Multi-criteria Group Research Articles

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

Prediction of eco sustainability component using fuzzy Z numbers based ratio analysis and interval type 3 fuzzy logic system

Amid rapid global urbanization, understanding eco-sustainability challenges in megacities like Delhi is crucial both locally and globally. This study introduces a novel multi-criteria group decision-making approach, the Z-Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), to address these complex challenges. This method prioritizes key eco-sustainability components like air quality index (AQI), water quality index (WQI), Temperature, and Precipitation, based on their impact, aiming to enhance resource allocation and environmental quality. Criteria weights are determined using fuzzy analytical hierarchical processing with Z-numbers, which manage decision-makers' uncertainty through a reliability function, illustrating their confidence levels and supporting the adaptability and effectiveness of eco-sustainability measures. A case study is conducted on seven sub-cities in Delhi examining their unique environmental impacts and urban sustainability challenges. Moreover, the most effective component of eco-sustainability say AQI is also dependent on some factors like PM10, PM2.5, SO2, O3, NO2, and CO. Considering these factors as inputs, an interval type-3 fuzzy logic system (IT3FLS) has been constructed to predict AQI, supporting timely interventions and improvements in eco-sustainability management. The IT3FLS model demonstrated exceptional precision across all Delhi sub-cities, with low root means square error (<0.05), and high R2 values (>0.94), affirming its effectiveness. To validate the Z-Fuzzy SWARA rankings, we compared them with existing Z-Fuzzy methods, consistently ranking AQI first. The IT3FLS's AQI predictions were also compared with those from fuzzy logic and advanced machine learning algorithms, demonstrating superior performance. Finally, the study underscores the managerial implications, emphasizing their role in guiding decision-making and strategy implementation.

Emergency water supply during disasters in Iran: a multi-criteria group decision analysis

Emergency water supply during disasters in Iran: a multi-criteria group decision analysis

20 years review of the multi actor multi criteria analysis (MAMCA) framework: a proposition of a systematic guideline

AbstractMulti-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

P,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document}Fractional fuzzy sets and their aggregation operators and applications

Using p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} fractional fuzzy sets (p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} FFS) to demonstrate the stability of cryptocurrencies is considered due to the complex and volatile nature of cryptocurrency markets, where traditional models may fall short in capturing nuances and uncertainties. p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} FFS provides a flexible framework for modeling cryptocurrency stability by accommodating imprecise data, multidimensional analysis of various market factors, and adaptability to the unique characteristics of the cryptocurrency space, potentially offering a more comprehensive understanding of the factors influencing stability. Existing studies have explored Picture Fuzzy Sets and Spherical Fuzzy Sets, built on membership, neutrality, and non-membership grades. However, these sets can’t reach the maximum value (equal to 1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1$$\\end{document}) due to grade constraints. For example, when considering ℘=(h,⟨0.9,0.8,1.0⟩h∈H)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\wp =(h,\\langle \ ext{0.9,0.8,1.0}\\rangle \\left|h\\in H\\right.)$$\\end{document}, these sets fall short. This is obvious when a decision-maker possesses complete confidence in an alternative, they have the option to assign a value of 1 as the assessment score for that alternative. This signifies that they harbor no doubts or uncertainties regarding the chosen option. To address this, p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} Fractional Fuzzy Sets (p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} FFSs) are introduced, using new parameters p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p$$\\end{document}, q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q$$\\end{document}, and r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r$$\\end{document}. These parameters abide by p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p$$\\end{document},q≥1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q\\ge 1$$\\end{document} and r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r$$\\end{document} as the least common multiple of p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p$$\\end{document} and q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q$$\\end{document}. We establish operational laws for p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} FFSs. Based on these operational laws, we proposed a series of aggregation operators (AOs) to aggregate the information in context of p,q,r-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p,q,r-$$\\end{document} fractional fuzzy numbers. Furthermore, we constructed a novel multi-criteria group decision-making (MCGDM) method to deal with real-world decision-making problems. A numerical example is provided to demonstrate the proposed approach.

Group decision-making with hesitant fuzzy linguistic preference relations in view of worst and average indexes

To address the situation where Multi-criteria group decision-making (MCGDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs), this study introduces a group decision-making (GDM) method in view of the worst additive consistency index (WACI) and average additive consistency index (AACI) simultaneously. First, several optimization models are constructed for deriving the WACI and AACI. The main characteristic of the constructed optimization models is that it takes into accounted the personalized individual semantics (PISs). And then the concept of acceptable additive consistent HFLPRs is developed based on the proposed WACI and AACI. Second, several optimization models are constructed for improving the consistency of HFLPRs. Two predefined thresholds for the WACI and AACI are considered in the proposed optimization models. It requires the consistency levels of all the linguistic preference relations (LPRs) associated with HFLPR meet the threshold of WACI, and the average consistency level of all LPRs reaches the threshold of AACI. Third, an algorithm is designed for deriving priority weights from acceptable consistent HFLPRs. Finally, the presented models are validated using a numerical example and extensive comparative analyses.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

A quantum probability theory-based method for heterogeneous multi-criteria group decision making with incomplete probabilistic linguistic preference relations considering the interference effect among decision makers

Quantum probability theory (QPT) reveals that decision makers (DMs) can interfere with each other and thus affect the decision result. However, existing methods of heterogeneous multi-criteria group decision making (HMCGDM) seldom consider the interference effect among DMs. This paper defines an interference degree based on the phase angle difference obtained by QPT to address HMCGDM problems in which the alternative and criteria sets provided by DMs may be different. In addition, the incomplete probabilistic linguistic preference relations (InPLPRs) are introduced to express the evaluation information of DMs. Considering that the same linguistic term may has different meanings for DMs, we construct a consistency-driven optimization model based on the personalized individual semantics (PISs) to estimate the missing information in InPLPR on each criterion provided by DMs and the corresponding complete PLPR (CPLPR). Then, for each DM, the individual weights of criteria and the comprehensive CPLPR on all criteria in the individual criteria set can be obtained by an optimization model. Subsequently, a novel QPT-based model is erected to derive the weights of DMs and the ranking order of alternatives. Finally, an example of tourism evaluation and several sensitivity and comparative analyses are offered to examine the applicability of the proposed method.

Exploiting experts’ asymmetric knowledge structures for consensus reaching: a multi-criteria group decision making model with three-way conflict analysis and opinion dynamics

Exploiting experts’ asymmetric knowledge structures for consensus reaching: a multi-criteria group decision making model with three-way conflict analysis and opinion dynamics

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:q$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:r$$\\end{document}. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Multi-criteria group decision-making problem under intuitionistic fuzzy logarithmic aggregation operators based on T-norm and T-conorm

Multi-criteria group decision-making problem under intuitionistic fuzzy logarithmic aggregation operators based on T-norm and T-conorm

Selection of Green Recycling Suppliers for Shared Electric Bikes: A Multi-Criteria Group Decision-Making Method Based on the Basic Uncertain Information Generalized Power Weighted Average Operator and Basic Uncertain Information-Based Best–Middle–Worst TOPSIS Model

This study introduces a novel multi-criteria group evaluation approach grounded in the theory of basic uncertain information (BUI) to facilitate the selection of green recycling suppliers for shared electric bikes. Firstly, a comprehensive index system of green recycling suppliers is established, encompassing recycling capacity, environmental sustainability, financial strength, maintenance capabilities, and policy support, to provide a solid foundation for the scientific selection process. Secondly, the basic uncertain information generalized power weighted average (BUIGPWA) operator is proposed to aggregate group evaluation information with BUI pairs, and some related properties are investigated. Furthermore, the basic uncertain information-based best–middle–worst TOPSIS (BUI-BMW-TOPSIS) model incorporating the best, middle, and worst reference points to enhance decision-making accuracy is proposed. Ultimately, by integrating the BUIGPWA operator for group information aggregation with the BUI-BMW-TOPSIS model to handle multi-criteria decision information, a novel multi-criteria group decision-making (MCGDM) method is constructed to evaluate green recycling suppliers for shared electric bikes. Case analyses and comparative analyses demonstrate that compared with the BUIWA operator, the BUIGPWA operator yields more reliable results because of its consideration of the degree of support among decision-makers. Furthermore, in contrast to the traditional TOPSIS method, the BUI-BMW-TOPSIS model incorporates the credibility of information provided by decision-makers, leading to more trustworthy outcomes. Notably, variations in attribute weights significantly impact the decision-making results. In summary, our methods excel in handling uncertain information and complex multi-criteria group decisions, boosting scientific rigor and reliability, and supporting optimization and sustainability of shared electric bike green recycling suppliers.

Multi-Objective Optimization based decision-making process using Dombi Logarithmic aggregation operator in CNN environment and its application to optimally select suitable greenhouse site for tomato crops

In this research article, we have defined Dombi Logarithmic aggregation operational laws in the framework of Cylindrical Neutrosophic Numbers (CNN) and utilized these laws to establish a new aggregation operator namely Cylindrical Neutrosophic Dombi Weighted Logarithmic Aggregation Operator(C N DW L A ). The said aggregation operational laws &amp; aggregation operator have been used to present a new and novel decision making process where Full Consistency Method (FUCOM) and Multi-Objective Optimization(MOO) have been inte- grated and embedded fruitfully. Here the objective functions were formulated using the concept of a single-layer neural network and the FUCOM method to assess criterion weights. We have resolved the most favorable Pareto optimal solution derived from Multi-Objective Optimization by employing simulation and the Technique for Or- der of Preference by Similarity to Ideal Solution (TOPSIS) approach. Finally, we have applied our proposed decision making method along with MARCOS and MOORA techniques to determine the best greenhouse site for cultivating tomato crops. An exhaustive sensitivity and comparative analysis has been conducted to assess the robustness and stability of our Multi Criteria Group Decision Making (MCGDM) techniques. It is observed that our proposed method has some advantages in cylindrical neutrosophic environment and it produces stable robust results with the variation of underlying model parameters.

Two-stage stochastic multi-criteria group decision making for wind-photovoltaic-hydropower scheduling

Scheduling of wind-photovoltaic-hydropower systems is a complex multi-objective collaborative problem which requires multi-criteria decision making (MCDM) to assist in formulating the most-preferred scheduling scheme. There are uncertainties existing in criteria weights (CWs) due to the limitations of the knowledge level of decision makers and the subjectivity of preferences. This paper proposes a two-stage stochastic multi-criteria group decision making model considering the uncertainties in the decision making environment and the progression of the decision making process. For the prophase decision making (the first stage), the stochastic multi-criteria acceptability analysis (SMAA) theory and the MCDM model VIKOR are combined to construct a SMAA-VIKOR model for inverse weight space analysis on the situation that the initial CW information is completely unknown. In the second stage of decision making, the decision makers are allowed to express their fuzzy preferences based on the information provided in the first stage by coupling the intuitionistic fuzzy analytic hierarchy process (IFAHP) into the SMAA-VIKOR model to obtain the final scheme. The wind-photovoltaic-hydropower system in Yalong River Basin is selected as the case study. Results show that among the 12 alternative schemes, scheme A2 is ranked first with the probability of 0.69 in the first stage while the probability of taking the first ranking in the second stage is increased to 1. More explicit ranking results are obtained based on the results of the first stage, which effectively enhances the confidence of decision makers and improves decision making efficiency.

Multi-criteria group decision analytics for sustainable supplier relationship management in a focal manufacturing company

In the context of sustainable development, while the benefits of incorporating environmental, social, and governance (ESG) considerations into supplier management have been widely discussed in recent years, a significant gap in the literature remains. Specifically, there is a lack of a standardized decision analytics framework for ESG-oriented supplier relationship management (SRM) processes in the manufacturing sector. To address this gap, this study introduces a multi-criteria supplier analytics framework (MCSAF) towards sustainable SRM. Through referring traditional supplier evaluation criteria as well as emerging ESG criteria, a novel decision hierarchy for supplier segmentation is developed. The proposed system utilizes the Bayesian best-worst method and the Canopy-k-means clustering algorithm to formulate different supplier segments for strategic development. The proposed MCSAF has been successfully implemented in a German solar water pump manufacturer. The results show that the five highest-ranked criteria are ‘Quality’, ‘Impact and importance on product quality and business growth’, ‘Environmental policy/management’, ‘Customer and product responsibility’, and ‘Risk and crisis management’. 52 suppliers are categorized into five segments, leading to the formulation of ESG-enabled supplier development strategies are subsequently formulated. The successful implementation of the MCSAF showcases its effectiveness in advancing ESG practices in sustainable supplier management.

P,q-Quasirung orthopair fuzzy multi-criteria group decision-making algorithm based on generalized Dombi aggregation operators

p,q-Quasirung orthopair fuzzy multi-criteria group decision-making algorithm based on generalized Dombi aggregation operators

CMAA–AHP: combinatorial multicriteria acceptability analysis with the analytic hierarchy process

AbstractCombinatorial multi-criteria acceptability analysis (CMAA) is a framework for supporting multicriteria group decisions that provides both a detailed analysis of the effects of individual decision-maker inputs as well as interactive guidance for a consensus-building process. The analytical hierarchy process (AHP) is a widely-used model of decision-maker evaluations that is based on pairwise comparisons. The goal of this work is to show how CMAA can be integrated with AHP in order to make its benefits available to AHP users. We use a minimal input format for AHP which avoids a problem with inconsistency and also reduces the cognitive load on the decision-makers. We extend the CMAA method by introducing new judgement and preference sensitivity variables, which provide helpful insights for the facilitator of the group decision. An example illustrates the combined CMAA–AHP method and its ability to deliver consensus in a very small number of iterations. Monte Carlo simulation is used to study the convergence behavior of the method for a range of problem dimensions. It was found that the mean number of steps to reach consensus grows linearly with the number of alternatives and criteria. We consider two previously published group decisions that use the standard AHP approach of averaging decision-maker judgements and preferences. In both cases, CMAA–AHP delivers the same rankings based on the original input. However, the new method also provides insight into each decision and would have been able to guide each group to consensus within a small number of resolution steps.

New covering techniques and applications utilizing multigranulation fuzzy rough sets

In order to conduct an in-depth study of Zhan’s methodology pertaining to the covering of multigranulation fuzzy rough sets (CMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {C}_{{MG}}$$\\end{document}FRSs), we build two families: the family of fuzzy β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-minimum descriptions and the family of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-maximum descriptions. Subsequently, utilizing these notions, we proceed to develop two variations of covering via optimistic (pessimistic) multigranuation rough set samples (CO(P)MG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO(P)}_{{MG}}$$\\end{document}FRS). The axiomatic properties are examined. In this study, we examine four models of covering using variable precision multigranulation fuzzy rough sets (CVPMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CVP}_{{MG}}$$\\end{document}FRSs). We proceed with analyzing the features of these models. Interconnections between these planned plans are also elucidated. This study explores algorithms that aim to identify innovative strategies for addressing multiattribute group decision-making problems (MAGDM) and multicriteria group decision-making problems (MCGDM). The test examples have been elucidated to provide an inclusive grasp of the efficacy of the offered samples. Ultimately, the distinctions between our methodologies and the preexisting research have been demonstrated.

Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making.

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers

Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers