Multiple Criteria Group Decision Research Articles

Distance and similarity measures of Pythagorean fuzzy sets and their applications to multiple criteria group decision making

Distance and similarity measures of Pythagorean fuzzy sets and their applications to multiple criteria group decision making

A Novel Selection Model of Surgical Treatments for Early Gastric Cancer Patients Based on Heterogeneous Multicriteria Group Decision-Making

Gastric cancer results in malignant tumors with high morbidity and mortality, and seriously affects the health and life quality of patients. Early detection and appropriate treatment for early-stage gastric cancer patients are very helpful to reducing the recurrence rate and improving survival rates. Hence, the selection of a suitable surgical treatment is an important part. At present, surgical treatment selection has been researched in numerous studies, but there is no study integrating fuzzy decision-making theory with quantitative analysis, considering the patient’s conditions with other relative conditions, and which can handle multisource heterogeneous information at the same time. Hence, this paper proposes a novel selection model of surgical treatments for early gastric cancer based on heterogeneous multiple-criteria group decision-making (MCGDM), which is helpful to selecting the most appropriate surgery in the case of asymmetric information between doctors and patients. Subjective and objective criteria are comprehensively taken into account in the index system of the selection model for early gastric cancer, which combines fuzzy theory with quantitative data analysis. Moreover, the evaluation information obtained from the patient’s conditions, the surgery, and the hospital’s medical status, etc., including crisp numbers, interval numbers, neutrosophic numbers, and probabilistic linguistic labels, is more complete and real, so the surgical treatment selection is accurate and reliable. Furthermore, the technique for order of preference by similarity to ideal solution (TOPSIS) method is employed to solve the prioritization of early gastric cancer surgical treatments. Finally, an empirical study of surgical treatment selection for early gastric cancer surgery is conducted, and the results of sensitivity analysis and comparative analysis suggest that the proposed selection model of surgical treatments for early gastric cancer patients is reliable and effective.

Proposal of a green index for small and medium-sized enterprises: A multiple criteria group decision-making approach

Abstract The impact of small and medium-sized enterprises' (SMEs) activities on the environment has proved to be negative in certain contexts, since these companies have not, for the most part, included environmentally sustainable practices in their processes, strategies, or long-term vision. Therefore, SMEs need to start adopting more environmentally-friendly behaviors to ensure a better future for coming generations. SMEs have failed to do this until now – despite making a significant effort – in part because dealing with this issue is a quite subjective process and a variety of aspects need to be considered. The current study sought to address this problem through the integrated application of cognitive mapping and the Analytic Hierarchy Process (AHP). Based on a constructivist approach, group sessions involving a panel of entrepreneurs and environmental experts created, tested, and validated a green index for SMEs that addresses two of the major limitations of current evaluation approaches. The first limitation is the manner in which evaluation criteria have been defined within the scope of SME environmental conduct assessments, while the second is the method with which these same criteria's weights – or trade-offs – are calculated. The proposed index produced promising results when used to identify the most environmentally-friendly SMEs in a sample of firms. These results can be used as benchmark data for environmental improvement initiatives, and the findings confirm that the methodologies underlying the proposed model-building process facilitate a clearer understanding of how to evaluate SMEs' pro-environmental behaviors. The advantages and limitations of the proposed index are also discussed.

Interval-valued distributed preference relation and its application to group decision making.

As an important way to help express the preference relation between alternatives, distributed preference relation (DPR) can represent the preferred, non-preferred, indifferent, and uncertain degrees of one alternative over another simultaneously. DPR, however, is unavailable in some situations where a decision maker cannot provide the precise degrees of one alternative over another due to lack of knowledge, experience, and data. In this paper, to address this issue, we propose interval-valued DPR (IDPR) and present its properties of validity and normalization. Through constructing two optimization models, an IDPR matrix is transformed into a score matrix to facilitate the comparison between any two alternatives. The properties of the score matrix are analyzed. To guarantee the rationality of the comparisons between alternatives derived from the score matrix, the additive consistency of the score matrix is developed. In terms of these, IDPR is applied to model and solve multiple criteria group decision making (MCGDM) problem. Particularly, the relationship between the parameters for the consistency of the score matrix associated with each decision maker and those for the consistency of the score matrix associated with the group of decision makers is analyzed. A manager selection problem is investigated to demonstrate the application of IDPRs to MCGDM problems.

Pythagorean Fuzzy Muirhead Mean Operators and Their Application in Multiple-Criteria Group Decision-Making

As a generalization of the intuitionistic fuzzy set (IFS), a Pythagorean fuzzy set has more flexibility than IFS in expressing uncertainty and fuzziness in the process of multiple criteria group decision-making (MCGDM). Meanwhile, the prominent advantage of the Muirhead mean (MM) operator is that it can reflect the relationships among the various input arguments through changing a parameter vector. Motivated by these primary characters, in this study, we introduced the MM operator into the Pythagorean fuzzy context to expand its applied fields. To do so, we presented the Pythagorean fuzzy MM (PFMM) operators and Pythagorean fuzzy dual MM (PFDMM) operator to fuse the Pythagorean fuzzy information. Then, we investigated their some properties and gave some special cases related to the parameter vector. In addition, based on the developed operators, two MCGDM methods under the Pythagorean fuzzy environment are proposed. An example is given to verify the validity and feasibility of our proposed methods, and a comparative analysis is provided to show their advantages.

A multiple criteria group decision-making approach for the assessment of small and medium-sized enterprise competitiveness

PurposeSmall- and medium-sized enterprises (SMEs) have become increasingly important in national and international markets because they contribute to the development of local and national economies. SMEs often face serious challenges when competing with multinational companies. The purpose of this paper is to develop a method for assessing SMEs’ competitiveness.Design/methodology/approachBased on a constructivist epistemology, this study makes an integrated use of cognitive mapping and the measuring attractiveness by a categorical-based evaluation technique (MACBETH). To this end, face-to-face sessions were conducted with a panel of entrepreneurs and senior managers who deal with the challenges of maintaining SME competitiveness every day. The proposed assessment system was tested and validated by the panel members.FindingsThe methodological processes adopted in this study provide promising results for decision makers seeking to identify the most competitive SMEs. Specifically, the results emphasize, among other points, the importance of innovation and the human dimension to gaining competitive advantages.Research limitations/implicationsThe evaluation system developed in this study is extremely versatile and confirms the usefulness of integrating cognitive mapping and MACBETH to facilitate evaluations of SME competitiveness. However, due to its idiosyncratic and process-oriented nature, generalizations need to be done with caution.Practical implicationsThe proposed method can be valuable to researchers seeking to develop mechanisms for evaluating SMEs’ entrepreneurial performance and include specialized know-how and sensemaking in organizational decision-making processes.Originality/valueThe integrated use of cognitive maps and MACBETH contributes to a better understanding of how to assess SMEs’ competitiveness. No prior work reporting the use of this dual methodology in this study context has been found.

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 Research on Multiple Criteria Group Decision-making of Suppliers Based on Hesitant Fuzzy Linguistic Terms

A Research on Multiple Criteria Group Decision-making of Suppliers Based on Hesitant Fuzzy Linguistic Terms

An integrated ANP-VIKOR methodology for sustainable supplier selection with interval type-2 fuzzy sets

Recently, sustainable supplier chain management (SSCM) has gained increasing attentions both from corporate and academic. A large number of multiple criteria group decision-making (MCGDM) methods for sustainable supplier selection have been developed. In this paper, considering that the interval type-2 fuzzy sets (IT2FSs) exhibit some interesting properties for handling uncertain information and high order imprecision, we integrate the ANP (analytical network process) method and the VIKOR (ViseKriterijumska Optimizacija I Kompromisno Resenje) method under interval type-2 fuzzy environment to solve supplier selection problem in SSCM. The proposed method consists of two steps. First, we obtain the weights of criteria via the ANP method. Then, based on the weights of criteria presented in step 1, a compromise solution will be proposed with the aid of VIKOR method. A numerical example is presented to show the decision process in detail. In addition, a comparative analysis with interval type-2 fuzzy ANP method and TOPSIS method is also presented to verify the validity of the proposed method.

A novel approach for multiple criteria group decision making problem with unknown criteria weight information

A novel approach for multiple criteria group decision making problem with unknown criteria weight information

An improved TOPSIS method for metro station evacuation strategy selection in interval type-2 fuzzy environment

With the constant growth of urban construction, metro station has being playing an increasingly important role in the public transportation system. To ensure the safe operation and the safety of the people, it is necessary to analyze how to select a suitable evacuation strategy while emergency event occurs in a metro station. The evacuation strategy selection can be regarded as a multiple criteria group decision making problem, which involves some conflict evaluation criteria. This paper presents an improved TOPSIS method to handle the evacuation strategy selection problem of metro station based on interval type-2 fuzzy sets. Firstly, the TOPSIS method in interval type-2 fuzzy environment is introduced. Then the evacuation strategy selection model and the calculation steps for metro station in emergency situation are constructed. Finally, a numerical example about the evacuation strategy selection along with a sensitivity analysis about the parameters and a comparison analysis with Chen’s research is provided to verify the efficiency of the proposed method.

Pythagorean fuzzy uncertain linguistic TODIM method and their application to multiple criteria group decision making

Pythagorean fuzzy uncertain linguistic TODIM method and their application to multiple criteria group decision making

Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices

Group decision making plays an important role in various fields of management decision and economics. In this paper, we develop two methods for hesitant fuzzy multiple criteria group decision making with group consensus in which all the experts use hesitant fuzzy decision matrices (HFDMs) to express their preferences. The aim of this paper is to present two novel consensus models applied in different group decision making situations, which are composed of consensus checking processes, consensus-reaching processes, and selection processes. All the experts make their own judgments on each alternative over multiple criteria by hesitant fuzzy sets, and then the aggregation of each hesitant fuzzy set under each criterion is calculated by the aggregation operators. Furthermore, we can calculate the distance between any two aggregations of hesitant fuzzy sets, based on which the deviation between any two experts is yielded. After introducing the consensus measure, we develop two kinds of consensus-reaching procedures and then propose two step-by-step algorithms for hesitant fuzzy multiple criteria group decision making. A numerical example concerning the selection of selling ways about ‘Trade-Ins’ for Apple Inc. is provided to illustrate and verify the developed approaches. In this example, the methods which aim to reach a high consensus of all the experts before the selection process can avoid some experts’ preference values being too high or too low. After modifying the previous preference information by using our consensus measures, the result of the selection process is much more reasonable.

Medical Tourism Destinations Prioritization using Group Decision Making Method with Neutrosophic Fuzzy Preference Relations

Medical tourism has developed rapidly worldwide, especially in Asia, and one of the most important problems facing the patient-tourists is the selection of the optimum destination. In this paper, we present a novel multiple criteria group decision making (MCGDM) methodology to evaluate and rank the medical tourism destinations vague based on vague information. A systematic assessment and selection model was constructed by investigating MCGDM problems with neutrosophic fuzzy preference relations (NFPRs). We began by defining NFPRs which allow the patient-tourists lacking information, time or patience, to express their uncertainty and hesitancy about the given preference values. The additive consistency and acceptable consistency for NFPRs were then proposed. Furthermore, the approach to improve the consistency for NFPRs was also validated and a series of aggregation operators were developed. In addition, we presented a systematic MCGDM method using NFPRs (MCGDM-NFPRs) in this paper to rank the medical tourism destinations. Then our proposed approach was applied to two cases considering different kinds of original data to prioritize medical tourism places. Finally, the applicability and feasibility of proposed approach were verified by the comparison with other previous methods, along with some analyses and a comprehensive discussion

Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria

In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE) as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM) approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS) to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager’s prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches.

An integrated Shannon entropy and TOPSIS for product design concept evaluation based on bijective soft set

This work addresses the problem of concept evaluation in uncertain environments of early design stages, with the help of the proposed integrated method involving bijective soft sets, Shannon entropy and technique for order of preference by similarity to ideal solution (TOPSIS). The application of soft set theory in the domain of product design and development is still not being efficiently utilised by previous works. Therefore, the benefit of soft set theory to define the attributes of an object through any type of approximations is used to deal with qualitative description of design criteria, by designers, and the linguistic requirements of customers. Bijective soft sets help the designers to effectively map individual customer requirements to the generated design concepts. With the help of Shannon entropy, customers’ preferences among the identified needs are captured in the new product development process, thus, improving the overall effectiveness of the concept evaluation process. This work considers concept evaluation process as a group multiple criteria decision making problem where, design specifications and customer requirements in qualitative form are first captured by using soft set theory and Shannon entropy and then, this information is incorporated in the framework of TOPSIS to identify the best concept in the evaluation process. The developed integrated method for concept evaluation process is validated with the help of a design illustration, which shows that soft set theory can serve as an effective tool for designers and researchers to reduce the imprecise and vague content of early design stages.

Linguistic value soft set-based approach to multiple criteria group decision-making

Molodtsov's soft set theory provides a general mathematical tool for dealing with uncertainty in decision-making. In the past few years, a large number of generalized soft set concepts and approaches to decision-making have been studied by many researchers. This paper considers a new generalization of Molodtsov's soft set concept, named linguistic value soft set, by introducing a linguistic variable to the definition of soft set theory. Then, the information in several soft sets can be saved in one linguistic value soft set. We define basic notions such as the equality of two linguistic value soft sets, and the subsets and complement of a linguistic soft set. Next, we present the binary operations for linguistic value soft set theory. Then, we discuss the properties of linguistic value soft set for the basic notions and binary operations. Further, we define the concept of linguistic value soft matrix and choice value matrix of a linguistic value soft set, and we discuss the operation laws. By using these new definitions of linguistic value soft set theory, we establish a new approach to multiple criteria group decision-making problems with linguistic value information. We present a detailed description of the decision-making problem and the decision steps. Finally, the validity of the proposed decision-making method is tested by a numerical example with the background of an evaluation decision problem for automobiles. The main contribution of this paper is twofold. One is to propose the concept of linguistic value soft set and establish the main results of the theoretical aspect for the new generalization form. Another is to present a new approach to multiple attribute group decision-making based on linguistic value soft set.

Multigranulation fuzzy rough set over two universes and its application to decision making

The original Pawlak’s rough set approach based on indiscernibility relation (single granularity) has been extended to multigranulation rough set structure in the recent years. Multigranulation rough set approach has become a flouring research direction in rough set theory. This paper considers rough approximation of a fuzzy concept under the framework of multigranulation over two different universes of discourse, i.e., multigranulation fuzzy rough set models over two universes. We present three types of multigranulation fuzzy rough set over two universes by the constructive approach, respectively. Some interesting properties of the proposed models are discussed and also the interrelationships between the proposed models and the existing rough set models are given. We then propose a new approach to a kind of multiple criteria group decision making problem based on multigranulation fuzzy rough set model over two universes. The decision rules and algorithm of the proposed method are given and an example of handling multiple criteria group decision making problem of clothes ranking illustrates this approach. The main contribution of this paper is twofold. One is to establish the multigranulation fuzzy rough set theory over two universes. Another is to try presenting a new approach to multiple criteria group decision making based on multigranulation fuzzy rough set over two universes. The proposed models not only enrich the theory of multigranulation rough set but also make a tentative to provide a new perspective for multiple criteria group decision making with uncertainty.

Three-way group decision making based on multigranulation fuzzy decision-theoretic rough set over two universes

Decision-theoretic rough set provides a new perspective to handle decision-making problems under uncertainty and risk. The three-way decision theory proposed by Yao is based on rough set theory and is a natural extension of the classical two-way decision approach. In this paper, we introduce the idea of decision-theoretic rough set into multigranulation approximation space and explore the rough approximation of a fuzzy decision object under the framework of two universes. We construct a variable precision multigranulation fuzzy decision-theoretic rough set over two universes by using the concept of an arbitrary binary fuzzy relation class between two different universes and the probability measurement of a fuzzy event. Several interesting properties of the proposed model are addressed and the decision rules are also deduced using the concept of three-way decision-making over two universes. Moreover, two special types of optimistic and pessimistic models are given by using different precision parameters. We then present a new approach to multiple criteria group decision making problems, based on variable precision multigranulation fuzzy decision-theoretic rough set over two universes. Meanwhile, we establish a cost-based method for sorting among all alternatives of group decision-making problems. Finally, an example of handling a medical diagnosis problem illustrates this approach.

An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

In order to deal with the fuzzy large-scale multiple-criteria group decision-making (FLMCGDM) problems, this paper incorporates clustering analysis and information aggregation operator into the problems of large-scale multiple-criteria group decision-making with interval type-2 fuzzy sets (IT2 FSs). The interval type-2 fuzzy equivalence clustering (IT2-FEC) analysis is used to classify decision-makers (DMs) to reduce the dimension of the large-scale DMs in the FLMCGDM problems. The combined weighted geometric averaging (CWGA) operator is extended into the case with IT2 FSs variables, which can take both the importance of individual and its relative position into account. Afterwards, a solution process for the FLMCGDM problems is proposed, in which the new equivalence clustering method and CWGA operator of IT2 FSs is incorporated. Finally, the reasonability and effectiveness of the proposed method are verified by an illustrative example. Compared with other methods, the IT2-FEC analysis can deal with the linguistic variables and produce dynamic clustering results in a more efficient way.