The availability of numerous types of aircraft and their technical capabilities are offering a wide range of alternatives. As customers have different expectations, aircraft type selection is a business strategy for the airline companies. The choice on carriers should be made in accordance with disparate dimensions such as customers' expectations, profit of the company, capacity limitations and market conditions. This study formulates aircraft type selection as a Multi Criteria Decision Making (MCDM) problem and proposes a novel model that incorporates fuzzy trigonometric norms to solve. Being differentiated from the existing models in the literature, a two-stage model is identified. In the 1st stage, a fuzzy trigonometric-based Ordinal Priority Approach (OPA) determines the criteria weights. In the 2nd stage, RAFSI (Ranking of Alternatives through Functional Mapping of Criteria Subintervals into Single Intervals) is integrated to determine the optimal aircraft type. The model simulated for a case of Turkish Airline company. Sensitivity tests justify robustness of the model. Results show that among the four options, medium-scale high-qualified but not luxury aircraft is the best option.

Choosing the right projects in renewable energy investments is very significant. Due to this issue, necessary improvements to the performance indicators of these projects should be made. However, every improvement made also leads to an increase in costs. There is a need for a priority analysis to find the most important factors affecting the selection of the right renewable energy projects. Accordingly, this study aims to evaluate critical determinants of renewable energy project selection and provide effective investment strategies for this situation with a new fuzzy decision-making model. First, the indicators of renewable energy project selection are analyzed by perception and expression-based quantum Spherical fuzzy M-SWARA. The weights of these determinants are also calculated by DEMATEL to check the reliability of the results. Secondly, the priorities of renewable energy project selection are ranked by considering perception and expression-based quantum Spherical fuzzy ELECTRE. This calculation is also made by TOPSIS methodology to measure the reliability of the findings. The main contribution of this manuscript is that perception and expression-based evaluation can be carried out in the proposed model. In this process, the facial expressions and emotions of the decision makers are considered so that the hesitancy of these people while answering these questions can be included in the evaluation process. Another important novelty is that a new decision-making model (M-SWARA) is also proposed. This new technique provides an opportunity to consider causality relationship between the criteria to reach the most significant ones. The weighting results are the same for both M-SWARA and DEMATEL approaches. This situation gives information that the findings are coherent and valid. Market analysis has the greatest value in both perception-based (0.272) and expression-based (0.259) evaluations. Owing to this analysis, it is possible to clearly understand the supply-demand balance in the market. The ranking results indicate that technical adequacy is the most significant priority alternative for the selection of the appropriate renewable energy alternatives.

Multiattribute decision‐making (MADM) approach is an effective method for handling ambiguous information in a practical situation. The process of the MADM technique has drawn a lot of interest from various academic and selection processes of extensive analysis. The aggregation operators (AOs) are the best mathematical tools and received a lot of attention from researchers. This article explored the theory of intuitionistic fuzzy IF sets (IFSs) and their certain fundamental operations. The theory of triangular norms also explores Aczel Alsina operations (AAOs) in advanced mathematical tools. The concepts of Heronian mean (HM) and geometric HM (GHM) operators are presented to define interrelationships among different opinions. We developed a list of certain AOs by utilizing AAOs under the system IF information, namely, IF Aczel Alsina HM (IFAAHM), IF Aczel Alsina weighted HM (IFAAWHM), IF Aczel Alsina GHM (IFAAGHM), and IF Aczel Alsina weighted GHM (IFAAWGHM) operators. Some particular characteristics of our invented methodologies are also presented. Solar energy is an effective, efficient resource to enhance electricity production and the country’s economic growth. Therefore, we studied an application of solar panel systems to solve real‐life problems under a robust technique of the MADM approach by utilizing our invented approaches of IFAAWHM and IFAAWGHM operators. A numerical example was also given to select more suitable solar panels under our proposed methodologies. To find the competitiveness and feasibility of discussed methodologies, we make an inclusive comparative study in which we contrast the results of existing AOs with the consequences of current approaches.

Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

The task of an information retrieval system (IRS) is to retrieve relevant information from large datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision support system (DSS) problem. The cubic m-polar fuzzy set (CmPFS) model is proposed to address the multi-polarity issue during algorithm selection and to express multi-polar information under m-fuzzy intervals. In this study, we introduce the Schweizer-Sklar (SS) aggregation operator based on CmPFS and develop various new aggregation operators using CmPFS and SS t-norm and t-conorm. New operations such as addition, usual multiplication, and power of two or more Cubic m-polar fuzzy Numbers (CmPFNs) based on SS t-norm and t-conorm are also defined. We also define score and accuracy functions to determine the priorities of alternatives. Four operators are proposed as follows: Cubic m-polar fuzzy Schweizer-Sklar weighted average (CmPFSSWA), Cubic m-polar fuzzy Schweizer-Sklar ordered weighted average (CmPOWA), Cubic m-polar fuzzy Schweizer-Sklar weighted geometric (CmPFSSWG), and Cubic m-polar fuzzy Schweizer-Sklar ordered weighted geometric (CmPFSSOWG). The desired qualities of these operators are explored and used to solve the DSS problem based on CmPFS. Finally, a practical application of an IRS is presented to demonstrate the effectiveness and reliability of the proposed operators.

This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision-making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision-making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.

This study proposes a novel structured framework for analyzing sustainable operational performance-related issues of ash handling unit (AHU) under vague/uncertain information and its impact on ecofriendly production. The structured framework is developed using an integrated fuzzy lambda–tau and fuzzy multicriteria decision-making methods. Reliability parameters are formulated for assessing the failure behavior of AHU using the fuzzy lambda–tau approach. Repair time parameter for the AHU system shows an increasing trend with a 0.6840% increase as the spread increases from ±15% to ±25% and 0.5981% when spread increases from ±25% to ±60%. Furthermore, the availability of the system decreases by 0.0335% with increase in spread from ±15% to ±25% and further decreases to 0.0315% with spread increases from ±25% to ±60%. Under risk analysis, possible failure causes with their fuzzy rating are listed under the failure mode and effect analysis (FMEA) approach. Evaluation based on distance from average solution (EDAS) and combinative distance-based assessment (CODAS) methods are further applied within the FMEA approach for identifying the most critical failure causes. Improper design (BT2), excessive gas volume (FH3), chocking/blockage (WA1), and scanty lubrication/greasing (SP1) associated with bottom ash handling, the fly ash handling system, wetting unit and ash disposal pipeline, and slurry pump with EDAS outputs (0.1464, 0.1090, 0.0918, 0.000) and CODAS outputs (3.3962, 3.5208, 2.6562, 2.0877) are found responsible for sudden failure of the considered unit. Sensitivity analysis has been done for evaluating the robustness of the proposed framework.

Uncertainty is often occurred in real-life decision-making problems due to the lack of complete information, imprecise data, and the vagueness of decision making experts in qualitative judgment, thus, the crisp values of criteria may be insufficient to handle such types of complex real situations. As the extension of fuzzy set, intuitionistic fuzzy set and Pythagorean fuzzy set, the Fermatean Fuzzy Set (FFS) has been demonstrated as a powerful tool to handle the uncertainty arisen in practical decision-making problems. Thus, this study aims to introduce an integrated Fermatean fuzzy information-based decision-making method by combining method based on the removal effects of criteria (MEREC) and additive ratio assessment (ARAS) methods with the application in a food waste treatment technology selection problem. By using Fermatean fuzzy numbers, the suggested approach successfully handle the qualitative data and uncertain information that often occur in practical situations. This study consists of four phases. First, entropy measure is developed for FFS and further utilized for determining the experts’ weights. Second, some Fermatean fuzzy Heronian mean operators and their properties are introduced to aggregate the Fermatean fuzzy information. These operators can provide us a valuable means to handle practical multicriteria decision-making problems on FFSs context. Third, an extended MEREC technique is originated to assess objective criteria weights within FFS context. Fourth, an integrated ARAS method is introduced with the combination of proposed entropy measure, generalized weighted Fermatean fuzzy Heronian mean operator and MEREC technique to evaluate and rank the alternatives. To confirm the reasonableness and practicality of the proposed methodology, an empirical case study of food waste treatment technology selection is discussed on FFSs settings. Further, a comparison with extant models and a sensitivity investigation are performed to confirm the validity and robustness of the obtained outcomes.

In this manuscript, the technique of the generalized MULTIMOORA method is investigated by using the concept of a T-spherical fuzzy set (T-SFS), which is the modified idea of a picture fuzzy set to handle awkward and vague information in daily life problems. T-SFS covers the degree of truth, abstinence, and falsity with a rule that the sum of the qth-powers of all degrees is restricted to the unit interval. Moreover, for examining the interrelationships among any number of T-SFSs, we construct the idea of T-spherical fuzzy Dombi prioritized weighted arithmetic (T-SFDPWA) aggregation operators, T-spherical fuzzy Dombi prioritized arithmetic, T-spherical fuzzy Dombi prioritized geometric, and T-spherical fuzzy Dombi prioritized weighted geometric (T-SFDPWG) aggregation operators are given. Then, the basic properties of T-SFDPWA and T-SFDPWG aggregation operators are investigated and discussed in some of their cases. The investigated operators based on T-SFS are utilized in the environment of multiattribute decision-making problems to find the consistency and proficiency of the developed operators. In last, we illustrated some numerical examples based on explored operators is to examine the reliability and validity of the investigated operators with the help of comparative analysis and graphical representations with some existing ideas to show the presented approaches are extensive powerful.

To cope with awkward and unreliable information in daily life issues, the theory of interval-valued picture fuzzy set (IVPFS) is a useful idea to manage unpredictable and vague information. An IVPFS contains the degree of truth, abstinence, and falsity in the finite subset of a unit interval with a rule that is the sum of the upper parts of all degrees is cannot exceed from unit interval. In this study, to find the interrelationships among any number of IVPFSs, we examined the interval-valued picture fuzzy frank averaging operator, interval-valued picture fuzzy frank weighted averaging operator, interval-valued picture fuzzy frank weighted geometric operator and discussed their properties. Moreover, some special cases of the presented operators based on IVPFSs are also discovered. The analytic hierarchy process (AHP) by using the IVPFSs is also explored and discussed in their different aspects. Finally, a multiattribute decision-making procedure is investigated by using proposed operators based on IVPFSs. We illustrated some numerical examples based on explored frank aggregation operators and AHP methods to examine the feasibility and validity of the presented approaches. The comparative analysis, geometrical representations, advantages of the investigated approaches are also discussed.

The inappropriate dumping of agricultural residues (ARs) can result in environmental pollution and the waste of valuable energy resources. The process of converting ARs to energy has been considered an important step for regional energy, agricultural development, and environmental sustainability and recently, many sustainable bioenergy technologies (BETs) have been developed for ARs. Since the assessment of ARs-to-energy conversion technologies contains several alternatives concerning multiple criteria with imprecise information, it is deliberated as an uncertain multicriteria decision-making (MCDM) problem. The Pythagorean fuzzy set (PFS) is an important and effective way to tackle the uncertainty present in real-life decision-making problems. To select a suitable conversion technology from a set of options and upgrade the ARs-to-energy industries, the present study develops a combined approach to PFSs based on weighted discrimination-based approximation (WDBA). This method extends the classical WDBA approach using an improved score function and discrimination measure within the PFS context, to evaluate MCDM problems with partial information on the criteria weights. To estimate the weights of the unknown attributes, a score function-based linear programming model is developed. A new ranking method is extended to grade the options using the proposed discrimination measure within the PFS environment. Further, a case study assessing ARs-to-energy conversion technologies is conducted to illustrate the practicality and feasibility of this method. A comparative analysis shows that the approach developed is more effective and proficient in facilitating decision experts' selection of desirable BETs for ARs.

The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision-makers and experts to adopt a fuzzy-based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision-making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t-conorms and t-norms have the same properties as those of the general t-conorm and t-norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision-making environment with uncertain conditions and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multiattribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy-neutrosophic-based approach for resilient supplier selection. The main contributions of this study work are the design, implementation and analysis of a multiattribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the proposed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.