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Due to energy's global reliance on fossil fuels and population growth, Greenhouse gas (GHG) emissions and their repercussions have attracted attention. Due to their cheaper cost and cleaner environment, renewable energy modes of transportation like electric vehicles are highly sought after. Electric vehicles are beneficial, but they also emit emissions indirectly in power plants that generate their electricity, which could affect small and medium communities. Thus, it is crucial to assess such modes of transportation's performance while considering key aspects and criteria. However, scholarly works in this field have not fully addressed the deployment of a comprehensive electric vehicle decision-making support system. This study addresses electric bus selection by introducing a novel approach to Multi Criteria Decision Making (MCDM) utilizing a developed integrated fuzzy set. We introduce an integrated approach that combines an Entropy weighting approach with a 2-tuple Linguistic T-Spherical Fuzzy Decision by Opinion Score Method (2TLTS-FDOSM). This approach is designed to tackle the challenges associated with evaluating the feasibility of electric bus models (EBMs) and addressing the theoretical challenge of MCDM in the context of the presented case study. These challenges include dealing with ambiguities and inconsistencies among decision-makers. The former method is utilized to ascertain the significance of assessment criteria, whereas the latter approach is applied to select the most favorable EBM by utilizing the weights obtained. As for the 2TLTS-FDOSM results, out of all the (n = 6) EBMs considered, A 3 (11-E) EBM obtained the highest score value, while the A 3 (9-E) EBM had the lowest score. The robustness of the results is confirmed through sensitivity analysis. [ABSTRACT FROM AUTHOR]

Energy Systems Integration (ESI) involves coordinating and planning energy systems to provide reliable and affordable energy services while minimizing environmental harm. It optimizes interactions among different energy sources to achieve sustainability goals and promotes efficient resource usage. However, evaluating and benchmarking ESI frameworks to select the most suitable and transparent ones is a complex Multi-Criteria Decision-Making (MCDM) problem. This complexity arises from trade-offs, conflicts, and importance considerations of the six ESI evaluation characteristics: Multidimensional, Multivectoral, Systemic, Futuristic, Systematic, and Applied. Hence, this study aims to address this complexity by integrating Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Multi-Attributive Border Approximation Area Comparison (MABAC). The proposed methodology consists of two phases. Firstly, the development of a Dynamic Decision Matrix (DDM) to handle 26 ESI frameworks as alternatives and the six ESI characteristics criteria. Secondly, the integration of mathematical processes is formulated based on the FWZIC-MABAC methods. Using the FWZIC technique, the ESI evaluation criteria were weighted based on the preferences of twelve experts. ESI-C2 (Multivectoral) and ESI-C1 (Multidimensional) criteria received the highest weights of 0.195 and 0.190, respectively, while the ESI-C5 (Systematic) criterion received the lowest weight of 0.110. The remaining criteria, namely ESI-C3 (Systemic), ESI-C6 (Applied), and ESI-C4 (Futuristic) obtained weights of 0.189, 0.168, and 0.147, respectively. The MABAC benchmarking results showed that A11 (Energy Security) and A15 (Energy Security under decarbonization) ranked first with the highest score value of 0.28081 for both. Conversely, A19 (EJM) had the lowest score value of −0.17022. The systematic rank and sensitivity analysis assessments were conducted to verify the efficiency of the proposed methodology. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100 % across three key perspectives. This methodology offers valuable support in making informed and sustainable decisions in the energy sector. • MCDM framework for Energy Systems Integration selection is developed. • Novel Dynamic Decision Matrix for 26 ESI frameworks and six criteria are presented. • Sensitivity analysis is used to assess the robustness of the selection framework. [ABSTRACT FROM AUTHOR]

Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves a multi-criteria decision-making (MCDM) problem. This needs to consider various organisational culture characteristics that influence digital innovation success, assign significance weights to each characteristic, and recognise that distinct organisational cultures may excel in different aspects necessitates the proper handling of data variations. Hence, to provide organisations seeking to align cultural practises with digital innovation objectives with valuable insights, this study aims to develop an MCDM model for evaluating and benchmarking organisational culture in companies to foster digital innovation. The benchmarking decision matrix is formulated based on the intersection of evaluation characteristics and a list of organisational culture aspects in companies. The MCDM model is developed in two phases. Firstly, a new weighting model, q -rung picture fuzzy-weighted zero-inconsistency (q -RPFWZIC), is formulated for assessing the evaluation characteristics under the q -rung picture fuzzy sets environment. Secondly, the simple additive weighting (SAW) model is formulated for benchmarking the organisational culture in companies using the extracted weights of the evaluation characteristics. The results indicate that characteristic C 6 (corporate entrepreneurship) has the highest weight, with a value of 0.161 , while characteristic C 3 (employee participation, agility and organizational structures) and C 7 (digital awareness and necessity of innovations) has the lowest weight of 0.088. Company A 2 secures the top rank with a score of 0.911 , satisfying eight evaluation characteristics, whereas company A 7 holds the last rank order, satisfying only one evaluation characteristic, obtaining a score of 0.101. In model evaluation, several scenarios were considered in a sensitivity analysis test based on a 100% increment in weight values for each characteristic to validate the reliability of the model results. [ABSTRACT FROM AUTHOR]

The technology deployment in smart e-tourism brings high potential in terms of customer data, events, reservations, and others. It acts as an effective and personalized guide to aid travelers. There is an increasing variety of smart e-tourism apps with multiple categories and criteria, but in terms of decision making, this presents a multicriteria complex problem to determine the best app from a group of available options with high criteria subjectivity. Literature reviews have evaluated and modeled the existing smart e-tourism apps alternatives, but informational uncertainty remains. The fuzzy sets and Multi-Attribute Decision Analysis (MADA) were used to handle the subjectivity issue. However, this process includes levels of uncertainty, which affects the decisions made and still an open issues. Spherical fuzzy rough sets (SFRSs) environment are useful in this situation for resolving fuzziness and ambiguity. This paper proposed a decision modeling approach for smart E-Tourism data management applications based on SFRSs environment. For methodology: firstly, a decision matrix is adopted for 5 different categories of Smart E-tourism's system applications on the basis of the integrated 12 evaluation criteria. Secondly, a new formulation and development formulating a new extension of FWZIC, called a Spherical Fuzzy Rough-Weighted Zero-Inconsistency (SFR-WZIC), for weighting the smart key concept attributes involved in modeling smart e-tourism, whereas a new formulation and development for a new extension of FDOSM, called a Spherical Fuzzy Rough Decision by Opinion Score Method (SFR-DOSM), for modeling the applications of smart e-tourism per each e-tourism category; then, the new developments are integrated. The proposed methods were evaluated using systematic ranking and sensitivity analysis. • A homogeneous SFRS-MCDM framework for benchmarking smart e-tourism applications is developed. • A new formulation of FWZIC and FDOSM based on SFRS are presented. • Systematic ranking and sensitivity analysis assessments were presented to evaluate the robustness of the proposed work. [ABSTRACT FROM AUTHOR]

• Formulated an extension of FWZIC method under fuzzy environment q-ROFS were called q-ROFWZIC. • Formulated an extension of FDOSM under fuzzy environment q-ROFS were called q-ROFDOSM. • Developed an integration between q-ROFWZIC method and q-ROFDOSM. • Performed the proposed Integration Methods on a Case Study of COVID-19 Vaccine Distribution Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase 'development' presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of 'recipients list' and 'COVID-19 distribution criteria' was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria. [ABSTRACT FROM AUTHOR]

Several DataGlove wearable electronic devices based on real-time Sign Language Recognition Systems (SLRSs) have been developed recently to assist the deaf and dumb community in translating hand gestures to their spoken language equivalents. Multidimensional evaluation and benchmarking of these systems are critical for determining the most desirable approach for meeting all essential requirements. However, this process is considered a Multicriteria Decision-Making (MCDM) problem due to the presence of several issues, including multiple evaluation criteria, criteria importance and criteria confliction. Hence, the MCDM approach is required to solve these issues. In this study, a new extension of the Fuzzy Decision by Opinion Score Method (FDOSM) for evaluating and benchmarking SLRSs is developed under an Interval-Valued Pythagorean Fuzzy Set (IVPFS) named IVP-FDOSM. Fundamentally, the methodology includes two phases. In the first phase, a decision matrix is formulated on the basis of identified 'multidimensional criteria of hand gesture recognition and sensor glove perspectives' and 'real-time SLRSs'. The second phase introduces the development of IVP-FDOSM in two stages. The decision matrix is transformed into an opinion matrix in the first stage (data transformation unit). Meanwhile, in the second stage (data-processing unit), the opinion matrix is converted into fuzzy opinion decision matrices with the assistance of three experts by transforming the opinion matrix's linguistic terms to Interval-Value Pythagorean Fuzzy Numbers (IVPFNs). Results indicate the following: (1) individual benchmarking results of real-time SLRS showed high variation (90%) based on the preference of each Decision Maker (DM), with only 10% of preferences being identical. (2) The results of group benchmarking reveal that the 10th real-time SLRS was the optimal one and the 6th was the worst. In addition, the rates of ranking match between the group benchmarking and each DM (first DM, second DM and third DM) were 23%, 37% and 43%, respectively. (3) For the results evaluation, the statistical test-based objective assessment shows that the first group received the lowest mean value (2.80333), while the third group received the highest mean (3.8). These values indicate that the group benchmarked systems resulting from IVP-FDOSM are undergoing a systematic ranking. Furthermore, the comparative analysis reveals that IVP-FDOSM is superior to IVP-TOPSIS and IVP-AHP in terms of ranking and weighting. • An extension of Interval-Valued Pythagorean Fuzzy Set (IVPFS) for FDOSM is presented. • The proposed extension is used in the evaluating and benchmarking real-time SLRSs. • A decision matrix is proposed based on 'multidimensional criteria of HGR and SG perspectives' and 'SLRSs. • The benchmarked systems are objectively validated based on statistical test method. [ABSTRACT FROM AUTHOR]