Autonomous vehicle robots operate in highly dynamic and uncertain environments where accurate risk evaluation is essential for safe and reliable performance. Traditional decision-making models often struggle to handle ambiguous sensor inputs, conflicting criteria, and multi-dimensional uncertainty, which may reduce decision precision and operational safety. To overcome these limitations, this study proposes an advanced AI-based risk assessment framework founded on Circular Complex Spherical Fuzzy Sets (CrC-SFSs). The proposed model integrates the WAPAS and CODAS multi-attribute decision-making methods to enhance robustness and ranking reliability under complex uncertainty conditions. The CrC-SFS environment enables a flexible representation of membership, non-membership, and hesitancy information in a circular complex domain, allowing improved modeling of uncertain and imprecise risk factors. Furthermore, novel weighted and ordered weighted aggregation operators are developed to effectively combine multiple risk criteria. Comparative and experimental analyses demonstrate that the proposed CrC-SFS–WAPAS–CODAS framework significantly improves decision confidence, ranking stability, and uncertainty management compared to existing fuzzy-based approaches. The findings confirm that the proposed model provides a powerful and intelligent tool for next-generation AI-driven risk assessment in autonomous vehicle robotic systems.

Sustainable development of the Chinese education sector demands a comprehensive investment strategy that integrates public-private partnerships, early childhood education, community engagement, teacher capacity building, curriculum relevance, equitable resource distribution, and continuous quality monitoring. Such an inclusive framework strengthens human capital and supports long-term economic growth. To effectively manage uncertainty and vagueness in multi-attribute group decision-making (MAGDM) problems arising from such investment evaluations, this study aims to develop novel aggregation operators within the complex q-rung orthopair fuzzy (Cq-ROF) environment. For this purpose, newly defined algebraic complex operational laws are introduced to construct the complex q-rung orthopair fuzzy weighted arithmetic mean (Cq-ROFWAM), ordered weighted arithmetic mean (Cq-ROFOWAM), weighted geometric mean (Cq-ROFWGM), and ordered weighted geometric mean (Cq-ROFOWGM) operators. These operators provide a flexible and robust framework for aggregating uncertain and complex evaluation information while effectively capturing decision-makers' preferences. Based on these operators, a novel MAGDM model is developed to support investment assessment in the Chinese education sector. An illustrative case study demonstrates the applicability and practicality of the proposed approach. Furthermore, comparative and numerical analyses verify that the proposed model produces stable and consistent alternative rankings, accurately identifies the optimal investment strategy, and offers reliable decision support. The main contributions of this work include the introduction of new Cq-ROF aggregation operators based on algebraic complex operational laws, the development of a comprehensive MAGDM framework, and the provision of a flexible and reliable decision-making methodology that outperforms existing approaches in handling uncertain information.

In fuzzy decision environments, the three-way multi-attribute decision-making (3 W-MADM) framework offers a theoretically grounded and risk-averse paradigm for formulating renewable energy policies aimed at mitigating energy crisis challenges. Current research, however, reveals a number of difficulties, such as the impact of subjective factors, subjective conditional probability, risk avoidance coefficients and high levels of redundancy in 3W-MADM. In order to address these issues, we develop a novel 3W-MADM model with objective risk avoidance coefficients that is based on q-rung orthopair fuzzy preference relations (q-ROFPRs). Reducing subjective biases, objective conditional probability, and low levels of redundancy in 3W-MADM are the objectives of the study. First, we represent uncertain evaluation information in group decision-making processes by introducing a new type of fuzzy preference structure termed q-ROFPRs from multisource fuzzy information. Secondly, we suggest a novel approach to conditional probability calculation based on a quasi-overlap function and risk avoidance coefficients in order to minimize the impact of subjective factors on decision-making. Third, we enhance the 3W-MADM paradigm by adding behavioral and psychological components by integrating the regret and prospect theories, which are the sources of the relative loss and utility functions. This improvement allows the model to more accurately reflect decision-makers' dispositions toward risk-taking, loss aversion, and regret sensitivity. Then, using the q-ROFP environment, a 3W-MADM approach is constructed under the aforementioned objective conditional probabilities, relative loss function and relative utility function. To confirm the practicality of the suggested method, we apply it to a renewable energy policy choice problem for energy crisis management. In terms of decision-making capability, the suggested strategy outperforms a number of different approaches that have been reported in the literature. We improve the 3W-MADM framework by incorporating behavioral and psychological elements, which enables the model to better capture the risk-seeking inclinations, loss aversion, and regret sensitivity of decision-makers. The suggested method's robustness and dependability are confirmed by statistical analysis using Spearman rank correlation, which supports its possible use in managing sustainable energy crises.

Granular-ball computing is a potent paradigm for managing uncertainty because it allows for both interpretability and flexibility in data processing. Granular-ball computing automatically removes noise and preserves local consistency while capturing data structures at multiple granularity levels, unlike traditional fuzzy rough set methodologies that only employ pairwise fuzzy connections. This study introduces a novel fuzzy multi-granularity granular-ball decision-theoretic rough set (FMG-GB-DTRS) framework to address noise, uncertainty, and adaptability challenges in three-way decision models. To ensure data integrity and local structural consistency, a granular-ball methodology is first constructed using a purity mechanism derived from fuzzy β-co-neighborhoods provided by experts. A granular-ball fuzzy neighborhood framework is created to improve neighborhood representation by giving samples from different granular-balls zero similarity, maintaining significant local connections while removing noise. Expanding upon these frameworks, we provide a thorough noise-handling system that functions cooperatively at three levels: multi-granularity decision approximations controlled by α and δ, purity-constrained granular structures, and expert-provided β-co-neighborhood similarity relations. By utilizing fuzzy sets, granular-ball adaptivity, and the DTRS optimization principle, we develop a strong three-way decision paradigm that can produce dependable, adaptable, and risk-aware decisions in intricate, noisy settings. Finally, experiments are executed out on nine data sets to verify the efficacy of the suggested method, and the outcomes are contrasted with seven existing techniques. Several assessment criteria, including Accuracy, Precision, Rand Index, Recall, and F1-Score, are used to assess the performance of the suggested approach. Additionally, robustness analysis is carried out at various noise ratios from 10% to 40%. The experimental findings show that for large-scale data decision-making challenges, the suggested approach delivers competitive and steady performance.

Rank reversal is a phenomenon that can occur with various proposed decision-making methods when the rank order of alternatives changes by the inclusion or removal of an uninformative alternative. This survey provides a trend analysis of five widely used decision-making methods that are subject to rank reversal: (i) the Analytic Hierarchy Process (AHP), (ii) the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), (iii) the Preference Ranking Organisation METHod for Enrichment Evaluations (PROMETHEE), (iv) the & Eacute;Limination Et Choix Traduisant la REalit & eacute; (ELECTRE), which means Elimination and Choice Translating Reality and (v) the VIse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), which means: Multicriteria Optimization and Compromise Solution in Serbian. The trend analysis also segments the literature based on three categories: (i) literature that proposes a modified procedure of a decision-making method to correct for rank reversal; (ii) literature that identifies the root cause of rank reversal within a method and (iii) literature that evaluates a proposed method based on its potential for rank reversal. The first observation of this paper is that despite the importance of choosing a method for decision-making that avoids rank reversal, and despite several publications on the effects of this issue, applications using methods prone to rank reversal continue to be widely used. Further, by tracing historical publication trends across the three categories, this paper shows how rank reversal research has developed over time, with research on correcting rank reversal (Category 1) remaining steady and dominant, while root-cause analysis of rank reversal within a method (Category 2) and evaluative work (Category 3) are growing. The survey ends by highlighting other methods that are prone to rank reversal but have not had sufficient literature drawing attention to their susceptibility to this issue. Examples include the Min-Max Regret method and the Characteristic Objects Method (COMET) of decision-making. We hope that this work draws further attention and sensitivity to the implications of rank reversal in newly proposed and existing decision-making applications and that it would enable a discussion that would be beneficial to various researchers and practitioners in the broader field of decision-making.

The three-way decision (3WD) model has gained widespread application in multi-attribute decision-making. However, existing models often neglect the variability in decision-makers minimum acceptance levels and risk attitudes across different criteria. With growing complexity and uncertainty in decision contexts, accurately capturing evaluation values remains a key challenge. In this paper, we address uncertainty in multi-attribute decision-making by introducing a novel type of fuzzy preference relation and developing collective fuzzy preference relations derived from multisource fuzzy information. To reduce the influence of subjective factors, we propose a new approach for calculating conditional probabilities based on collective fuzzy preferences and attribute weights. Furthermore, we formulate relative utility and relative loss functions within the optimistic three-state 3WD model, grounded in Prospect-Regret theory, and implement the model using Python. We also examine the threshold characteristics arising from psychologically perceived values in the optimistic three-state 3WD framework. In addition, we present a classification method for alternatives using the three-state 3WD model, with its implementation detailed in Python. To demonstrate the feasibility and practical value of the proposed approach, we apply it to the problem of evaluating the impact of interactive learning for blind students. The recommended methodology performs better with regard to decision-making capacity than several other methods that have been established in the existing literature.

Multi-criteria evaluation and financial sustainability analysis in stock markets often involve uncertain and imprecise information, which requires advanced decision-making models. In this paper, we discuss the concepts of Circular Complex Picture Fuzzy Sets (CrC-PiFS) for handling uncertainty in financial assessments. The circular complex T-spherical fuzzy set is an extension of the complex picture fuzzy set, complex spherical fuzzy set, and complex T-spherical fuzzy set. We define improved algebraic operations for CrC-PiFS, including direct sum, direct product, and scalar multiplication, based on t-norms and t-conorms. The aim of this study is to enhance the representation of uncertainty in multi-criteria stock-market decision-making. To achieve this, we introduce circular complex picture fuzzy weighted/ordered weighted arithmetic mean and geometric mean aggregation operators under a new class of algebraic circular complex T-spherical fuzzy operational laws, and discuss their properties. Finally, we present a novel decision-making framework incorporating the CRITIC–WASPAS method and highlight its applicability to stock-market analysis, particularly in evaluating and prioritizing market stability factors, investment strategies, and financial sustainability indicators.

The purpose of this study is to present an emergency decision making (EDM) technique based on a fuzzy rough set model. In order to build fuzzy rough approximations, researchers in literature use the concept of fuzzy similarity relations. As far as we are aware, there is currently no study approach for fuzzy rough set models based on ( β ^,δ ^⧫) -fuzzy similarity connections when β ^∈[ 0,0.5) , and δ ^⧫∈( 0.5,1] are involved. This study intends to extend the idea of Pawlak’s rough sets to the so-called α -fuzzified multigranulation rough sets based on ( β ^,δ ^⧫) -fuzzy similarity relations in order to address this research area. Additionally, the approximation created using α -fuzzified multigranulation rough sets plays a crucial part in the relationship between ( β ^,δ ^⧫) -fuzzy similarity relations and crisp set. Moreover, the approximation created on the basis of α -fuzzified multigranulation rough sets utilising ( β ^,δ ^⧫) -fuzzy similarity relations is helpful in understanding various uncertainties and their interrelationships. Also, we explain about the linkages between the novel fuzzy rough approximation operators and the multigranulation ( ℐ_O,O) -fuzzy rough set model that was constructed using quasi-overlap functions. Then, utilising a multigranulation ( ℐ_O,O) -fuzzy rough set and ( β ^,δ ^⧫) -fuzzy similarity relations, we develop three techniques for manipulating unpredictable issues. We also go through how to use the suggested ways to compare them to other current models in order to determine which conditional attribute is best for emergency plans from the ones that are mentioned.

The term green building refers to the use of state-of-the-art scientific and technological methods to design buildings that have the least potential adverse environmental impact. To assess possible green construction choices, a comprehensive analysis is required, taking into account a number of aspects, many of which may be incompatible. A novel category of fuzzy sets, known as circular linguistic T-Spherical fuzzy sets, is presented in this article. It is founded on the circular linguistic T-Spherical fuzzy sets and linguistic variables can be used to characterize the qualitative evaluation of decision makers while giving them greater latitude in expressing their opinions regarding admissible membership grades. Secondly, we introduced several circular linguistic T-Spherical fuzzy Hamy mean aggregation operators, including circular linguistic T-Spherical fuzzy Hamy mean/weighted Hamy mean aggregation operators and their duals within the setting of an entirely novel algebraic circular linguistic operational laws. Meanwhile, we also explain a variety of characteristics that these suggested operators exhibit, as well as particular scenarios that permit parameter adjustments. Next, the CODAS and WASPAS construction frameworks incorporate these aggregation operators, making it simpler to rank and thoroughly assess concepts. Furthermore, we provide a detailed analysis of the recently proposed multi-attribute group decision method based on the circular linguistic T-Spherical fuzzy weighted Hamy mean/dual weighted Hamy mean aggregation operators. We test the viability of the approach by applying the suggested methods to issues pertaining to the creation of green buildings and sustainable architecture. Regarding decision-making skills, the proposed method performs better than several other methods that have been explored in the literature. This study improves the methods for making decisions about green buildings and sustainable architecture in complicated and unpredictable situations.

Artificial intelligence (AI) tools completely transform the medical field by increasing efficiency, precision, and availability while expanding the field's understanding of medicine and available treatments. However, to guarantee the responsible and secure application of AI technology in healthcare, it is imperative to address issues like data protection, moral concerns, and compliance with regulations. The concept of a p, q-cubic quasi-rung orthopair fuzzy set provides an effective optimization strategy for managing the uncertainty about the use of AI tools in healthcare issues. By adding an additional parameter, the p, q-cubic quasi-rung orthopair fuzzy set enables a more comprehensive and flexible description of the insufficient information. The key objective of the paper is to represent uncertain evaluation information about the use of AI tools in medical industry processes by introducing p, q-cubic quasi-rung orthopair fuzzy weighted arithmetic/geometric mean aggregation operator and p, q-cubic quasi-rung orthopair fuzzy ordered weighted arithmetic/geometric mean aggregation operator under the environment of a new class of algebraic operational laws. Further, to determine the optimum order of each action, which may reflect the inter-correlations among criteria, an additional structure incorporating p, q-cubic quasi-rung orthopair fuzzy assessment of alternatives and ranking based on the TOPSIS method implementing p, q-cubic quasi-rung orthopair fuzzy-criteria importance through inter-criteria correlation (CRITIC) is offered. Additionally, we go into extensive detail on the newly suggested multi-attribute group decision process that is based on the p, q-cubic quasi-rung orthopair fuzzy weighted arithmetic/geometric mean aggregation operators using new class of algebraic operational laws environment. We use the artificial intelligence tools in medical industry problem to further verify the feasibility of the approach. In terms of decision-making capability, the suggested strategy outperforms a number of different approaches that have been reported in the literature.

Classic risk sharing results determine the optimal share of each member in a group that faces a present deal by maximizing the sum of expected utilities of the group members. For decision-makers with exponential utility functions, this formulation is equivalent to maximizing the sum of certain equivalents of the group members. This paper investigates the effects of time preference and different (but constant) risk tolerances among the group members on the individual shares when the payoff is received at a future time period. The analysis first defines several concepts: (i) a group future risk tolerance to be used for valuing the certain equivalent of future payoffs, (ii) a group time preference compounding factor that takes into account the time preference of individuals in the group, and (iii) a group present risk tolerance with time preference by which the partnership should operate for the discounted value of future deals. The results show that if the individuals in a group have the same time preference, then the classic risk sharing results still apply. However, when individuals have different time preferences, then the optimal shares of the individuals are modified by two components; the first depends on the ratio of the individual time preference compounding factor to the group time preference compounding factor, and the second depends on the surety of the deal multiplied by the group future risk tolerance. Several examples illustrate the results.

In this study, we attempt to present a three-way multi criteria decision-making (MCDM) technique with fuzzy soft dominance degree relation based on additive consistency to simultaneously choose the best alternative, rank alternatives, and classify alternatives, which would aid decision-makers in making better decisions. First, we provide the additive consistency based fuzzy soft dominance degree relation. Then, based on the fuzzy soft dominance degree relation, we build the idea of the θ-similarity class and define new relative loss functions with unknown risk coefficient vectors and unknown weights of the alternatives. Further, using θ-similarity classes based conditional probability, we offer a theoretical approach for finding total decision cost to attain the best cost-sensitive granularity selection. Furthermore, we also discuss in detail about the newly proposed three-way MCDM method’s decision making mechanisms. We further check the method’s viability using the emergency plan selection problem. Comparative assessments demonstrate that the proposed approach has a better decision-making function than several other methods available in the literature. Experimental evaluations demonstrate that the proposed technique consistently rank the considered object and chooses the best one. Furthermore, the suggested technique can satisfy the decision-makers’ preferences in the classification choice in addition to offering suitable ranking decision recommendation for decision-makers.

Local rough sets are an efficient model to analyze large-scale datasets with finite labels because they are an essential development in classical rough sets. The objective of this paper, we put forth the idea of a local soft rough approximation measure (LSRAM), which preserves rough approximation measure associated characteristics from the context of traditional rough set theory. In order to account for the uncertainty brought on by the difference between the given lower and upper approximations based on soft equivalence relation, we propose the notion of local soft knowledge distance (LSKD). Moreover, some associated proposition’s, theorems, corollaries, and a novel GM built on the LSKD model are given. Subsequently, LSRAM is combined with the suggested GM to create the enhanced LSRAM. This illustrates that the upgraded LSRAM maintains monotonicity with granularity subdivision. Further, to examine the conflict situation in the Middle East, we create a new conflict analysis model that is based on local soft rough sets in a framework of soft equivalence relations and soft indiscernible relations. Finally, we provided positive responses to a number of queries that different authors had raised. Our recently constructed model is much more effective than the existing strategies, according to an analysis of a general algorithm for conflict problems.

Abstract Current study was carried out to compare two presynch-OvSynch protocols with standard OvSynch protocol in postpartum Holstein dairy cattle. Postpartum cows (n = 473) were randomly divided into one of the three protocols: I) G7G-OvSynch (n = 159), cows were subjected to PGF2α followed by GnRH 48 h later. After 7 d post-GnRH injection, standard OvSynch protocol was introduced, II) G7GM-OvSynch (n = 162), cows were subjected to the same protocol as in group I) except one extra PGF2α at 12 h interval in Ovsynch part, and III) standard OvSynch protocol (n = 152). The progesterone (P4) profile was subjected to d 30 and d 60 post FTAI. Ovarian status was monitored at the 1st GnRH, PG, and 2nd GnRH injection of the breeding part. Pregnancy/ AI was diagnosed on d 30,60, and 90 post-FTAI, and pregnancy loss was also on d 60 and 90 post-FTAI. Pregnancy data were analyzed using the Chi-square test and plasma P4 profile by ANOVA using SAS. Ovulatory follicle diameter (mean ± SD) was 15.44 ± 1.24 in G7G-Ovsynch, 15.21 ± 1.36 in G7GM-OvSynch, and 14.90 ± 1.02 in OvSynch, respectively (P > 0.05). Plasma P4 profile (ng/mL; Mean ± SD) on d 30 post TAI was 6.50 ± 1.39,7.01 ± 1.13 and in 6.49 ± 1.32 G7G-OvSynch, G7GM-Ovsynch and OvSynch groups, respectively (P = 0.09). On d 60 post TAI, P4 profile was recorded to be 7.75 ± 1.20, 7.58 ± 1.36 and 6.80 ± 0.96 in G7G-OvSynch, G7GM-OvSynch and OvSynch, respectively (P < 0.05). P/AI on d 30 was 51.57% (83/159), 57.41% (93/162) and 42.76% (65/152) G7G-Ovsynch, G7GM-OvSynch, and OvSynch, respectively (P = 0.033). Similarly, AI on d 60 and d 90 post FTAI was 44.65% (1/159):51.85% (84/162), 38.16% (58/152): 43.40% (69/159) and 51.23% (83/162):36.18% (55/152) in G7G-Ovsynch, G7GM-OvSynch, and OvSynch protocols (P = 0.051: 0.027), respectively. Pregnancy loss on d 60 and d 90 post FTAI was 13.41%: 2.90%, 9.68%: 1.20% and 10.77%: 5.20% in G7G-Ovsynch, G7GM-OvSynch, and OvSynch, respectively (P = 0.73: 0.35). Although statistically non-significant (P = 0.56), overall pregnancy loss on d 90 post-FTAI was also numerically greater in the G7G-OvSynch (15.85%) protocol as compared with G7GM-OvSynch (10.75%) and OvSynch (15.38%). We concluded that the G7GM-OvSynch protocol has resulted in an increased P/AI, P4 profile in comparison with G7G-OvSynch and OvSynch. Similarly, pregnancy loss was also less in the G7GM-Ovsynch protocol, which makes it a protocol of choice for postpartum dairy cows.

The value of information is an important concept in decision analysis that has been quantified as the buying price (BPI) for the information and the expected utility increase (EUI) obtainable by using the information. These two measures rank information sources identically in a scalar-valued decision problem only when the utility function is linear or exponential. In contrast, this paper focuses on the value of information across scalar-valued decision problems sharing the same utility function such as different divisions within an organization exploring various information sources for their decisions using the same organizational utility function. In this context, it still makes sense to ask which sources are more informative. We show that BPI and EUI rank information sources identically in this context only when the utility function is linear. However, if the certainty equivalent increase is used instead of EUI, then identical ranking with BPI across problems is maintained for the broader class of linear or exponential utility functions. We discuss the importance of these results for distributed decision-making settings, where different departments within an organization may calculate the value of information separately. Our results advise against using EUI to measure information value in this context when risk attitude is important.

Coronary artery disease (CAD) is a serious health problem that causes a considerable number of mortality in a number of affluent nations throughout the world. The estimated death encountered in many developed countries includes including Pakistan, reached 111,367 and accounted for 9.87% of all deaths, despite the mortality rate being around 7.2 million deaths per year, or 12% of all estimated deaths accounted annually around the globe, with improved health systems. Atherosclerosis progressing causes the coronary arteries to become partially or completely blocked, which results in CAD. Additionally, smoking, diabetes mellitus, homocystinuria, hypertension, obesity, hyperlipidemia, and psychological stress are risk factors for CAD. The symptoms of CAD include angina which is described as a burning, pain or discomfort in the chest, nausea, weakness, shortness of breath, lightheadedness, and pain or discomfort in the arms or shoulders. Atherosclerosis and thrombosis are the 2 pathophysiological pathways most frequently involved in acute coronary syndrome (ACS). Asymptomatic plaque disruption, plaque bleeding, symptomatic coronary blockage, and myocardial infarction are the prognoses for CAD. In this review, we will focus on medicated therapy which is being employed for the relief of angina linked with CAD including antiplatelet medicines, nitrates, calcium antagonists, blockers, catheterization, and the frequency of recanalized infarct-related arteries in patients with acute anterior wall myocardial infarction (AWMI). Furthermore, we have also enlightened the importance of biomarkers that are helpful in the diagnosis and management of CAD.

Unmanned Aerial Vehicles (UAVs) are important to perform a wide range of operations. In order to ensure mission success, UAVs need to be agile and to have a robust design having good longitudinal and lateral stability characteristics. In this paper, we have provided a comprehensive and open architecture design scheme for design and development of an unmanned aerial vehicle capable of carrying out various missions. This work presents two different designs of such aerial platform which includes Flight Dynamic Modeling, aerodynamic parameters estimation using high fidelity numerical techniques involving Computational Fluid Dynamics (CFD) and USAF DATCOM. These designs are then subjected to 6 Degrees of Freedom (DOF) simulation environments for assessing their performance in different flight scenarios. A Performance Based optimization (PBO) is then carried out in order to optimize existing design and come up with improved model parameters. These optimized designs are then passed through a stringent selection criteria for selecting a design for development

This paper investigates the effects of constructed scales used to evaluate criteria, and monotonic pertur-bations of those scales, on the ranking of decision alternatives. The analysis focuses on the widely used 'weight and rate' method of multicriteria decision-making, but the findings also provide perspectives on scale dependence in numerous settings. We introduce a simulation method to generate 'weight and rate' decisions uniformly and use simulation to characterize the sensitivity of the obtained rankings to mono-tonic transformations. We define a weight-rate function and cumulative weight-rate function to charac-terize decision alternatives and draw on the results of stochastic dominance to address the sensitivity of the rankings to the choice of the constructed scale analytically for certain classes of decisions. We define absolute, first-order, and second-order weight-rate dominance conditions. We also define a special case where the domain of the weight-rate function of one alternative is contained within the domain of an-other. We show that this special case is especially sensitive to the constructed scale. We also show via simulations that increasing the number of attributes or the number of alternatives increases sensitivity to the constructed ratings scale. The numerical and theoretical results show the ranking of decision al-ternatives is sensitive to the constructed scale, except in limited cases such as absolute and first-order weight-rate dominance. The results provide insights for practitioners and government officials into this widely used method of decision-making and cautions them to the sensitivity of their results to the rating and weighting scales that are used.(c) 2023 Elsevier B.V. All rights reserved.