Sustainable transportation is an emerging field of research that primarily focuses on eco-friendliness. Smart cities actively attempt to adopt sustainable practices for reducing carbon footprints and improving people’s lives. Developing countries like India intensely concentrate on the zero-carbon theme and promote measures to expedite the process. Mayor’s transport strategy 2018 is a useful document that describes different zero-carbon measures that smart cities could adopt to promote sustainable transport. Due to the heterogeneity of these measures, ranking becomes essential. Driven by the claim, in this article, we develop a new framework using q-rung fuzzy information (qRFI) for rational ranking of zero-carbon measures. QRFI reduces subjective randomness and allows uncertainty modeling from three dimensions: 1) membership; 2) hesitancy; and 3) nonmembership. Specifically, the framework presents criteria importance through intercriteria correlation (CRITIC) with the rank sum (RS) and measurement of alternatives and ranking based on compromise solution (MARCOS) methods for ranking zero-carbon measures. Some notable innovative aspects are: 1) both subjective and objective weights are calculated for criteria; 2) experts’ weights are derived methodically; and 3) zero-carbon measures are ranked with close resemblance to human-driven decisions. To exemplify the usefulness of the proposed tool, a real case example of ranking zero-carbon measures for Coimbatore is demonstrated. Advantages of the innovations are: 1) methodical calculation of weights mitigates biases and inaccuracies; 2) consideration of subjective/objective weights gives better expressibility and understanding of criteria importance; and 3) distance-based human-driven decision scheme offers a flexible and rational ranking. Concerning the findings, the technical aspect criterion receives the highest weight value of 0.1404, followed by documentation (0.1173) and modality shift (0.1125) criteria. Moreover, “start zero-carbon zones,” incentive-based ultralow-carbon vehicles,” and “reduced emissions from on-road machinery” are measures that can be implemented to achieve zero-carbon emissions. The strengths and weaknesses of the tool are inferred via sensitivity and comparison checks.

Cloud vendors (CVs) play an indispensable role in the development of IT sectors and industry 4.0. Many CVs evolve every day, and a systematic selection of these is becoming substantial for organizations. Literature studies have shown that multicriteria decision-making (MCDM) is a powerful tool for systematic selection. However, the major issue with the state-of-the-art models is that they do not effectively represent uncertainty. Moreover, the personalized selection of CVs based on user queries is not prominent in an MCDM context. In this paper, to circumvent these issues, a new decision framework is proposed that utilizes a generalized preference style called interval-valued probabilistic linguistic term set (IVPLTS). This preference style considers occurring probability values as interval numbers instead of a single precise value, which provides flexibility during preference elicitation. Initially, missing values are imputed systematically by using a case-based method. Then, the consistency of these preferences is checked using Cronbach's alpha coefficient, and the inconsistent preferences are repaired rationally by using an iterative method. A programming model is proposed for determining the weights of the evaluation criteria. Furthermore, Maclaurin symmetric mean (MSM) is extended to IVPLTS for aggregating preferences from each expert. The interval-valued probabilistic linguistic comprehensive (IVPLC) method is proposed for prioritizing CVs in a personalized manner. Finally, the framework's practicality is validated by using a case study of CV selection for an academic institution; strengths and weaknesses of the framework are conferred by comparison with extant CV selection models.

With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision-making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q-rung orthopair fuzzy set (q-ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of qth power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Firstly, we introduce some basic set operations for q-ROFS. The properties of these operations are also discussed in detail. Later, we propose a q-rung orthopair fuzzy decision-making method based on weighted distance-based approximation (WDBA), in which the weights of decision-makers are obtained from a nonliner optimization model according to the deviation-based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.

Component Based Software (CBS) development is the assembling of already developed components and preparing for integration. For the last two decades, researchers have been concentrating much on CBS both in industry and academia, because CBS helps in the reduction of manpower, cost, software development time and better system maintainability. The selection of the best component from the large collection of components is a difficult task and only few researchers have addressed this issue so far by using optimization techniques. In this paper, we propose a new methodology to group the components effectively, based on the software architecture, by using fuzzy concepts. Again, Fuzzy Clustering Technique (FCT) is introduced to find the optimal grouping between the reusable components and the software architectures. The proposed model has the following advantages: (i) the proposed Fuzzy Weighted Relational Coefficient (FWRC) is used to measure the fuzzy relational value of the suggested 9 component parameters; (ii) A mathematical model is established to remove all less utility leveled components before grouping; (iii) FCT performs on newly developed similarity coefficient formula; (iv) Zero — One programming (optimization) technique is used to optimize the group; (v) Fuzzy Weighted Approach is used to find the dominant component among the component grouping, and (vi) Optimal grouping is achieved by the property, namely, maximizing the cohesion and minimizing the coupling when compared to other methods. Finally, the validation of the derived methodology is verified with sample Business Enterprise Applications.

Genetic algorithms have proved to be a successful method for predicting the protein structure. In this paper, we propose a new intermediate selection strategy for genetic algorithms and we implement it for protein folding problem. In a standard genetic algorithm the children replace their parents. The idea behind this is that both parents pass on their good genetic material to their children. In practice however, children can have worse fitness than their parents. We therefore propose another intermediate selection step, which we call as modified keep-best reproduction (MKBR) that ensures that new genetic information is entered into the gene pool, as well as good previous genetic material is being preserved. We have demonstrated the superiority of modified keep-best reproduction on several instances of the proteinfolding problem, which not only finds the optimum solution, but also finds them faster than the standard generational replacement schemes.