In response to the intensely competitive bidding environment in Taiwan\'s construction industry, this study innovates with a spherical fuzzy extension of the modified CRITIC method (SFmCRITIC), a pioneering approach crafted to refine the decision-making process. The research centers on the pivotal role of accurately weighting various significant factors, such as financial stability, market insight, and specialized expertise, to optimize bid success rates. The SFmCRITIC method stands out by quantitatively incorporating the uncertainty and subjectivity typically encountered in bid evaluation. The findings from this methodological application demonstrate a clear hierarchy of influencing factors, offering a nuanced view of strategic priorities for construction firms. This hierarchical understanding not only aids firms in targeted resource distribution and strategic development but also holds promise for adaptation in diverse industry contexts beyond Taiwan, potentially revolutionizing bidding strategies at a global scale.

Nanomaterials are widely used in several biomedical and environmental applications, due to their ideal properties. However, the synthetic and characterization procedure requires significant costs and has a negative environmental impact. Various methods are available in order to control the pre-synthesis design of the produced materials, predicting their behavior and minimizing the series of experiments. Multi-Criteria Decision-Making is proposed in this study in order to determine the best combination of the physicochemical parameters and to define the best alternative among fifteen different samples of nanostructured titanium dioxide. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to achieve a final ranking of the available alternatives by avoiding several of the trials that would follow testing the biological effect and the photocatalytic degradation of organic pollutants. Thus, this approach helps us to stay environmentally and ethically correct, saving time, money, and energy and also providing an optimization of the nanomaterials that are developed.

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study\'s findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

The multi-criteria decision-making (MCDM) field has long sought tools capable of adeptly capturing the intricacies of human decision-making amidst uncertainty. Hesitant fuzzy sets (HFS) have become a cornerstone in the MCDM field due to their ability to capture the intricacies of human decision-making under uncertainty. Nonetheless, we identified a significant gap in traditional HFS formulations, which often fail to fully harness the nuanced and implicit preferences of decision-makers (DMs). This shortcoming can lead to suboptimal decision outcomes in complex and uncertain environments. We introduce the normal wiggly hesitant fuzzy set (NWHFS), a novel construct that encapsulates both explicit and implicit preferences within a more representative framework. This study pioneers the development of new correlation coefficients for NWHFSs, offering a robust quantitative measure to elucidate the intricate relationships between variables. Our findings demonstrate that NWHFSs significantly enhance the MCDM process, providing a nuanced perspective that traditional HFS models cannot match. The proposed correlation coefficients not only reveal the concealed preferences of DMs but also broaden the decision-making spectrum, offering a more profound understanding of the relationships between alternatives and criteria. We illustrate the superiority of our approach through comparative analysis with existing methods, highlighting its ability to discern subtleties that other models overlook. Moreover, we integrate NWHFSs into clustering analysis, showcasing their potential to classify data sources with shared attributes effectively. This integration is particularly noteworthy for its ability to navigate complex datasets, offering a new dimension in data mining and resource retrieval. In essence, our study redefines the MCDM paradigm by introducing NWHFSs and their correlation coefficients, setting a new standard for decision-making accuracy and insight. The implications of our work extend beyond theory, offering practical solutions to real-world decision-making challenges.

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India\'s booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors\' early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.

Droughts are classified as the most expensive climate disasters as they leave long-term and chronic impacts on the ecosystem, agriculture, and human society. The intensity, frequency, and duration of drought events have increased in the past and are expected to continue rising at global, continental, and regional scales. Nature-based solutions (NBS) are highlighted as effective solutions to cope with the future impacts of these events. Despite this, there has been limited comprehensive research on the effectiveness of NBS for drought mitigation, and existing suitability mapping frameworks often overlook drought-specific criteria. To address this gap, a new framework is proposed to identify areas suitable for two drought-coping NBS types at a regional scale: detention basins and managed aquifer recharge. Two multi-criteria decision-making techniques (MCDM), i.e. Boolean logic and Analytic- Hierarchy Process (AHP), were used to map suitable large-scale NBS. The new framework accounts for unique criteria to specifically address drought conditions. By incorporating climate change scenarios for both surface and groundwater, recharge, and different groundwater characteristics, it identifies suitable and sustainable locations capable of managing extreme drought events. Executed through Boolean logic at a regional scale in Flanders (Belgium), the framework\'s strict approach yields significant potential areas for detention basins (298.7 km²) and managed aquifer recharge (867.5 km²). Incorporating AHP with the same criteria introduces a higher degree of flexibility for decision-makers. This approach shows a notable expansion across Flanders, varying with the level of suitability. The results underscore the highly suitable potential for detention basins (2552.2 km²) and managed aquifer recharge (2538.7 km²), emphasizing the adaptability and scalability of the framework for addressing drought in the region. The comparison between potential recharge volume due to detention basin and groundwater use in the region indicated that the detention basins could partially compensate for the high water demand. Therefore, creating a framework targeting drought is vital for the sustainable management of water scarcity scenarios.

Malaysia\'s excessive energy consumption has led to the depletion of traditional energy reserves such as oil and natural gas. Although Malaysia has implemented multiple policies to achieve sustainable national energy development, the current results are unsatisfactory. As of 2022, only 2% of the country\'s electricity supply comes from renewable energy, which accounts for less than 30% of the energy structure. Malaysia must ensure energy security and diversified energy supply while ensuring sustainable energy development. This article uses the fuzzy multi-criteria decision-making(MCDM) method based on cumulative prospect theory to help decision-makers choose the most suitable renewable energy for sustainable development in Malaysia from four dimensions of technology, economy, society, and environment. The results show that solar power is the most suitable renewable energy for sustainable development, followed by biomass, wind, and hydropower, but the optimal alternative is sensitive to the prospect parameters. Finally, it was analyzed that efficiency, payback period, employment creation, and carbon dioxide (CO2) emissions are the most critical factors affecting the development of renewable energy in Malaysia under the four dimensions. Reasonable suggestions are proposed from policy review, green finance, public awareness, engineering education, and future energy. This research provides insightful information that can help Malaysian decision-makers scientifically formulate Sustainable development paths for renewable energy, analyze the problems encountered in the current stage of renewable energy development, and provide recommendations for Malaysia\'s future renewable energy transition and sustainable development.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses\' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Due to its various advantages in different industrial fields, hydrogen can provide energy based on sustainability goals and recreates a critical function in the economy of countries. In this regard, evaluating hydrogen production technologies on an industrial scale is necessary for industrial development and economic growth. Therefore, this study proposes a comprehensive, integrated framework of hybrid fuzzy decision-making for assessing hydrogen production technologies in Iran. In addition to considering sustainability factors, political, technical, and reliability indicators are also assessed in this research to make a comprehensive assessment. The Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) technique determines the importance of indicators, and the Fuzzy Weighted Aggregates Sum-Product Assessment (F-WASPAS) approach ranks technologies. The weighing findings indicated that the sub-indices of investment cost, technical infrastructure development, and implementation costs were introduced as the most significant sub-indices with weights of 0.226, 0.151, and 0.126, respectively. The evaluation findings with the F-WASPAS method and comparative analysis with various decision-making methods revealed that electrolysis based on solar energy and electrolysis based on wind energy technologies had the highest preference. In this regard, the infrastructure and costs of hydrogen production can be improved by presenting various incentives, such as improving financial conditions while attracting investment and increasing cooperation with top companies. So, sustainable development, economic growth, and industrial development are provided.

UNASSIGNED: Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty.
UNASSIGNED: For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment.
UNASSIGNED: A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.
UNASSIGNED: The online version contains supplementary material available at 10.1186/s12544-021-00501-6.