Rapid urbanisation and increase in private vehicle ownership are straining transportation systems in developing countries like India. At the same time, limited urban land availability makes it difficult to allocate sufficient space for parking and transportation infrastructure. This imbalance could result in certain parking lots becoming congested while others remain underused. A lack of knowledge about parking availability in a district can hinder development and access to economical, educational, and recreational opportunity. The rapid development of autonomous vehicles (AVs) has ignited extensive interest in their potential to modernize mobility and transportation ecosystems, however, selecting the location for the parking lots will become more difficult with the addition of AVs to urban traffic. This study aims to develop decision-making framework to choose the suitable location for the AVs parking lot, where the information about the options and criteria is specified in the form of interval-valued Fermatean fuzzy sets (IVFFSs). In this context, first an improved score function is presented to ordering the interval-valued Fermatean fuzzy numbers (IVFFNs). Second, a distance measure is developed to compute the difference on IVFFSs. Third, some aggregation operators are developed to unite the IVFFNs into a single IVFFN. These operators are developed by combining the Einstein and Interaction operations within the context of IVFFNs. Lastly, a hybrid decision-making methodology is presented by integrating the score function, distance measure, aggregation operators and the COmbinative Distance-based ASsessment (CODAS) approach with IVFFSs perspective. The proposed methodology is implemented to a case study of alternative location selection for AVs parking lot in the transportation sector, which displays its practicality and validity in the context of uncertainty setting. Sensitivity assessment and comparison are discussed to check the utility and stability of the determined outcomes. The outcomes conclude that the proposed framework can offer more feasible decisions while considering several alternatives and criteria with uncertain information.

Inappropriate treatment of electronic waste (e-waste) poses substantial risks to the environment and ultimately leads to negative consequences on human health. Selecting an appropriate strategy for electronic waste management (EWM) is crucial before treating e-waste. This research aims to propose an innovative model for evaluating and prioritizing sustainable EWM strategies based on the most influential criteria. This model combines the relative closeness coefficient-based tool, the step-wise weight assessment ratio analysis tool, and the alternative ranking order method accounting for two-step normalization (AROMAN) within the setting of q-rung orthopair fuzzy sets (q-ROFSs). Based on the proposed score function, the weights of decision experts (DEs) are derived, while the weights of criteria are estimated using an integrated objective-subjective weighting approach. For the relative closeness coefficient-based objective weighting tool, a novel divergence measure on q-ROFSs is developed, which describes the discrimination between them. By combining these steps, an integrated AROMAN is presented to critically assess EWM strategies, demonstrating its practicality and efficacy through a case study of the EWM strategy selection problem. The findings of the integrated weighting model indicate that the criterion “quality management systems” is the most important for EWM strategies’ prioritization. Based on the ranking results, it is revealed that “Organizing the informal recycling sector” has the maximum preference among the others with respect to the 21 criteria considered. Sensitivity analysis is further conducted with respect to varying values of the used parameters, which reveals that the strategy “Organizing the informal recycling sector” constantly obtains its top ranking despite how the parameters’ values vary. Moreover, this study compares the proposed method with q-ROFS-based decision-making methods to evaluate its effectiveness. The outcomes of this work aim to encourage a more proactive attitude towards EWM.

Wearable health devices have gained significant momentum in the daily life by providing continuous monitoring of human physical activities. This study aims to propose a hybrid decision support framework for evaluating and prioritizing the wearable health devices for community-dwelling adults. For this purpose, the steps of gained and lost dominance score (GLDS) method are incorporated with improved symmetry point of criterion (SPC) and stepwise weight assessment ratio analysis (SWARA) weighting methods under the context of q-rung orthopair fuzzy sets (q-ROFSs). The proposed framework is classified into four stages. First, the weights of experts are computed through a q-rung orthopair fuzzy score function-based procedure. To this end, we propose novel score formula for q-rung orthopair fuzzy numbers, followed by comparative analysis. Second, the opinions of experts are aggregated to get the collective assessment degree of each option against several criteria. Third, the criteria weights are determined through a collective weight-determining process involving q-rung orthopair fuzzy SPC and SWARA models. For this purpose, we propose a new distance measure for describing the degree of dissimilarity between q-ROFSs. Based on these stages, an integrated q-rung orthopair fuzzy GLDS method is presented to rank the wearable health devices for community-dwelling adults. To assess the effectiveness of the proposed method, we implemented it to a case study of wearable health device selection problem, wherein the information about the alternatives and criteria is given in terms of q-rung orthopair fuzzy numbers. The opinions of four experts are used to assess the wearable health devices. Numerical experiment indicates that the criterion “accuracy (0.173)”, “battery life (0.157)” and “data management (0.149)” are the three topmost significant criteria to wearable health device selection. In addition, the results show that an option “wrist-worn wearable device (B4)” has the maximum collective score, followed by the health tracking ring (B2), smart headbands (B1) and smart clothing (B3). The numeric values of these selected wearable health devices are B4 = 0.0648 > B2 = -0.0130 > B1 = -0.0178 > B3 = -0.0825. Further, sensitivity analysis with respect to criterion weighting parameter is conducted to verify the steadiness of attained results. Finally, the efficiency of introduced approach is tested based on the comparison with other ranking q-rung orthopair fuzzy decision-making approaches.

In this paper, we propose a new distance measure between spherical fuzzy sets (SFSs) to overcome the drawbacks of existing distance measures between SFSs. We develop a modified symmetry point of criterion (SPC) weight-determining method to get attributes’ weights based on developed distance measure between SFSs and the spherical fuzzy weighted aggregation operators. In the conventional SPC method, a symmetry point is estimated as the middle value or the average value of a given interval [a, b], where a and b denote the maximum rating and the minimum rating of a criterion, respectively, and then it employed the absolute distance measure to find the modulus of symmetry (MOS) matrix of attributes, but the SPC method is unable to describe the imprecise and the uncertain data during the determining of the weights of attributes. We further propose a new multi-attribute decision-making (MADM) method based on the proposed modified SPC weight-determining method and the compromise ranking of alternatives from the distance to the ideal solution (CRADIS) approach using the proposed distance measure between SFSs. The proposed MADM approach can overcome the drawbacks of the existing MADM approaches in the SFSs setting. It offers us a very useful approach for MADM in the context of SFSs. To illustrate the applicability of proposed framework, it is applied to a case study of industry 4.0 enabling technologies (I4.0ETs) to achieve the digital transformation in the photovoltaic sustainable supply chain (PVSSC). The outcomes demonstrate that “Artificial intelligence” is the most suitable I4.0ETs to achieve digital transformation in the PVSSC among the considered six I4.0ETs.

As a consequence of notable demand for diverse electronic devices, electronic waste (e-waste) is the fastest increasing form of waste that can have adversative impacts on the human being, animals and environment by polluting the natural resources. It is made up of longstanding or end-of-life electronic appliances that are naturally rejected by their original possessors because of their short lifespan. Improper handling of e-waste disposal can cause long-term and undesirable risks to public health, the environment and those managing it. As e-waste contains a number of hazardous as well as valuable materials, therefore, selecting an appropriate e-waste management strategy is important before its treatment. This work aims to develop hybrid ranking framework for assessing and prioritizing the strategies of e-waste management (EWM) with uncertain information. For this purpose, new score function and distance measure are introduced on “intuitionistic fuzzy sets (IFSs)”. The developed ranking approach is categorized in the following stages. First stage computes the weight of invited decision-experts (DEs) using IF-score function-based method and further creates an aggregated decision-matrix by uniting diverse opinions of DEs concerning the performance of each option over given attributes. Second stage evaluates the weights of criteria with an integrated weight-determining structure that combines objective weight by “MEthod based on the Removal Effects of Criteria (MEREC)” and the subjective weight via “Relative closeness coefficient (RCC)” with intuitionistic fuzzy information (IFI). Based on these stages, the third stage of this framework presents an incorporated “IF-additive ratio assessment (IF-ARAS)” approach to rank the alternatives. This paper further applies proposed hybrid framework on a case study of EWM strategies, which illustrates its practicality and feasibility. Lastly, sensitivity and comparative study are presented to test the stability and robustness of proposed hybrid framework on IFSs setting.

In parallel with technological advances, the number of strategies proposed and implemented to achieve carbon neutrality has been steadily increasing. While carbon taxes and carbon trading markets transform economies through market-based mechanisms, renewable energy transitions and land-use planning approaches are driven by structural changes. As the societal costs of these strategies are significant, their efficiency must be carefully analysed. This study introduces a picture fuzzy decision-support framework to evaluate green policies in terms of their effectiveness in achieving carbon neutrality from a multidimensional perspective The proposed model integrates picture fuzzy sets (PiFSs), a new PiF-divergence measure, the Ranking Comparison (RANCOM) method, and the Multi-Attribute Ideal-Real Comparative Assessment (MAIRCA) approach, collectively forming the PiF-divergence–RANCOM–MAIRCA model. The newly developed PiF-divergence measure overcomes several limitations of existing PiF-divergence metrics by effectively handling uncertainty and determining objective weights for evaluation criteria. The MAIRCA component bring in line theoretical and real-world evaluations, aligns criteria, and computes aggregate disadvantage gap measures to rank alternative strategies, where smaller gaps indicate superior performance. The model is applied to a case study evaluating four of the most prominent alternative carbon-neutrality strategies; renewable energy transformation, land-use planning, carbon taxation, and carbon trading markets, under a multidimensional set of criteria. The results indicate that renewable energy transformation should be prioritized above all other strategies, followed by land-use planning, carbon taxation, and carbon trading markets, respectively. Sensitivity and comparative analyses confirm the robustness, consistency, and efficiency of the proposed model. Overall, the study demonstrates the practical potential of fuzzy multi-criteria decision-making methods in guiding policy design for sustainable and carbon–neutral transitions.

The most important and effective facility in the development of medical tourism is medical hotel, which includes various types of medical clinics, rooms, food beverage services, hotel products and services, while the medical-tourists are enjoying their independence. Worldwide popularity of medical tourism has provided motivations for entrepreneurs to initiate this innovative hoteling concept based on the functions of a hotel, a hospital and a healthcare center. Medical hotels contribute to disease transmission control by interrupting infection chains, letting down contact rates, and improving resource allocation within healthcare systems. The present work offers a wide-ranging method to assess the medical hotels intervention to control the spread of infectious disease. In this regard, this paper introduces an extended model using the combination of proposed hesitant fuzzy Hellinger distance measure, the criteria importance through intercriteria correlation (CRITIC), the evaluation using distance from average solution (EDAS) and hesitant fuzzy sets (HFSs) and further applies to assess medical hotels intervention to control the spread of coronavirus outbreak. In the following, new Hellinger distance measure on HFSs is developed, the CRITIC approach is implemented for determining the weight of different factors affecting the medical hotels selection and the EDAS method is applied for ranking of medical hotel alternatives. Further, the efficacy and feasibility of the developed hybrid method are presented with an application and the outcomes are interpreted. Finally, a comparison is made to reveal the robustness of the developed model over the existing ones.

Lean Six Sigma (LSS) represents a powerful fusion of procedures, integrating the productivity-focused principles of Lean with Six Sigma’s robust emphasis on quality improvement. Organizations have been applying LSS to reduce lead time, errors, and waste generation, and at the same time, enhance flexibility and efficiency. The LSS approach asserts that organizations can gain significant advantages by combining a customer-focused approach and the waste-reduction principles of Lean with the data-driven tools and structured defect-elimination methods of Six Sigma. Critical Success Factors (CSFs) are the key elements that are essential for an organization to accomplish its strategic goals. This research aims to evaluate the CSFs for the successful implementation of LSS in the food business. For this purpose, a decision support framework is introduced based on the combination of Symmetry Point of Criterion (SPC), Rank Reciprocal Weighting (RRW), Opinion Weight Criteria Method (OWCM), and Relative Closeness Coefficient (RCC) models within the context of Pythagorean fuzzy information. In this respect, a new score function and distance measure are proposed from a Pythagorean fuzzy information perspective. To show the feasibility and efficacy of the proposed model, a case study relating to the CSFs assessment for LSS implementation in the food industry is considered and solved, which consists of 16 CSFs and 4 food businesses with Pythagorean fuzzy information. Numerical experiment indicates that the CSF “Linking LSS to customer satisfaction & business strategy” ranks as the top factor with the weight value (0.681), followed by “Continuous monitoring (0.0672)” and “Top-level management support (0.067)”. It shows the significant association of the LSS approach with customer satisfaction and business strategy in the food businesses. Further, the sensitivity analysis is conducted to measure the impact of changes in the experts’ weights over different weight strategies and the assessment degree of CSFs over different weighting coefficients during the CSFs assessment of the LSS. Finally, the comparative analysis is conducted to validate the reliability and superiority of our proposed methodology.

Health-care waste (HCW) comprises potentially harmful microorganisms that can infect medical professionals, hospital patients and the general public. The management and treatment of HCW are of great concern due to its potential risk to the society and environment. At the present time, several techniques are available for the treatment of HCW materials. Each technique has its own advantages and disadvantages in terms of continuing effects on the human health and environment. On the other hand, uncertainty is widely associated with the real-life HCW disposal techniques assessment problem. This work aims to develop a hybrid decision-making model for assessing the HCW disposal techniques from uncertainty and sustainability perspectives. In this model, an integrated weighting tool using the symmetry point of criterion (SPC) and pivot pairwise relative criteria importance assessment (PIPRECIA) models is presented to prioritize the importance of considered indicators under interval-valued intuitionistic fuzzy (IVIF) environment. To this aim, a new interval-valued intuitionistic fuzzy score function is proposed with its effectiveness over existing ones. Further, a combined operational competitiveness rating (OCRA) approach, named as IVIF-SPC-PIPRECIA-OCRA, is presented to evaluate the health-care waste disposal techniques. To confirm the applicability and usefulness of introduced model, it is applied to a case study of HCW disposal techniques assessment, wherein the information is given in terms of interval-valued intuitionistic fuzzy numbers. This study identifies the criteria/indicators through comprehensive survey and then determines the most suitable HCW disposal technique using the proposed approach by means of socio-technical and triple bottom line theories. The results indicate that Emission, Annual operating cost, Waste residuals and Environmental impact are the most effective sustainable factors and Plasma arc technology is the most suitable technique for the disposal of health-care waste. Sensitivity and comparative analyses are performed to check the consistency, robustness and efficiency of introduced IVIF-SPC-PIPRECIA-OCRA model. The obtained results support the policymakers to set up a systematic tool for choosing the most suitable HCW disposal technique from sustainability points of view.

Fossil fuel-powered trucks and vehicles used in road freight transportation play a notable role in the emission of greenhouse gases. Although the road vehicle industry's use of renewable energy is promising in terms of sustainability, the vehicle manufacturing industry's initiatives are still in their infancy. Moreover, existing studies on using electric and renewable energies in transportation have primarily focused on electric automobiles. Considering these research and practice gaps, this work investigates the selection of the most proper fuel cell electric long-haul trucks (FCETs) to restructure the Turkish fleet of long-haul trucks operating nationwide concerning sustainability. However, assessing these vehicles is challenging, as they are produced based on new and advanced technology, with severe and highly complicated uncertainties. Thus, this paper suggests a Pythagorean fuzzy distance measure-based weighted integrated sum product (WISP) with the integration of the symmetry point of criteria (SPC) and relative closeness coefficient (RCC)-based weighting methods. Surprisingly, and unlike the findings of earlier works, the acquired conclusions indicate that refueling time (0.1161) is the most influential factor for FCET selection, followed by range (0.0837) and torque (0.0785) among the 14 criteria. Besides, the first alternative (R1) outperforms the other options, followed by R5 and R7. Finally, robustness and validity checks ensured the consistency, stability, and practicality of the conclusions. The research can guide manufacturers who produce FCETs and aim to enhance the quality and desirability of their products. Furthermore, practitioners and researchers can utilize the proposed model to solve challenging decision-making problems.

Transportation plays a key part in daily routines of people and the global economy, but it is also contributing vastly to the greenhouse gas emissions and climate change. Digital mobility solutions can improve the transport efficiency and enable carbon-free future with smart transport choices. In this manuscript, we develop a hybrid ‘Picture Fuzzy Euclidean Taxicab Distance-based Approach (PF-ETDBA)’ to evaluate the strategy implementation alternatives in urban mobility to reduce digital carbon footprint. This approach first computes the numeric significance of decision experts by picture fuzzy rank sum procedure. For this purpose, the new distance measure is presented to evade the limitations of some extant picture fuzzy measures. Next, an integrated approach for criteria weight is presented to estimate the criteria weights including the objective weight via picture fuzzy standard deviation (PF-SD) approach and subjective weight with picture fuzzy relative closeness coefficient (PF-RCC) model. Based on these processes, a hybrid algorithm is introduced to tackle with decision-making problems with picture fuzzy information, named as ‘PF-SD-RCC-ETDBA’. Furthermore, the developed model is implemented to rank the strategy implementation options across twelve diverse criteria, which demonstrating its superiority and applicability. It is found that an option “Progressive digital transition strategy” is the most suitable choice with minimum evaluation score (-0.0098), followed by Transformative smart mobility innovation strategy (0.002) and Policy-driven sustainable mobility governance strategy (0.0078). Moreover, sensitivity and comparative analyses are conducted to validate the stability and robustness of obtained results.

Energy demand is increasing tremendously due to the growing world population and rapid technological advancement. In the meantime, dependence on fossil fuels is one of the main causes of environmental problems like climate change and greenhouse gas emissions. These challenges highlight how vital it is to switch from fossil fuels to sustainable renewable energy sources (RESs). Renewable energy technologies can be an option that can help nations achieve sustainable development goals. However, selecting the most appropriate RES for any precise geographic zone is a complicated decision-making problem as of comprising various measured criteria. In this regard, we develop new entropy and knowledge measures for Pythagorean fuzzy sets (PFSs) along with their desirable properties. On the basis of these measures, an integrated Pythagorean fuzzy-entropy−stepwise weight assessment ratio analysis (SWARA)−alternative ranking order method accounting for two-step normalization (AROMAN) methodology is proposed to prioritize and evaluate the various RESs over diverse considered criteria. In this methodology, the developed entropy and score function-based approach is presented to obtain objective weights of criteria, together with determining subjective weights of criteria using the SWARA method. AROMAN is employed to prioritize the RES options on PFSs. Comparative study and sensitivity analysis are discussed to demonstrate the feasibility and applicability.

Energy storage technology (EST) is crucial in mitigating the environmental impact of energy storage and reducing carbon footprints. It is a vital component of renewable energy sources and decarbonization of world energy structures. The selection of a suitable EST depends on multiple aspects of sustainability; thus, the decision-making methods are a more proper way to systematically deal with this problem. Uncertainty is commonly occurred in the selection of a suitable EST. As a generalization of a fuzzy set, a single-valued neutrosophic set (SVNS) has been demonstrated as a useful framework for handling indeterminate, inconsistent, and uncertain data of realistic decision-making situations. Considering the idea of SVNS, this paper develops a hybrid multi-criteria group decision-making (MCGDM) approach to assess and prioritize ESTs over different qualitative and quantitative criteria. The proposed “simple weighted sum product (WISP)” method combines the Hellinger distance measure-based decision experts’ weighting tool and integrated criteria weight-determining model to deal the single-valued neutrosophic information (SVNI)-based MCGDM problems with completely unknown weights of decision experts (DEs) as well as defined criteria. To evade the drawbacks of extent distance measures, we introduce a novel single-valued neutrosophic Hellinger distance measure to compute the degree of discrimination on SVNSs. Some illustrative examples are taken to exemplify the efficiency of developed Hellinger distances over existing ones. Further, the developed Hellinger distance is utilized to derive the weight of DEs. In addition, the criteria weight-determining procedure is given by integrating objective weight through a “method based on the removal effects of criteria (MEREC)” and subjective weight through “ranking comparison (RANCOM)” under SVNI. Based on these models, a combined WISP approach is developed to rank the alternatives on SVNI. The proposed ranking framework is employed in an empirical study of the EST selection problem, which shows its practicality and feasibility. In this study, the evaluation criteria are categorized into technical, environmental, social, economic, and performance dimensions with the DEs’ opinions. Comparative and sensitivity analyses are made to approve the validity and stability of the developed ranking approach. The present study offers valuable insights for choosing multi-criteria EST under an indeterminate, inconsistent, and uncertain environment, which also expands the application scopes of the combined MEREC-SWARA-WISP method.

Purpose As the thrust on sustainable development gains momentum, the financial sector has emerged as the prime mover to drive sustainability. Sustainable finance is a powerful tool that aims to align investments and financial services with sustainable development goals. Owing to the association of various factors, the assessment of the digital transformation (DT) challenges in sustainable finance services can be treated as a multi-criteria group decision-making problem. This paper aims to introduce a hybrid Pythagorean fuzzy (PF)-based decision-making model for assessing DT challenges in sustainable financial services (SFSs). Design/methodology/approach On the basis of the proposed score function and distance measure, we present the stepwise algorithm of the introduced ranking framework in which the weights of decision experts are derived using the score function and rank reciprocal-based procedure. Further, the developed model determines the impacts of DT challenges by combining the objective weights through the symmetry point of the criterion tool and the subjective weights by relative closeness coefficient-based approach within the context of PF sets. As per the decision experts and DT challenges’ impacts, a hybrid PF mixed aggregation by a comprehensive normalization technique approach is presented to evaluate the SFS systems. Findings The results of the study prove that environmental, social and governance finance is the most appropriate choice by means of DT challenges. Sensitivity analysis is conducted to analyze the impacts of different parameters on the final result. Finally, a comparison with extant methods including TOPSIS, VIKOR, Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) is provided to test the validity of the introduced model. Originality/value To develop the proposed method, we first present a new score function for PF number. Some numerical examples are discussed to show its advantages over existing PF-score functions. Next, we develop a modified distance measure for describing the discrimination degree between PF sets and further highlight its effectiveness by comparing it with existent PF-distance measures.

Implementing industry 4.0 (I4.0) strategies in automobile manufacturing firm leads to the higher demand for newer services, drives innovation, continuously innovates to meet the changing needs and expectations of customers, and enables the development of sustainable solutions. This paper develops a q-rung orthopair fuzzy information (q-ROFI)-based decision support tool to evaluate I4.0 strategies in the automobile manufacturing firm. The proposed framework firstly calculates weight of decision expert using a procedure considering the qrung orthopair fuzzy set (q-ROFS). Next, an individual opinions of decision experts are aggregated into single decision through q-ROF weighted averaging operator. Further, criteria weights are computed by a combined weighting procedure involving objective weighting through entropy-based procedure and subjective weighting by stepwise weight assessment ratio analysis (SWARA) model with q-ROFI. In the following purpose, new entropy is introduced based on the cross entropy of q-ROFS and new score function is proposed for q-ROFS to evade the limitation of existing q-ROF-score function. On the basis of these steps, a modified combined compromise solution (CoCoSo) approach is presented to assess and prioritize the alternatives under q-ROFS context. Finally, the proposed framework is applied to a case study of I4.0 strategies evaluation problem in automobile manufacturing firm. According to the outcomes, the most suitable strategy among the other strategies over considered twenty-five evaluation criteria for assessing I4.0 strategies in the automobile manufacturing firms is as new business models development strategies (0.319), improving information systems strategies (0.273) and human resource management (HRM) strategies (0.210), respectively. The most significant criteria for assessing I4.0 strategies in the automobile manufacturing firms are technology (0.055), coordination (0.052), and legal problems (0.048), respectively. Moreover, comparison with different existing methods is presented to validate the robustness of introduced method.

Smart manufacturing is a holistic strategy for product optimization in manufacturing with minimum costs and improved operational efficiency. However, the implementation of smart manufacturing technologies in small and medium enterprises (SMEs) is limited due to a lack of awareness and understanding of their potential benefits. The manuscript aims to propose a hybrid picture fuzzy information (PFI)-based decision support tool and its application in dealing with the smart manufacturing technologies assessment problem for SMEs. The proposed method firstly computes the decision experts’ weights using a picture fuzzy distance measure and rank sum model. In the following, a modified distance measure is introduced for PFI and presents its effectiveness over the existing ones. Next, the individual experts’ views are combined into group decisions using picture fuzzy Sugeno-Weber-weighted geometric (PFSWWG) operators. To this aim, new PFSWWG operators are developed for PFI with their desirable characteristics. Further, a collective weighting procedure is developed by combining the objective weight with the symmetry point of criteria (SPC) model and subjective weight via the picture fuzzy ranking comparison (RANCOM) method. Based on these procedures, we present a hybrid combinative distance-based assessment (CODAS) approach for solving multi-criteria group decision-making (MCGDM) problems with PFI. Moreover, the CODAS model is executed as a case study of smart manufacturing technologies evaluation problems for SMEs, which exemplifies its practicality and feasibility. Sensitivity assessment is accomplished to test the steadiness and reliability of the attained outcomes. Lastly, the comparison is made to validate the robustness and effectiveness of the proposed methodology. The findings show that the presented methodology can offer a practical way to solve smart manufacturing selection problems with uncertain data.

With the significant dependency on climate patterns and water availability, the agriculture sector is highly prone to fluctuating climate conditions. However, the assessment of agricultural sustainability in agro-climatic regions is a multifaceted and uncertain decision-making problem due to the involvement of multiple sustainability aspects concerning environmental, economic, and social dimensions. To this aim, this work proposes a hybrid ranking framework in the context of single-valued neutrosophic sets, which combines the modified relative closeness coefficient-based approach, the pivot pairwise relative criteria importance assessment tool, and the weighted integrated sum product method with single-valued neutrosophic information. This framework first presents a formula to obtain the significance of decision experts' opinions using the truth membership, indeterminacy membership, and falsity membership degrees of a single-valued neutrosophic set. Next, the decision experts' opinions are unified to determine the aggregated single-valued neutrosophic decision matrix, wherein each of its elements denotes the single-valued neutrosophic performance rating of an alternative with respect to considered evaluation indicators. Further, the weights of indicators are determined by combining the objective and subjective weighting models through the relative closeness coefficient-based approach and the pivot pairwise relative criteria importance assessment model, respectively. To find the relative closeness coefficient of indicators, a novel single-valued neutrosophic distance measure is proposed, which evades the deficiencies of existing distance measures. Finally, a hybrid weighted integrated sum product method is presented to rank the alternatives. To demonstrate the relevance and exhibit the efficacy of the introduced framework, it is applied to a case study of agricultural sustainability assessment in 10 considered agro-climatic regions of India. For this purpose, some indicators related to the triple bottom line of sustainability have been recognized from the literature review for the selection of a sustainable agro-climate region in India. The outcomes demonstrate that the "Trans-Gangetic Plain" region has the maximum preference, while the "Western Dry Region" has the least preference among the other agro-climatic regions. Irrigation intensity, crop diversification, receiving remittance as well as membership in the agricultural credit society are influencing indicators responsible for agricultural sustainability in the "Trans-Gangetic Plain" region compared with the "Western Dry Region". Lastly, sensitivity and comparative discussions are shown to test the stability and strength of the proposed ranking model under the setting of single-valued neutrosophic sets. The proposed multi-criteria evaluation method provides insights for policymakers in evaluating and selecting economically, socially, and environmentally sustainable agro-climatic regions under the context of single-valued neutrosophic information.

This study aims to evaluate and prioritize the key interested regions of Circular Economy (CE) in terms of implementing the industry 4.0 technologies for the performance of logistics activities in the agri-food sector. For this purpose, we introduce a hybrid ranking framework based on Relative Closeness Coefficient (RCC)-based objective weighting model, the RANking COMparison (RANCOM) subjective weighting procedure and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) with Intuitionistic Fuzzy Information (IFI). In this framework, new IF-score function and an improved distance measure are proposed in the context ofIFI to evade the limitations of existing ones. A hybrid IF-RCC-RANCOM-MACONT framework is introduced to prioritize the options over defined criteria. To prove the applicability of introduced approach, it is employed on a case study of circular economy interested regions assessment in the agri-food sector, consisting of five alternatives and nine criteria under the dimensions of sustainability. Sensitivity analysis is shown to highlight the impact of used parameters on the final outcomes. At last, a comparison with extant approaches is made to demonstrate the robustness of obtained results.

Micromobility is an innovative urban transport solution that can effectively tackle greenhouse gases and reduce the use of private vehicles, especially for short-distance travel options. As a type of shared micromobility service, the electric scooter (e-scooter) is designed to provide convenient and quick rides for short distances. The constantly growing adoption of these emerging transportation technologies brings many factors to be considered. In this paper, we evaluate the available e-scooters and identify the most appropriate choice with respect to several factors. To this aim, we propose a decision support tool with the combination of modified symmetry point of criterion (SPC), the pivot pairwise relative criteria importance assessment (PIPRECIA), and the weighted aggregated sum product assessment (WASPAS) approaches under the setting of q-rung orthopair fuzzy (q-ROF) sets. First, the developed method calculates the significance values of decision experts through a new q-ROF-score function. Next, the criteria weights are determined using an incorporated objective-subjective weighting model. It comprises a q-ROF-modified SPC tool for objective weighting and a q-ROF-PIPRECIA tool for subjective weighting. Subsequently, we present the stepwise mathematical algorithm of a q-ROF-WASPAS approach using q-ROF aggregation operators and criteria weighting tools. To verify the compatibility and effectiveness of the introduced model, it is applied to evaluate and prioritize the e-scooters under the q-ROF environment. Sensitivity analysis is made to examine the impact of varying values of weighting and utility parameters. The efficiency of the introduced approach is confirmed by different comparative investigations.

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.