This study introduces an integrated decision-making framework for Construction Service Provider (CSP) selection, addressing limitations in traditional methods that prioritize cost over quality and holistic evaluation. The framework integrates “Hyperbolic Fuzzy Sets (HYFS)” to capture uncertainties in expert opinions, a variance method to weigh expert reliability, the “Logarithmic Percentage Change-driven Objective Weighting (LOPCOW)” method to determine criterion weights, and the “Weighted Aggregated Sum Product Assessment (WASPAS)” algorithm to rank CSPs. A case study involving five construction companies and fifteen criteria was conducted to validate the proposed framework. The framework proposed in the study is able to effectively rank the CSPs, demonstrating practical utility in selecting an optimal CSP considering both qualitative and quantitative factors. Sensitivity analysis showed the framework is robust to changes in criterion weights. It is to be noted that, “Experience”, “Quality of work”, and “Technology and Innovation” emerged as the top three categories of criterions influencing the selection process. The study contributes to the literature by introducing usage of HYFS to CSP selection problem, explicit computation of expert weights based on variance, LOPCOW for criterion weights, and an integrated HYFS-Variance-LOPCOW-WASPAS framework. The study offers a practical tool for stakeholders to move beyond cost-centric bidding, promoting fairness, efficiency, and accountability in project selection and various other decision-making contexts.

The research introduces a credible decision-support tool for dealing with uncertainty for supplier selection in the aerated concrete industry. The developed framework includes criteria selection, determination of expert and criterion weights, and alternative ranking within a hyperbolic fuzzy environment. Criteria selection is done via the Laplacian score. Expert weights are methodically determined via entropy measure. Criteria are weighted using the LOPCOW and RANCOM methods, and alternatives are ranked using the hyperbolic extension of the DEPART method. The model is employed to solve a circular supplier selection problem in the aerated concrete industry. Comprehensive sensitivity and comparison checks are conducted.

This study presents a novel hybrid methodology for multi-attribute group decision-making (MAGDM) under un certainty using interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). Each alternative is evaluated in terms of interval-valued membership and non-membership degrees with respect to multiple attributes, allowing a richer representation of expert assessments and inherent hesitation. To objectively determine the influence of decision-makers, correlation coefficients are computed based on deviations from upper and lower outlier assessments. A key feature of the approach is an individualized linear programming (LP) model solved separately for each al ternative. This enables the optimization of attribute-wise membership and non-membership values to derive the best achievable score for each alternative under its most favorable attribute weighting, an approach analogous to efficiency estimation in data envelopment analysis. To validate the rankings and integrate attribute importance, an enhanced CRITIC-TOPSIS method is employed. CRITIC captures contrast intensity and inter-criteria correla tion to derive objective attribute weights, while TOPSIS ranks alternatives based on proximity to ideal solutions. The applicability and robustness of the proposed method are demonstrated through a sustainable supplier se lection problem considering economic, environmental, and social dimensions. Sensitivity analysis, comparative evaluation, and Copeland rank fusion further confirm the consistency and reliability of the decision outcomes.

Climate change is a pressing issue globally, and countries are implementing zero-carbon measures (ZCMs) to combat it. The ranking of such measures is essential, as it facilitates policymakers in preparing action plans for sustainability. As a variant of the hesitant fuzzy set (HFS), the probabilistic hesitant fuzzy set (PHFS) is utilized, which considers both hesitancy during rating and the associated confidence level for each value. Logarithmic percentage change-driven objective weighting (LOPCOW) and simple weighted sum product (WISP) are cuttingedge weighting and ranking methods for multi-criteria decision-making (MCDM), respectively. This study aims to rank ZCMs to achieve SDG13 via a novel integrated LOPCOW-WISP multi-criteria method with PHFSs. So far, there has been no work on developing LOPCOW and WISP methods under PHFSs, and no PHFS-driven research has evaluated ZCMs. Regarding the findings, "sustainable agriculture" is the foremost measure, followed by "research and innovation" and "decarbonizing industry." Sensitivity and comparison analyses are further conducted to realize the method's robustness. The findings can also help to shape the practical and future end-use vision for energy resources, allowing for significant advantages while incurring zero-carbon emissions costs. Policymakers can readily use this framework in logical decision-making processes. Furthermore, the study proposes an ideal measurement method that prioritizes technological, agricultural, and research and development factors, along with concerns like energy efficiency, to achieve the goal of zero-carbon emissions.

This paper presents a novel framework for prioritization of green batteries by considering sustainability criteria encompassing socio-economic-environmental-technical aspects. Previous studies on green batteries bring out two questions — what is the importance of sustainable criteria in ranking green batteries and what is the priority of green batteries based on user demand. Since the answers to these questions are subtle, we gain motivation and present an integrated hyperbolic fuzzy based decision framework that methodically calculates experts weights, criteria weights, and customized ranks of batteries. Sustainable criteria considered in this paper are obtained from literature and expert advice as energy density, lifespan/cycle stability, efficiency, chargedischarge rate, availability, recyclability, scalability, temperature sensitivity, cost, and environmental impact of materials. Qualitative rating is interpreted as hyperbolic fuzzy value, followed by variance method is applied for determining experts weights, weighted CRITIC is presented for criteria weights, and query-based rank algorithm is put forward for customized ranking of batteries. Requirement from stakeholders in terms of criteria set is obtained and it is embedded in the CoCoSo formulation for gaining customized ranks. Practicality is demonstrated by considering a case example along with scenario-based query gathering from stakeholders that led following inferences viz., charge/discharge rate and lifespan being top two criteria with Lithium Iron Phosphate and Nickel Metal Hydride batteries being top two batteries. Finally, the pros and cons of the developed framework is presented for aiding policymakers choices.

Sustainable hospitals require careful selection of green building materials (GBMs) to reduce eco-impact and improve resilience. This is crucial as hospitals consume significant resources and their material choices influence durability, availability, and indoor air quality. Existing studies did not model uncertainty effectively, calculate experts’ weights methodically, and determine personalized/combined ranks of GBMs. This paper aims to address these gaps by developing an integrated decision framework that evaluates factors/criteria, assigns importance values, and ranks GBMs systematically. The methodology combines hyperbolic fuzzy data with attitudinal variance, LOPCOW, and choice-based WISP methods to determine experts’ weights, factor importance, and material grades. The proposed rank algorithm produces both personalized and cumulative grades. Results show durability, material availability, and indoor air quality as the top three factors, with hempcrete, cross-laminated timber, and rammed earth as the leading GBMs. This framework contributes by offering stakeholders a rational, uncertainty-resilient tool for sustainable hospital design.

Solar is seen as a potential clean energy solution as world leaders shift energy sources to meet sustainability goals and country demand. Site selection for solar farms is a crucial and complex decision problem involving multiple attributes. Previous studies on site selection (i) could not model natural language preferences; (ii) did not effectively capture expert hesitation; (iii) were prone to the subjective biases of experts; (iv) involved indeterminate weighting of hybrid attributes; and (v) lacked personalized ranking of farms. Motivated by these gaps, this study proposes an integrated decision-making approach using a double-hierarchy hesitant fuzzy context. Firstly, experts' weights are methodically derived via a regret/rejoice measure. Secondly, attributes' weights are determined via an evidence-based rank sum strategy. Thirdly, an algorithm to rank solar farms is formulated with a combinative distance-based assessment (CODAS) and the Copeland scheme. Lastly, a case example of sites from India exemplifies the approach's usefulness. Sensitivity analysis and comparison reveal the pros and cons of the developed approach. Results infer that Vellore and Ramanathapuram are two potential locations where a solar farm can be installed, and annual solar irradiation, technology, distance from river, sunshine hours, and land cover are the top five attributes that aid in solar farm location selection.

Location selection for underground natural gas storage is a multifaceted decision-making problem, as diverse factors are involved. Earlier studies on location selection for natural gas faced challenges such as uncertainty handling, methodical estimation of experts' reliability, capturing hesitation during factor significance calculation, and personalized location ordering. Therefore, the present work develops a novel integrated weighted aggregated sum product assessment (WASPAS) methodology with generalized (q-rung orthopair) fuzzy information, considering three dimensions of uncertainty: membership grade, hesitancy grade, and non-membership grade, with a flexible window allowing experts easy preference articulation. The reliability of experts is calcu-lated using the Cronbach measure, and the importance of the criteria is computed based on the regret factor. A ranking algorithm is developed with a modified weighted aggregated sum product assessment formulation and choice vector to obtain personalized ordering of natural gas locations. The usefulness is illustrated using a case study of location selection for underground natural gas storage in India. Results show that, political acceptance the most crucial indicator when selecting an optimal underground storage location for natural gas. The outcomes concluded that the introduced integrated framework (i) is robust, even after alterations are realized for the weights of the criteria and strategy values, (ii) produces rank orders that are consistent with the earlier models, and (iii) yields broader rank values, to support better discrimination of alternative locations and appropriate backup management compared to the extant model. Finally, the benefits, shortcomings, and implications are discussed. The model introduced can be a novel guide for natural gas location selection and can aid investors in planning their investments.

This study examines energy efficiency investments in smart manufacturing. For this purpose, a hybrid decision framework is put forward within the double hierarchy linguistic context. The hybrid model encompasses methods for rational determination of decision parameters, viz., experts' weights, criteria weights, and ranks. The research gap in methodically determining experts' weights, along with considering the interdependencies among experts, is addressed by DHHFL-CRITIC, which utilizes correlation and variance measures to overcome this challenge. Likewise, the gap of fluctuations or spikes in preferences owing to extreme scale values is nullified through DHHFL-LOPCOW, which uses log functions to smooth spikes. Finally, the computational complexity gap during rank determination is circumvented by DHHFL-OPARA, which eliminates the normalization procedure, thereby reducing the complexity during rank determination. These methods together form the framework, and the framework analyzes investment options in energy efficiency. The findings of this study have important implications for both theory and practice. In the theoretical sense, a new model is proposed to contribute to the literature. Thanks to the latest model, reliable results can be obtained. In practical terms, a set of criteria affecting energy efficiency in smart manufacturing processes is defined, and the most optimal strategies affecting energy efficiency in these processes are identified. The analysis results with the new model show that the most influential criteria are maintenance and idle time, along with the green building design and energy monitoring system being the key options for investments to support energy efficiency with smart manufacturing.

It is critical to determine which factors impact more smart grid investments and which smart grid investment policy is more suitable for renewable energy projects. Nonetheless, a limited amount of research has focused on this topic, meaning a new study is needed to fill this gap and aid in making decisions under ambiguities. Thus, this research proposes a novel fuzzy group decision-making framework. Twelve drivers are examined through the fuzzy weighted decision-making trial and evaluation laboratory (F-DEMATEL-W) methodology. Subsequently, four smart grid investment policies are ranked using fuzzy weighted aggregated sum product assessment (F-WASPAS). Hence, one of the novelties of this research is the proposal of a robust decision-making tool named F-DEMATEL-W-WASPAS. Other novelties are: (i) the importance of the indicators/criteria is methodically determined by considering pairwise interactions and weights of experts; (ii) both individualistic expert-driven weight vector and cumulative weight vector of indicators are determined; (iii) alternative policies are ranked with minimum decision parameters; (iv) drivers that are crucial for the effectiveness of smart grid investment are determined with their causal relationship, and (v) smart grid investment policies are ranked reliably. The findings demonstrate that cyber security, sufficient legal procedures, and financial viability are the foremost drivers to increase the effectiveness of smart grid investments. Moreover, encouraging sustainable energy production using financial incentives is the foremost policy, followed by exchanging surplus electricity for the system owners. The work may contribute to the ongoing discussion on designing smart grid investment policies for renewable energy projects.

Waste treatment transforms waste into valuable resources, addressing environmental challenges while supporting sustainable practices in energy recovery, resource management, and pollution control. This study addresses the selection of appropriate “Food Waste Treatment Method” (FWTM), an important component in sustainability that requires effective management. The existing models of FWTM selection have some limitations that include (i) inadequate handling of uncertainty, (ii) insufficient systemic determination of experts’ importance, and (iii) lack of customized ranking based on user preferences. To address these gaps in selecting FWTM, this study proposes an integrated and personalized “Multi-Criteria Decision-Making” (MCDM) framework. In this framework, “q-Rung Orthopair Fuzzy Set” (qROFS) has been utilized to manage data uncertainty since it presents the ratings of FWTM based on various criteria as a tuple containing a degree of preference and non-preference. To systematically determine the expert weights and criteria weights, “Linear Programming” (LP) and “LOgarithmic Percentage Change-driven Objective Weighting” (LOPCOW) have been used, respectively. A novel extension of “COmplex PRopotional ASsessment” (COPRAS), a query-based COPRAS, has been used in this framework. This extension of COPRAS offers adaptability and personalization in the selection of FWTM since it also considers the user’s preference for the kind of FWTM based on the user’s requirements and specifications. A case study is also presented, which helps understand the model’s applicability. Notably, from the selected FWTMs, the model identified anaerobic digestion as an optimal FWTM, followed by incineration and heat-moisture reaction. Sensitivity and comparative analyses are performed to understand the strengths and weaknesses of the model. Results from the sensitivity analysis of queries infer that there is a strong effect of query vector(s) on the ranking of FWTMs. As the number of queries increases, rank orders at certain positions stabilize, indicating that the demand from multiple sources converges. Further, it is noted that anaerobic digestion is highly preferred, and composting is less preferred in this study. The global contribution of this study lies in providing a dynamic, context-sensitive framework for selecting food waste treatment methods, enabling optimized decision-making tailored to diverse environmental, social, and economic scenarios.

This paper primarily focuses on grading barriers that hinder internet-of-things (IoTs) adoption, which eventually promotes sustainable supply chain execution. As countries globally plan to combat climate change, supply chain sustainability is substantial. Digital technology, such as IoT, supports sustainability within supply chains. Still, studies infer that the adoption could be more direct and involve barriers that must be graded for efficient implementation and planning. Previous barrier grading models (i) did not accept natural language-based ratings; (ii) subjective orientation of experts’ weights is not well explored; (iii) hybrid determination of attributes’ weights is lacking; and (iv) personalized grades for barriers are also unexplored. Motivated by these gaps, this article develops an integrated model by considering preferences in the natural language form via double hierarchy fuzzy data (DHFD). Later, the rank sum (RS) approach is presented for determining the weights of experts, and the RS-Cronbach factor is put forward for the hybrid weight calculation of attributes. An algorithm to grade barriers is proposed based on WISP formulation combined with the Copeland method. Finally, a case example from Coimbatore is presented to understand the framework’s usefulness, and sensitivity/comparison reveals the pros and cons of the framework.

This paper presents a novel combined decision approach for the rational selection of solar panels (SPs) to facilitate investment in line with Sustainable Development Goal (SDG) 7. SPs are crucial in solar energy harvesting, and earlier studies on SP selection suggest that uncertainty is not adequately modelled. The interrelationships between criteria and the importance of experts are essential, but personalized grading of SPs is lacking. Driven by this claim, this work develops a decision approach using the variance method, the CRITIC method, and a simple rank procedure with the Copeland strategy for determining expert and criterion weights and grading SPs in both personalized and combined fashion, respectively, with hyperbolic fuzzy data. A case example of SP selection reveals the usefulness of the approach. Results infer that monocrystalline silicon SP is highly preferred with a potential focus on material availability, cost, efficiency, and eco-impact. Additionally, the framework is robust to weight alterations, and the developed framework supports policymakers in their action plans, promoting the adoption of sustainable energy.

This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

Necessary actions should be taken to improve renewable energy investments to minimize the carbon emission problem. In this process, the most significant determinants should be identified for some reasons, such as using human and financial resources more effectively. However, there are limited studies in the literature that prioritize the analysis of these items. This situation can be accepted as a missing gap in the literature. Accordingly, this study evaluates sector-wise investment decisions in renewable energy projects. To do so, a novel integrated q-rung orthopair fuzzy set (q-ROFS) decision-making model has been generated. Firstly, the weights of the theory of the solution of inventive problems (TRIZ)-driven criteria are computed via the q-ROF decision-making trial and evaluation laboratory (DEMATEL) methodology. The second stage of the proposed model consists of selecting the most appropriate investment alternatives with the help of the q-ROFS-based simple ranking process (q-ROF SRP). The main contribution of this study is that key sector-wise investment decisions in renewable energy projects can be identified by establishing a novel decision-making model. The main superiority of the proposed model is that the DEMATEL method is extended to the q-ROFS context to determine the weights of the factors. With the help of this issue, uncertainties and subjective randomness in the decision-making process can be minimized. In addition to this situation, causal directions between these indicators can be taken into consideration for this condition. The findings indicate that possible extension with modularity is the most critical indicator for this situation. Similarly, resource efficiency is also found to be the most influencing item. In addition to them, the ranking results demonstrate that waste-to-energy technologies and energy storage systems are the most critical investment alternatives.

Access to quality education for girls is one of the key targets of the United Nations’ Sustainable Development Goals. This study has two major goals. First, this study proposes a hybrid decision framework that integrates hyperbolic fuzzy sets, multi-attitudinal variance, soft cluster rectangle, and integrated simple weighted sum product methods to prioritise strategies that improve access to quality education for girls. Second, this study explores the use of generative AI expertise in a multi-criteria decision-making framework that traditionally uses only human experts. The findings reveal "Effectiveness" to be the most important criterion when assessing the strategies and "Health and Hygiene Interventions" to be the most prioritised strategy. The findings also show a divergence of opinions between the AI experts and human experts in the extremes and alignment of opinions in the non-extremes. The study serves as a foundation for future research on integrating generative AI into decision-making frameworks while also providing policymakers with a structured approach to prioritising strategies for improving girls’ education.

The paper attempts to mitigate disputes during drafting of a construction contract by presenting a decision framework. The research questions considered are to set the main criteria involved and their relative importance in contract clauses selection and evaluate the priority of different contract clauses. In response, the paper presents an integrated framework involving hyperbolic fuzzy data, CRiteria Importance Through Intercriteria Correlation (CRITIC) method for criteria weight calculation, and query-based Weighted Aggregated Sum Product ASsessment (WASPAS) method for determining personalized priority of contract clauses. Results infer that work termination, customer reserve, guarantee periods and responsibilities of contractor/customer are the key criteria, and contract under the Federation Internationale des Ingenieurs-Conseils (FIDIC) Yellow Book is of top priority. Such integrated framework serves as supplement to contractors and customers for prompt and rational decisionmaking by reducing human intervention, managing uncertainty, and reducing bias/subjectivity. In the future, plans are made to include a priori information into the decision framework.

Appropriate selection of solar panels is significant to facilitate solar power generation. With the help of this situation, it can be possible to minimize many problems, such as high maintenance and repair costs and lack of efficiency. However, there are very few studies in literature that determine the most importance of these factors. Accordingly, this study makes an evaluation related to the prioritization of solar panels to facilitate solar power generation. For this purpose, a new model has been established by combining different techniques. First, the weights of experts are determined through statistical variance method and selected criteria are weighted via q-ROF-CRITIC methodology. After that, solar panel types are ranked by using q-ROF-CRADIS method. The main contribution of this study is that a comprehensive analysis can be performed to generate an appropriate priority to select the viable solar panels with a novel framework. Regarding the methodological contribution, the use of CRADIS technique in the ranking of alternatives enhances the resemblance to human-driven decision making. Similarly, the biggest advantage of CRITIC is that the interactions among criteria are considered, and the proposed ranking algorithm enables choice-based ranking of solar panels that provides a sense of personalization during prioritization. The findings indicate that temperature coefficient is the most important factor to select the appropriate solar panel types. Similarly, availability also plays a key role in this context. On the other hand, polycrystalline silicon solar panel and thin film solar panel are found appropriate for the investment within the solar energy market.

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel ‘F’ Flag feature for early detection. This novel ‘F’ indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN ‘F’ Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the ‘F’ indicator, resulting in the highest specificity of + 7.34%.

Health 4.0 is gaining significant attention globally to support better treatment and care for people. Digital technologies such as the IoMT "("Internet of Medical Things"") and blockchain substantially promote quality health services. Literature shows that embedding blockchain in IoMT is an effective way to attain secure and quality healthcare. Selecting a viable blockchain service provider (BSP) becomes a complex task and can be considered an MCDM "("Multi-Criteria Decision-Making"") problem. Earlier studies on BSP selection indicate that complex expressions cannot be well-modelled and methodical estimation of decision parameters by capturing hesitation and interaction of entities is not adequately explored. Driven by the issues, authors put forward a novel MCDM framework with (i) a double hierarchy linguistic structure for data collection in natural expressions; (ii) regret measure for experts' reliability calculation; (iii) a weighted CRITIC approach for criteria weight determination, and (iv) CRADIS-Copeland algorithm for ranking BSPs. Finally, a case example from Recent Indian healthcare is exemplified to demonstrate the framework's applicability. Recently, the Indian healthcare sector launched initiatives/plans as part of Health 4.0 to promote data management for a better quality of treatment by seeking support from digital technologies such as IoMT and blockchains to ensure data privacy and improved customer experience. Sensitivity analysis and comparison with extant methods infer that (i) BC1, BC5, and BC3 are the top three BSPs for the considered problem; (ii) criteria such as privacy aspects, intercommunication capability, global accessibility, and total cost constitute 58% importance in the supposed decision problem; (iii) developed model reduces human intervention/biases; (iv) developed model is robust to weight alteration and yields unique rank orders with both personalised and cumulative ordering of BSPs; and (v) proposed model produces broader rank values that aid in effective backup planning.