Multiple Criteria Group Decision Research Articles

A hybrid linear Diophantine fuzzy group decision approach for enterprise digital transformation maturity evaluation

PurposeThe main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.Design/methodology/approachThis paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.FindingsFirstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.Originality/valueA composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

Hesitant Fuzzy Monotonic Dependent OWA Operator and Its Application in Symmetric Group Decision-Making

Some new techniques of aggregating hesitant fuzzy numbers (HFNs) by using monotonic dependent OWA (MDOWA) operators are investigated. By utilizing the score value of HFN, the concepts of hesitant fuzzy configuration vector and hesitant fuzzy hybrid configuration vector are proposed. Then, some methods of calculating variable weights related to the MDOWA operators under hesitant fuzzy environments are presented. Further, some operators, including hesitant fuzzy monotonic dependent OWA (HFMDOWA) operators and hesitant fuzzy hybrid monotonic dependent OWA (HFHMDOWA) operators, are developed, such as balanced HFMDOWA operators, rewarded HFMDOWA operators, balanced HFHMDOWA operators, rewarded HFHMDOWA operators, and so on. These developed operators are applied to multiple criteria group decision making (MCGDM), and a novel MCGDM algorithm is presented. By using the presented operators and algorithm, we can obtain symmetric decision-making results. Finally, an application example is provided to demonstrate the effectiveness of the developed MCGDM techniques.

Large-scale multiple criteria group decision-making with information emendation based on unsupervised opinion evolutions

Large-scale multiple criteria group decision-making (MCGDM) is prevalent in diverse decision-making scenarios, involving numerous decision makers (DMs), the set of alternatives and criteria, and continuous temporal cycles. Opinions from DMs dynamically evolve through iterative interaction, leading to dynamic opinion evolutions. However, traditional MCGDM methodology usually establish the opinion formation on a static time point throughout information aggregation, which will lead to information distortion. This study develops a novel large-scale MCGDM method with information emendation based on an unsupervised opinion dynamics (UOD) model, combining with the intuitionistic fuzzy set (IFS) and the technique for order preference by similarity to an ideal solution (TOPSIS). The IFS is utilized to quantify opinions since it can effectively achieve a tradeoff between information retention and convenience of evaluation. Simultaneously, in the proposed UOD model, the weight updating mechanism is further considered to improve the interaction adequacy, and the unsupervised mechanism for interaction threshold helps to decrease the influences of subjectivity from DMs. Moreover, numerical simulations validate the UOD model’s feasibility. Finally, a school site selection problem is carried out to elaborate the effectiveness of the proposed method. This study will provide a methodological reference for solving large-scale MCGDM problems, facilitating rapid convergence of opinions within large-scale groups, and enrich the research on opinion dynamics in the field of decision-making.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Solution strategy for sustainable additive manufacturing design problem using Pythagorean fuzzy MCGDM methodology

To solve difficulties involving various groups’ decision-making problems, this work has been proposed to develop a logical aggregation approach to aggregate decision-makers’ crisp data into Pythagorean fuzzy numbers. By combining the established strategy with the Pythagorean fuzzy TOPSIS method, a hybrid Pythagorean fuzzy multiple criteria group decision-making methodology is presented. Based on fuzzy rules inference and the Takagi–Sugeno technique, a novel function is created to represent the degrees of uncertainty in decision-makers’ data. As an example, the material selection process in practical additive manufacturing designs is provided to show how the proposed methodology may be applied to actual applications. Sensitivity analysis is used to evaluate the effectiveness of the suggested methodology. The outcomes demonstrate that the plan was successful in producing a PFN that accurately reflects the decision-maker’s knowledge.

Multi-Criteria Group Decision-Making with ELECTRE-III Method for Selection of Female Spouse in Pythagorean Neutrosophic Environment

Selecting a female partner in the 21st century is more challenging as compared to the 20th century. The globalized world, changing societal norms, and technology have increased the pool of potential partners, making it harder to find a compatible match. The prevalence of social media and online dating has also changed how people interact, leading to new challenges in establishing genuine connections. This paper presents a novel Multiple-Criteria Group Decision Making (MCGDM) model for selecting a female spouse using the ELECTRE-III method in a Pythagorean neutrosophic environment. The proposed model takes into account multiple criteria and addresses the challenges of handling conflicting and uncertain information. The Pythagorean neutrosophic set theory (PNST) is used to handle the vagueness and uncertainty of the decision-making process. The proposed model is validated using a case study of selecting a female spouse based on education, family background, beauty, and skill capability. The case study results demonstrate the proposed model's effectiveness in addressing the complexity and uncertainty of the decision-making process and providing a useful tool for decision-makers in selecting a spouse.

Hamacher Maclaurin symmetric mean aggregation operators and WASPAS method for multiple criteria group decision making under [formula omitted] - spherical fuzzy environment

T-spherical fuzzy sets can accurately represent fuzzy information and effectively simulate real-world decision making scenarios by adjusting the parameter t. The Maclaurin symmetric mean can combine multiple arguments by considering the relationships between them in any decision making process. The main goal of this paper is to develop Maclaurin symmetric mean aggregation operators based on the Hamacher operations of T-spherical fuzzy sets. The developed operators are thoroughly examined through their fundamental properties. The defined operators are adopted to develop a decision making methodology called WASPAS (Weighted Aggregated Sum Product ASsessment) for solving multiple criteria group decision making problems in a T-spherical fuzzy environment. A real-life example of project assessment is illustrated to demonstrate the practicality of the proposed decision making approach. Sensitivity analysis of the parameters is carried out to check their effect on the decision results. A comparison analysis with existing methods confirms the accessibility of the developed approach.

Bayesian Weighting for Multiple Criteria Group Decision-making with Interval Preference Information

Bayesian Weighting for Multiple Criteria Group Decision-making with Interval Preference Information

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Formula omitted]-spherical fuzzy Hamacher Heronian mean geometric operators for multiple criteria group decision making using SMART based TODIM method

T-spherical fuzzy set studied in this paper adequately defines the uncertain information of any decision making situations. This paper aims to provide a solution approach to a multiple criteria group decision making situation under T-spherical fuzzy context. Every group decision making process utilizes aggregation operators to combine the different expert’s opinions. This paper aims to construct several aggregation operators such as T-spherical fuzzy Hamacher Heronian mean-geometric, weighted geometric, ordered geometric, and hybrid geometric operators. The fundamental properties of the proposed operators are thoroughly investigated. The introduced operators are more potent in considering the criteria interrelationships. Criteria weights play an essential role while aggregating different criteria during multiple criteria decision making processes. This work adopts the simple multi-attribute rating technique (SMART) to compute the criteria weights. Utilizing the proposed operators and SMART technique, a multiple criteria group decision making approach called the TODIM (an acronym in Portuguese for Iterative Multi-Criteria Decision Making) is defined based on the Euclidean distance between the T-spherical fuzzy numbers. A decision making situation of selecting the most deserving one from a set of lecturers based on their research capacity is solved using the proposed TODIM approach to show its applicability. Sensitivity analysis is carried out to reveal the effect of parameters on the decision results. A comparative study is made to exhibit the feasibility of the proposed approach.

Cubical fuzzy Einstein Bonferrori mean averaging aggregation operators and their applications to multiple criteria group decision making problems

Consolidating cubical fuzzy numbers (CFNs) is essential in an uncertain decision-making process. This study focuses on creating innovative cubical fuzzy aggregation operators based on the newly proposed Einstein operational laws, utilizing the Bonferroni mean function to capture the interrelationships among the aggregated CFNs. The first contribution of this paper is introducing a novel cubical fuzzy Einstein Bonferroni mean averaging operator. Building upon this operator, we extend our research to develop cubical fuzzy Einstein Bonferroni mean weighted, ordered weighted, and hybrid averaging operators, taking into account the weights of the aggregated CFNs. To ensure their effectiveness, we thoroughly investigate the desirable properties of these proposed operators. Furthermore, we leverage the introduced operators to establish a new approach known as the cubical fuzzy linear assignment method, which proves valuable in resolving multiple criteria group decision-making problems. As a practical demonstration of the method’s utility, we apply it to address a real-life challenge: identifying the optimal location for constructing a wind power plant under a cubical fuzzy environment. To validate the effectiveness of our approach, we compare its results with those obtained using existing methods from the literature. Additionally, we conduct a statistical analysis to visualize the correlative conjunction between the ranking outcomes obtained by different operators

An Integrated Group Decision-Making Framework for the Evaluation of Artificial Intelligence Cloud Platforms Based on Fractional Fuzzy Sets

Due to the rapid development of machine learning and artificial intelligence (AI), the analysis of AI cloud platforms is now a key area of research. Assessing the wide range of frameworks available and choosing the ideal AI cloud providers that may accommodate the demands and resources of a company is mandatory. There are several options, all having their own benefits and limitations. The evaluation of artificial intelligence cloud platforms is a multiple criteria group decision-making (MCGDM) process. This article establishes a collection of Einstein geometric aggregation operators (AoPs) and a novel Fractional Fuzzy VIKOR and Fractional Fuzzy Extended TOPSIS based on the entropy weight of criteria in fractional fuzzy sets (FFSs) for this scenario. The FFSs provide an evaluation circumstance containing more information, which makes the final decision-making results more accurate. Finally, this framework is then implemented in a computational case study for the evaluation of artificial intelligence cloud platforms and comparison of this model with other existing approaches, such as the extended GRA approach, to check the consistency and accuracy of the proposed technique. The most optimal artificial intelligence cloud platform is I1

Linguistic neutrosophic matrix energy and its application in multiple criteria group decision-making

Matrix energy is an important representation tool of collective information. Then, it is not applied to various fuzzy and linguistic environments. To compensate for this gap, this article aims to extend the matrix energy to propose the energy of a linguistic neutrosophic matrix (LNM) for solving a multiple criteria group decision-making (MCGDM) problem, which fully contains LNMs of decision-maker weights, criteria weights, and alternative evaluations. To realize the objective, this study first presents the energy of LNM in view of the true matrix energy, the false matrix energy, and the indeterminate matrix energy. Then, a MCGDM technique is established in view of the LNM energy method in a LNM circumstance. Finally, the developed MCGDM technique using the LNM energy is used to solve the hospital location choice problem in the full LNM scenario. Meanwhile, the decision results indicate the validity and usability of the established MCGDM technique.

An efficient spherical fuzzy MEREC-CoCoSo approach based on novel score function and aggregation operators for group decision making.

The major objective of the current investigation is to build an integrated multiple criteria group decision-making (MCGDM) methodology based on combined compromise solution (CoCoSo) andspherical fuzzy set for determining the optimal solar power station. To begin with, an innovative spherical fuzzy score function is brought forward to strengthen the efficiency of the comparison for spherical fuzzy number (SFN). Secondly, several newly operational laws for SFN are defined and some novel aggregation operation based on them are propounded. The corresponding excellent properties of the novel operators are also explored at length. Further, the spherical fuzzy method on the removal effects of criteria (MEREC) technique is presented by the proposed score function to work out the importance of the criteria. Lastly, an MCGDM approach is propounded based on improved spherical fuzzy CoCoSo to obtain the ranking of the solar power station locations. The feasibility and practicability of the proposed SF-MEREC-CoCoSo method are investigated through the comparison study with the extant methods. The sensibility analysis is also executed to discuss the robustness and stability of the propounded methodology.

Fermatean hesitant fuzzy rough aggregation operators and their applications in multiple criteria group decision-making

The precise selection of suppliers to fulfill production requirements is a fundamental component of all manufacturing and process industries. Due to the increasing consumption levels, green supplier selection (GSS) has been one of the most important issues for environmental preservation and sustainable growth. The present work aims to develop a technique based on Fermatean hesitant fuzzy rough set (FHFRS), a robust fusion of Fermatean fuzzy set, hesitant fuzzy set, and rough set for GSS in the process industry. On the basis of the operational rules of FHFRS, a list of innovative Fermatean hesitant fuzzy rough weighted averaging operators has been established. Further, several intriguing features of the proposed operators are highlighted. To cope with the ambiguity and incompleteness of real-world decision-making (DM) challenges, a DM algorithm has been developed. To illustrate the applicability of the methodology, a numerical example for the chemical processing industry is presented to determine the optimum supplier. The empirical findings suggest that the model has a significant application of scalability for GSS in the process industry. Finally, the improved FHFR-VIKOR and TOPSIS approaches are employed to validate the proposed technique. The results demonstrate that the suggested DM approach is practicable, accessible, and beneficial for addressing uncertainty in DM problems.

Multiple criteria group decision making based on q-rung orthopair fuzzy soft sets

Multiple criteria group decision making based on q-rung orthopair fuzzy soft sets

An approach to hesitant fuzzy linguistic multiple criteria group decision making with uncertain criteria weights considering incomparability between alternatives

In multi-criteria group decision-making (MCGDM) problems, the consensus of experts is taken as the result, and it is common that the result may include incomparability between alternatives. The longest ranking of alternatives that attains consensus and allows the incomparability between alternatives is called a maximum consensus sequence (MCS). In decision-making problems related to emerging industry, it is hard to accurately obtain criteria weights. In this case, how to weight different criteria values and individual opinions to mine an MCS is worth studying. This article applies the hesitant fuzzy linguistic ORESTE method to obtain the ranking of alternatives, allowing for uncertain criteria weights and incomparability between alternatives. Then, a compromise-or-not mechanism based on uncertain criteria weights, which can derive experts’ attitudes towards collective result, is developed to tackle the consensus issue. Two kinds of conflicting items about the incomparability in the collective result and divergent opinions of uncompromising experts are identified. Through information exchange among experts regarding the conflicting items, an MCS can be obtained. The proposed method provides an inspiration for MCGDM in emerging industries.

R, s, t-Spherical Fuzzy VIKOR Method and Its Application in Multiple Criteria Group Decision Making

r, s, t-Spherical Fuzzy VIKOR Method and Its Application in Multiple Criteria Group Decision Making

Bipolar complex fuzzy credibility aggregation operators and their application in decision making problem

<abstract><p>A bipolar complex fuzzy credibility set (BCFCS) is a new approach in computational intelligence and decision-making under uncertainty. Bipolar complex fuzzy credibility (BCFC) information has been employed as a strategy for dealing with confusing and unreliable situations that arise in everyday life. In this paper, we used the concept of aggregation operators to diagnose the well-known averaging and geometric aggregation operators, as well as evaluate some properties and related results. Using described operators, an algorithm for multiple criteria group decision making is proposed. Then, a numerical example of a case study of Hospital selection is discussed. Lastly, the comparative analysis of suggested operators with existing operators are also given to discuss the rationality, efficiency and applicability of these operators.</p></abstract>

A hierarchical integration method under social constraints to maximize satisfaction in multiple criteria group decision making systems

Aggregating multiple opinions or assessments in a decision has always been a challenging field topic for researchers. Over the last decade, different approaches, mainly based on weighting data sources or decision-makers (DMs), have been proposed to resolve this issue, although social choice theory, focused on frameworks to combine individual opinions, is generally overlooked. To resolve this situation, a novel methodology is developed in this paper based on social choice theory and statistical mathematics. This method innovates by dividing the assessment into components which provides a multiple assessment analysis, assigning weights to each source regarding their position compared to the group for each considered criteria. This multiple-weighting process maximises individual and group satisfaction. Furthermore, the method makes it possible to manage previously assigned influence. An example is given to illustrate the proposed methodology. Additionally, sensitivity analysis is performed and comparisons with other methods are made. Finally, conclusions are presented.