Single-valued neutrosophic TODIM method based on cumulative prospect theory for multi-attribute group decision making and its application to medical emergency management evaluation

In recent years, emergent public health events happen from time to time, which puts forward new requirements for the establishment of a perfect medical emergency system. It is a new direction to evaluate the effectiveness of medical emergency systems from the perspective of multi-attribute group decision making (MAGDM) issues. In such article, we tend to resolve the MAGDM issues under single-valued neutrosophic sets (SVNSs) with TODIM method based on cumulative prospect theory (CPT). And the single-valued neutrosophic TODIM method based on CPT (CPT-SVNTODIM) for MAGDM issues are developed. This new method not only inherits advantages of classical TODIM method, but also has further improvement in some aspects. For example, we set up the entropy to calculate attribute weights for ensuring the more objective decision-making process. Furthermore, it is also an extension of MAGDM method to utilize single-valued neutrosophic numbers (SVNNs) to depict decision makers’ ideas. In addition, we introduce the application of CPT-SVN-TODIM method in the assessment of medical emergency management. And finally, the reliability of CPT-SVN-TODIM method is confirmed by comparing with some other methods

VIKOR method for multiple criteria group decision making under 2-tuple linguistic neutrosophic environment

In this article, the VIKOR method is proposed to solve the multiple criteria group decision making (MCGDM) with 2-tuple linguistic neutrosophic numbers (2TLNNs). Firstly, the fundamental concepts, operation formulas and distance calculating method of 2TLNNs are introduced. Then some aggregation operators of 2TLNNs are reviewed. Thereafter, the original VIKOR method is extended to 2TLNNs and the calculating steps of VIKOR method with 2TLNNs are proposed. In the proposed method, it’s more reasonable and scientific for considering the conflicting criteria. Furthermore, the VIKOR are extended to interval-valued 2-tuple linguistic neutrosophic numbers (IV2TLNNs). Moreover, a numerical example for green supplier selection has been given to illustrate the new method and some comparisons are also conducted to further illustrate advantages of the new method

Advances in FUZZY techniques and applications: in occasion of Lofti Zadeh 100 birth anniversary

Advances in FUZZY techniques and applications: in occasion of Lotfi Zadeh 100 birth anniversary. Technological and Economic Development of Economy, 27(2), pp. 280-283

Modified EDAS Method Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-valued Intuitionistic Fuzzy Information

The Interval-valued intuitionistic fuzzy sets (IVIFSs) based on the intuitionistic fuzzy sets combines the classical decision method is in its research and application is attracting attention. After comparative analysis, there are multiple classical methods with IVIFSs information have been applied into many practical issues. In this paper, we extended the classical EDAS method based on cumulative prospect theory (CPT) considering the decision makers (DMs) psychological factor under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original EDAS method based on the CPT under IVIFSs (IVIF-CPT-MABAC) method is built for MAGDM issues. Meanwhile, information entropy method is used to evaluate the attribute weight. Finally, a numerical example for project selection of green technology venture capital has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new methods effectiveness and stability.Comment: 48 page

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

An overview of fuzzy multi-criteria decisionmaking methods in hospitality and tourism industries: bibliometrics, methodologies, applications and future directions

Stakeholders in hospitality and tourism industries are involved in many decision-making scenarios. Multi-criteria decision-making (MCDM) methods have been widely used in hospitality and tourism industries. Although some articles summarised the applications of MCDM models in hospitality and tourism industries, they ignored the fuzziness of individual cognition in an uncertain environment. In addition, these surveys lacked a comprehensive overview from the perspective of bibliometrics analysis and content analysis regarding the whole hospitality and tourism industries. To analyse the applications of fuzzy MCDM methods in hospitality and tourism industries and further explore future research directions, this article reviews 85 selected papers published from 1997 to 2022 regarding fuzzy MCDM models applied in hospitality and tourism industries. Through analysing the results of bibliometric analysis, methodologies and applications, we found that analytic hierarchy process (AHP) and TOPSIS methods are the most widely used MCDM methods, and tourism evaluation, hotel evaluation and selection, tourism destination evaluation and selection are the most attractive research issues in hospitality and tourism industries. Finally, future research directions are proposed from three aspects. This article provides insights for researchers and practitioners who have interest in fuzzy MCDM models in hospitality and tourism industries

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

EDAS method for multiple criteria group decision making with picture fuzzy information and its application to green suppliers selections

In this paper, we construct picture fuzzy EDAS model based on traditional EDAS (Evaluation based on Distance from Average Solution) model. Firstly, we briefly review the definition of picture fuzzy sets (PFSs) and introduce the score function, accuracy function and operational laws of picture fuzzy numbers (PFNs). Then, we combine traditional EDAS model for MCGDM with PFNs. In our model, it’s more accuracy and effective for considering the conflicting attributes. Finally, a numerical example for green supplier selection has been given to illustrate this new model and some comparisons between EDAS model with PFNs and PFWA, PFWG aggregation operators are also conducted to further illustrate advantages of the new method. First published online 23 August 201

Digital transformation in the defense industry: A maturity model combining SF-AHP and SF-TODIM approaches

As an inevitable process, digitalization has become a priority for many companies. The measurement of digital maturity is the first step toward adequately executing this. Although digital maturity models (DMM) have been developed for different sectors in the literature, such studies in the defense industry are lacking due to sector-specific dynamics. This study aims to close this gap and proposes a digital maturity model specific to the defense industry. In this study, a novel model was developed that combines the SF-AHP and SF-TODIM methods due to the uncertainty and hesitancy contained in the evaluation. The validity of the presented novel model has been demonstrated in a prominent defense company in Turkey. According to the results, the most notable digital maturity dimensions are the evaluation of opportunities and alignment with stakeholders. In addition, the model indicates that the company owns the required soft skills, such as leadership, organizational culture, and strategic determination for digital transformation (DT). On the other hand, essential hard skills such as technology and operational competencies are yet to be improved. Lastly, sensitivity and comparison analyses are conducted to validate and verify the obtained results’ stability and robustness

Picture fuzzy cross-entropy for multiple attribute decision making problems

In this paper, we investigate the multiple attribute decision making problems with picture fuzzy information. The advantage of picture fuzzy set is easily reflecting the ambiguous nature of subjective judgments because the picture fuzzy sets are suitable for capturing imprecise, uncertain, and inconsistent information in the multiple attribute decision making analysis. Thus, the cross entropy of picture fuzzy sets, called picture fuzzy cross entropy, is proposed as an extension of the cross entropy of fuzzy sets. Then, a multiple attribute decision making method based on the proposed picture fuzzy cross entropy is established in which attribute values for alternatives are picture fuzzy numbers. In decision making process, we utilize the picture fuzzy weighted cross entropy between the ideal alternative and an alternative to rank the alternatives corresponding to the cross entropy values and to select the most desirable one(s). Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness