Financial Strategies in Multi-National companies (F.S.M.). The F.S.M. typically has to consider multiple factors involving multiple stakeholders and, hence, can be handled by applying an appropriate Multi-Criteria Group Decision-Making (M.C.G.D.M.) approach. To address this issue, we develop an M.C.G.D.M. framework to tackle the F.S.M. problem. To handle inherent uncertainty in business decisions as reflected by linguistic reasoning, we embark on constructing a Linguistic Pythagorean Fuzzy (L.P.F.) M.C.G.D.M. framework that is capable of tackling both uncertain decision information and linguistic variables. The proposed approach extends the combinative distance-based assessment (C.O.D.A.S.) method into the L.P.F. environment, and processes decision input expressed as Pythagorean fuzzy sets (P.F.S.) and pure linguistic variables (rather than converting linguistic information into fuzzy numbers). The developed L.P.F.-C.O.D.A.S. technique aggregates the L.P.F. information and is applied to the F.S.M. problem with uncertain linguistic information. A comparative analysis is carried out to compare the results obtained from the proposed L.P.F.-C.O.D.A.S. approach with those from other extensions of C.O.D.A.S. Furthermore, a sensitivity analysis is conducted to check the impact of changes in a distance threshold parameter on the ranking results.

A novel q-rung orthopair fuzzy TODIM approach for multi-criteria group decision making based on Shapley value and relative entropy

In the real multi-criteria group decision making (MCGDM) problems, there will be an interactive relationship among different decision makers (DMs). To identify the overall influence, we define the Shapley value as the DM’s weight. Entropy is a measure which makes it better than similarity measures to recognize a group decision making problem. Since we propose a relative entropy to measure the difference between two systems, which improves the accuracy of the distance measure.In this paper, a MCGDM approach named as TODIM is presented under q-rung orthopair fuzzy information.The proposed TODIM approach is developed for correlative MCGDM problems, in which the weights of the DMs are calculated in terms of Shapley values and the dominance matrices are evaluated based on relative entropy measure with q-rung orthopair fuzzy information.Furthermore, the efficacy of the proposed Gq-ROFWA operator and the novel TODIM is demonstrated through a selection problem of modern enterprises risk investment. A comparative analysis with existing methods is presented to validate the efficiency of the approach.

A Multi-Criteria Group Decision-Making Approach Based on Improved BWM and MULTIMOORA with Normal Wiggly Hesitant Fuzzy Information

Multi-criteria group decision-making (MCGDM) problems are widespread in real life. However, most existing methods, such as hesitant fuzzy set (HFS), hesitant fuzzy linguistic term set (HFLTS) and inter-valued hesitant fuzzy set (IVHFS) only consider the original evaluation data provided by experts but fail to dig the concealed valuable information. The normal wiggly hesitant fuzzy set (NWHFS) is a useful technique to depict experts’ complex evaluation information toward MCGDM issues. In this paper, on the basis of the score function of NWHFS, we propose the linear best-worst method (BWM)-based weight-determining models with normal wiggly hesitant fuzzy (NWHF) information to compute the optimal weights of experts and criteria. In addition, we present some novel distance measures between NWHFSs and discuss their properties. After fusing the individual evaluation matrices, the NWHF-ranking position method is put forward to develop the group MULTIMOORA method, which can be determined by the final decision results. Moreover, we investigate the Spring Festival travel rush phenomenon deeply and apply our methodology to solve the train selection problem during the Spring Festival period. Finally, the applicability and superiority of the proposed approach is demonstrated by comparing with traditional methods based on two aggregation operators of NWHFSs.

A heterogeneous QUALIFLEX method with criteria interaction for multi-criteria group decision making

Green supplier selection complexity involves such problems which usually need to offer the management of dependencies among criteria in the selection process. Meanwhile, heterogeneous contexts are adopted based on different criteria, which drives to propose a novel multi-criteria group decision making (MCGDM) method. To solve this problem, this paper proposes a new MCGDM approach for heterogeneous information and dependent criteria based on the QUALItative FLEXible (QUALIFLEX) method and Choquet integral. For managing dependency among criteria, a new graphical representation of criteria interaction is presented and the identification of fuzzy measure is then obtained considering group consensus reaching. The multi-criteria heterogeneous QUALIFLEX method with regard to dependency of criteria is finally applied to a green supplier selection problem and a comparative analysis is performed to illustrate its feasibility and effectiveness.

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

AbstractProbabilistic interval‐valued hesitant fuzzy sets (PIV‐HFSs) are suitable for aggregating information from different groups because the probabilistic information of all the groups can be included by using interval values. Moreover, decision makers (DMs) prefer to use interval values to provide evaluation information. Furthermore, the traditional multi‐criteria group decision‐making (MCGDM) approach has some limitations, such as obtaining the DMs' weights with inappropriate methods and neglecting the interactions amongst the criteria and the psychological characteristics of DMs. Motivated by these research background, the main contents of this study are as follows. First, PIV‐HFSs are proposed, and the convex combination operation is extended into PIV‐HFSs. Second, a hybrid MCGDM approach with PIV‐HFSs is suggested that is based on the maximizing deviation method, fuzzy analytic network process (FANP) and TODIM (an acronym in Portuguese for interactive and multi‐criteria decision‐making model). Third, an evaluation case of health management centres based on the service‐specific failure mode and effect analysis (FMEA) is considered. The results show that the most crucial secondary factor is frequency (0.35775) and that the most serious failure mode is the inaccurate check‐in. The results demonstrate that the proposed model can evaluate service quality effectively and that it performs better than other methods.

A new multi-criteria group decision-making approach based on q-rung orthopair fuzzy interaction Hamy mean operators

The recently proposed q-rung orthopair fuzzy set (q-ROFS) is a powerful and effective tool to describe uncertainty and vagueness, and Hamy mean (HM) has a significant advantage of capturing the interrelationship among aggregated arguments. In order to take full advantage of q-ROFS and HM, and consider the interactions between membership and non-membership degrees at the same time, in this paper, we propose a family of q-rung orthopair fuzzy Hamy mean operators based on interaction operations. First, we define interaction operational rules for q-rung orthopair fuzzy numbers. Based on the new operational rules, q-rung orthopair fuzzy interaction HM and q-rung orthopair fuzzy interaction weighted HM operators are proposed. Further, we propose a dual Hamy mean (DHM) operator and extend it to accommodate q-rung orthopair fuzzy environment. Based on interaction operational rules and DHM, q-rung orthopair fuzzy interaction DHM operator and its weighted form are also developed. Then, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, a numerical instance, as well as some comparative analyses, is provided to illustrate the validity and advantages of the new approach.

A systematic fuzzy multi-criteria group decision-making approach for alternatives evaluation

Given that the values of the criteria in uncertain multi-criteria group decision-making (MGDM) problems take the form of fuzzy linguistic variables, this paper proposes a model based on hesitant fuzzy linguistic term sets (HFLTSs), named MGDM-HFLTS, to estimate investment alternatives for angel investors. To meet the challenges of complexity, lack of information and time pressure among several possible values in MGDM, the HFLTSs are introduced and revised. The HFLTSs, which are convenient and sufficiently flexible to reflect the decision-makers’ preferences, are introduced to represent the hesitation or doubt originating from systematic comparisons of the assessment values of alternatives for each criterion during both preference elicitation and alternative evaluation phases. Then, context-free grammar is revised for computing with words to enhance and extend the applicability of HFLTSs according to a set of various membership degrees over which decision-makers hesitate when eliciting their preferences over alternatives. Subsequently, the most satisfactory alternative(s) is/are determined by the outranking relationship approach to integrate the degree of preference and entropy information. In addition, studies of evaluation criteria and their weights in angel investment decision-making are investigated. An illustrative example of an angel investment implemented by the proposed MGDM-HFLTS and its corresponding algorithm confirms the effectiveness and practicability of the proposed method.

Benchmarking the performance of dairy products in Chinese dairy industry

PurposeThe purpose of this paper is to present a review of the main factors that are important to consumers of dairy products and develop a new product performance index for evaluating and benchmarking the performance of dairy products. This helps dairy product companies understand their dairy products’ overall performance level, relative to other dairy products in the market.Design/methodology/approachA new product performance index is developed for evaluating and benchmarking the performance of dairy products with respect to multiple criteria. Seven important criteria are identified for evaluating and benchmarking the performance of dairy products. To deal with the subjective assessments of qualitative performance measures, linguistic terms approximated by fuzzy numbers are used. Based on the concept of the degree of dominance, a fuzzy multicriteria group decision-making approach is developed to obtain a product performance index for each dairy product.FindingsThe proposed multicriteria group decision-making approach is found to be useful and effective in evaluating and benchmarking the performance of dairy products. The approach is capable of adequately considering the presence of multiple decision makers, the multi-dimensional nature of the performance evaluation problem, and modeling the subjectiveness and imprecision of the performance evaluation process.Research limitations/implicationsThe outcome of the multicriteria group decision-making approach is dependent on the subjective inputs provided by the decision makers.Practical implicationsThis product performance index will provide useful insights for companies understand their strengths and weaknesses in terms of their products’ performance criteria, and identify relevant areas for continuous improvement. This product performance index is also applicable for dealing with the general multicriteria decision-making problems.Social implicationsThe proposed multicriteria group decision-making approach can be used as a decision-making tool for providing various decision makers in dairy product companies and general consumers with useful information regarding the performance of different dairy products.Originality/valueThis paper highlights the important factors for evaluating and benchmarking dairy products and develops a new product performance index for evaluating and benchmarking the performance of dairy products in China.

Algorithm for Probabilistic Dual Hesitant Fuzzy Multi-Criteria Decision-Making Based on Aggregation Operators with New Distance Measures

Probabilistic dual hesitant fuzzy set (PDHFS) is an enhanced version of a dual hesitant fuzzy set (DHFS) in which each membership and non-membership hesitant value is considered along with its occurrence probability. These assigned probabilities give more details about the level of agreeness or disagreeness. By emphasizing the advantages of the PDHFS and the aggregation operators, in this manuscript, we have proposed several weighted and ordered weighted averaging and geometric aggregation operators by using Einstein norm operations, where the preferences related to each object is taken in terms of probabilistic dual hesitant fuzzy elements. Several desirable properties and relations are also investigated in details. Also, we have proposed two distance measures and its based maximum deviation method to compute the weight vector of the different criteria. Finally, a multi-criteria group decision-making approach is constructed based on proposed operators and the presented algorithm is explained with the help of the numerical example. The reliability of the presented decision-making method is explored with the help of testing criteria and by comparing the results of the example with several prevailing studies.

A Linguistic Neutrosophic Multi-criteria Group Decision-Making Approach with EDAS Method

This study develops an approach that incorporates power aggregation operators with the evaluation based on distance from average solution (EDAS) method under linguistic neutrosophic situations to solve fuzzy multi-criteria group decision-making problems. Firstly, the existing operational laws and comparison methods of linguistic neutrosophic numbers (LNNs) are analysed. Secondly, the distance measurement between two LNNs is defined. Thirdly, the power-weighted averaging operator and the power-weighted geometric operator with LNNs are developed to support the decision makers’ evaluation information. The models to derive the criteria weights are also constructed based on the proposed distance measurements. Finally, the EDAS method is extended to resolve group decision-making problems in the linguistic neutrosophic environment. An illustrative example of the property management company selection is given to verify the effectiveness and practicality of the proposed approach.

Signed distance-based consensus in multi-criteria group decision-making with multi-granular hesitant unbalanced linguistic information

Abstract This study aims to deal with hesitant fuzzy linguistic multi-criteria group decision-making (MCGDM) problems with multi-granular unbalanced linguistic term sets. Firstly, a signed distance measure is developed as the support tool for hesitant fuzzy linguistic term sets. This measure is based on the ordinal semantics of linguistic terms and the possibility distribution method. In this manner, the signed distance measure can release the strict constraint of symmetric and uniform linguistic term sets in qualitative decision-making. Secondly, a signed distance-based transformation function is proposed to unify the multi-granular hesitant unbalanced linguistic assessments. Moreover, comprehensive consensus measures on three levels are proposed to measure the consensus degree. These measures simultaneously consider the consensus degree between individual and collective preference and between experts. Subsequently, a consensus reaching algorithm is designed and incorporated into the proposed MCGDM approach. Lastly, an illustrative example, followed by an in-depth comparative analysis and discussion is presented to demonstrate the proposed approach. Results show that the proposed approach can flexibly and effectively tackle MCGDM problems with multi-granular hesitant unbalanced linguistic information.

A Hybrid Fuzzy Analytic Network Process (FANP) and Data Envelopment Analysis (DEA) Approach for Supplier Evaluation and Selection in the Rice Supply Chain

In the market economy, competition is typically due to the difficulty in selecting the most suitable supplier, one that is capable to help a business to develop a profit to the highest value threshold and capable to meet sustainable development features. In addition, this research discusses a wide range of consequences from choosing an effective supplier, including reducing production cost, improving product quality, delivering the product on time, and responding flexibly to customer requirements. Therefore, the activities noted above are able to increase an enterprise’s competitiveness. It can be seen that selecting a supplier is complex in that decision-makers must have an understanding of the qualitative and quantitative features for assessing the symmetrical impact of the criteria to reach the most accurate result. In this research, the multi-criteria group decision-making (MCGDM) approach was proposed to solve supplier selection problems. The authors collected data from 25 potential suppliers, and the four main criteria within contain 15 sub-criteria to define the most effective supplier, which has viewed factors, including financial efficiency guarantee, quality of materials, ability to deliver on time, and the conditioned response to the environment to improve the efficiency of the industry supply chain. Initially, fuzzy analytic network process (ANP) is used to evaluate and rank these criteria, which are able to be utilized to clarify important criteria that directly affect the profitability of the business. Subsequently, data envelopment analysis (DEA) models, including the Charnes Cooper Rhodes model (CCR model), Banker Charnes Cooper model (BCC model), and slacks-based measure model (SBM model), were proposed to rank suppliers. The result of the model has proposed 7/25 suppliers, which have a condition response to the enterprises’ supply requirements.

Some Normal Intuitionistic Fuzzy Heronian Mean Operators Using Hamacher Operation and Their Application

Hamacher operation is a generalization of the algebraic and Einstein operation and expresses a family of binary operation in the unit interval [0,1]. Heronian mean can deal with correlations of different criteria or input arguments and does not bring out repeated calculation. The normal intuitionistic fuzzy numbers (NIFNs) can depict normal distribution information in practical decision making. A decision-making problem was researched under the NIFN environment in this study, and a new multi-criteria group decision-making (MCGDM) approach is herein introduced on the basis of Hamacher operation. Firstly, according to Hamacher operation, some operational laws of NIFNs are presented. Secondly, it is noted that Heronian mean not only takes into account mutuality between the attribute values once, but also considers the correlation between input argument and itself. Therefore, in order to aggregate NIFN information, we developed some operators and studied their properties. These operators include Hamacher Heronian mean (NIFHHM), Hamacher weighted Heronian mean (NIFHWHM), Hamacher geometric Heronian mean (NIFHGHM), and Hamacher weighted geometric Heronian mean (NIFHWGHM). Furthermore, we applied the proposed operators to the MCGDM problem and developed a new MCGDM approach. The characteristics of this new approach are that: (1) it is suitable for making a decision under the NIFN environment and it is more reasonable for aggregating the normal distribution data; (2) it utilizes Hamacher operation to provide an effective and powerful MCGDM algorithm and to make more reliable and more flexible decisions under the NIFN circumstance; (3) it uses the Heronian mean operator to deal with interrelations between the attributes or input arguments, and it does not bring about repeated calculation. Therefore, the proposed method can describe the interaction of the different criteria or input arguments and offer some reasonable and reliable MCGDM aggregation operators, which can open avenues for decision making and broaden perspectives of the decision experts. Lastly, an application is given for showing the effectiveness and feasibility of the approach presented in this paper.

Multiple Criteria Group Decision-Making Considering Symmetry with Regards to the Positive and Negative Ideal Solutions via the Pythagorean Normal Cloud Model for Application to Economic Decisions

Pythagorean fuzzy sets are highly appealing in dealing with uncertainty as they allow for greater flexibility in regards to the membership and non-membership degrees by extending the set of possible values. In this paper, we propose a multi-criteria group decision-making approach based on the Pythagorean normal cloud. Some cloud aggregation operators are presented in this paper to facilitate the appraisal of the underlying utilities of the alternatives under consideration. The concept and properties of the Pythagorean normal cloud and its backward generation algorithm, aggregation operators and distance measurement are outlined. The proposed approach resembles the TOPSIS technique, which, indeed, considers the symmetry of the distances to the positive and negative ideal solutions. Furthermore, an example from e-commerce is presented to demonstrate and validate the proposed decision-making approach. Finally, the comparative analysis is implemented to check the robustness of the results when the aggregation rules are changed.

A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation

Selection of appropriate subcontractors for outsourcing is very important for the success of construction projects. This can improve the overall quality of projects and promote the qualification and reputation of the main contractors. The evaluation of subcontractors can be made by some experts or decision-makers with respect to some criteria. If this process is done in different time periods, it can be defined as a dynamic multi-criteria group decision-making (MCGDM) problem. In this study, we propose a new fuzzy dynamic MCGDM approach based on the EDAS (Evaluation based on Distance from Average Solution) method for subcontractor evaluation. In the procedure of the proposed approach, the sets of alternatives, criteria and decision-makers can be changed at different time periods. Also, the proposed approach gives more weight to newer decision information for aggregating the overall performance of alternatives. A numerical example is used to illustrate the proposed approach and show the application of it in subcontractor evaluation. The results demonstrate that the proposed approach is efficient and useful in real-world decision-making problems.

Evaluating two-stage holistic production systems using novel CSDEA approach

PurposeThe purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences.Design/methodology/approachThe authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences.FindingsThe authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set.Originality/valueThe authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

NEW GROUP DECISION-MAKING ARCAS APPROACH BASED ON THE INTEGRATION OF THE SWARA AND THE ARAS METHODS ADAPTED FOR NEGOTIATIONS

Groups are generally considered to be more effective as compared to single individuals. The practical implementation of Operation Research methods in group negotiations needs simple contexts and clear cause-and-effect relationships easily discernible by everyone. This paper proposes a multi-criteria group decision-making approach allowing decision makers/experts involved in a negotiation process to better express and defend their preferences in the selection of the best alternative. In the proposed approach, the most appropriate alternative is the alternative with the largest number of appearances in the first position or in ranking lists, or the one determined based on negotiations of decision makers/experts. The proposed ARCAS approach is based on the use of the ARAS method, a new normalization procedure, and the SWARA method. In the proposed approach, each decision maker/expert involved in evaluation has an opportunity to set the preferred level of rating for each criterion used in such evaluation. Finally, a case study is presented in order to highlight the proposed approach. The obtained results confirm the usability and efficiency of the proposed approach.

AN INTEGRATED FUZZY MCGDM APPROACH FOR SUPPLIER SELECTION PROBLEM

Materials constitute a large proportion of the total project cost and the absence of right materials in the right quantities and quality on site when needed is one of the most commonly experienced causes of delays in construction projects. Although supplier selection is a strategic issue, contractors generally select suppliers based on their past experi­ences, which may result in selecting wrong suppliers. Supplier selection decision is generally made by multiple decision makers and is affected by several criteria. Therefore, selecting the right supplier among many alternatives considering several compromising and conflicting criteria is a multi-criteria group decision-making (MCGDM) problem. This paper proposes an integrated fuzzy MCGDM approach, which employs fuzzy analytic hierarchy process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together, for the supplier selection problem. In the proposed approach, fuzzy AHP is used to analyse the structure of the supplier selection problem and to determine the weights of the criteria, and the fuzzy TOPSIS method is employed to rank the alternative suppliers. The proposed approach is applied to a problem of selecting the most appropriate rail supplier and company management found the proposed decision approach satisfactory and implementable in future supplier selection problems.

Benchmarking knowledge management practices in small and medium enterprises

PurposeThe purpose of this paper is to formulate the process of measuring and benchmarking the performance of knowledge management (KM) practices as a multicriteria group decision-making problem and present a new multicriteria group decision-making approach for effectively evaluating the performance of KM practices to meet the interests of various stakeholders in small and medium enterprises (SMEs).Design/methodology/approachA new multicriteria group decision-making approach is developed for evaluating the performance of KM practices of individual SMEs. Intuitionistic fuzzy numbers are used for representing the subjective assessments of decision makers in evaluating the relative importance of the evaluation criteria and the performance of individual KM practices with respect to specific evaluation criteria. A fuzzy multicriteria group decision-making algorithm is developed for measuring and benchmarking the performance of alternative KM practices.FindingsThe proposed multicriteria group decision-making approach is capable of effectively evaluating the performance of KM practices through adequately considering the presence of multiple decision makers, the multi-dimensional nature of the evaluation problem, and appropriately modeling the subjectiveness and imprecision of the evaluation process. The presentation of an example shows that the proposed fuzzy multicriteria group decision-making algorithm is simple to use and efficient in computation.Research limitations/implicationsThe outcome of the multicriteria group decision-making approach is highly dependent on the inputs provided by the decision maker.Practical implicationsThe novelty from this research lies in the utilization of a multicriteria group decision-making approach for evaluating the performance of KM practices in an organization. The outcome from the performance evaluation process allows the enterprise to adopt appropriate KM practices for achieving competitive advantages.Social implicationsThe proposed multicriteria group decision-making approach has a significant social implication as it can be used as a decision-making tool for providing various decision makers in SMEs with useful and strategic information concerning the performance of KM practices in a given situation.Originality/valueThe originality of this paper lies in the development of the multicriteria group decision-making approach for effectively measuring and benchmarking the performance of KM practices of individual SMEs.

Simplified Neutrosophic Linguistic Multi-criteria Group Decision-Making Approach to Green Product Development

For many companies, green product development has become a key strategic consideration due to regulatory requirements and market trends. In this paper, the life cycle assessment technique is used to develop an innovative multi-criteria group decision-making approach that incorporates power aggregation operators and a TOPSIS-based QUALIFLEX method in order to solve green product design selection problems using neutrosophic linguistic information.