Search Results (2,719)

To tackle preference conflicts and uncertainty in large-group emergency decision-making (LGEDM), this study proposes a probabilistic linguistic LGEDM method integrating the Louvain algorithm and group pressure model. First, expert weights are determined based on a social trust network, and the Louvain algorithm is employed for expert clustering, reducing the complexity of large-scale decision information. Second, a group pressure model is introduced to dynamically adjust expert preferences, enhancing consensus and decision consistency. Third, probabilistic linguistic term sets (PLTSs) are utilized to represent fuzzy and uncertain information, while attribute weights are determined by incorporating both subjective and objective factors, ensuring scientific rigor in decision-making. Finally, an improved TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision-Making) method is adopted to account for the loss aversion behavior of decision-makers (DMs), enabling a more accurate characterization of psychological decision-making traits. The experimental results demonstrate that the proposed method outperforms existing approaches in terms of decision efficiency, group consensus, and result robustness, offering effective support for emergency decision-making in crisis situations. Full article

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments. Full article

Thin-walled structures are extensively utilized in construction because of their lightweight nature and excellent energy absorption efficiency, especially under dynamic loads. Improving the energy-absorbing performance of thin-walled structures by inspiring natural multi-cell designs is a sufficient approach. This paper investigates the energy-absorbing characteristics of variable novel cross-section designs of thin-walled structures subjected to oblique impact loading. Straight and tapered types with seven cross-sectional designs of novel thin-walled structures were studied. The nonlinear ABAQUS/Explicit software 6.13 version was implemented to analyze the crashworthiness behaviors for the proposed variable cross-section designs under different loading angles. The crushing behaviors of the proposed thin-walled structures were examined for various wall thicknesses of 0.5 mm, 1.5 mm, and 2.5 mm and impact loading angles of 0°, 15°, 30°, and 45°. It was determined that the energy-absorbing characteristics of novel thin-walled structures can be efficiently controlled by varying two geometries and seven cross-section designs. A multi-criteria decision-making method (MCDM) using a complex proportional assessment method (COPRAS) was performed to select the optimum thin-walled structures with cross-section designs. It was shown that a tapered square thin-walled structure with 2.5 mm thickness had the best crashworthiness performances with energy absorption (EA) of 11.01 kJ and specific energy absorption (SEA) of 20.32 kJ/kg under a 30° impact angle. Moreover, the results indicated that the EA of the thin-walled structure decreased with the increase in the impact loading angle. In addition, with the increase in the impact loading angle, the peak crushing force (PCF) decreased and reflected the reduction in energy absorbed at a larger angle. The MCDM method in conjunction with the COPRAS method is proposed; it provides valuable insights for safer and more resilient building construction. Full article

The article analyzes long-term renovation strategies in EU member countries using the F-TOPSIS method, focusing on chosen criteria such as CO₂ emission reductions, renovation rates, energy savings, investment requirements, and overall strategy quality. High-performing countries, such as Finland and Spain, demonstrate the importance of clear targets, robust planning, and substantial financial commitments. In contrast, several countries show gaps in strategic detail or ambition, highlighting challenges in achieving EU climate neutrality goals. The methodology underscores the effectiveness of multi-criteria decision-making tools in assessing complex renovation strategies. The findings emphasize the need for harmonized metrics and innovative approaches, such as digital tools like building renovation passports. Full article

Background: The supply chain refers to the full process of creating and providing a good or service, starting with the raw materials and ending with the final customer. It requires cooperation and coordination between many parties, including the suppliers, manufacturers, distributors, retailers, and customers. Methods: In the medicinal supply chain (MSC), the critical nature of these processes becomes more complicated. It requires strict regulation, quality control, and traceability to ensure patient safety and compliance with regulatory standards. This study is conducted to suggest a smooth channel to deal with the challenges and adaptability of the MSC. Different MSC challenges are considered as criteria which deal with various adaptation plans. Multi-criteria decision-making (MCDM) methodologies are taken as optimization tools and probabilistic linguistic term sets (PLTSs) are considered for express uncertainty. Results: The subscript degree function (SDF) and deviation degree function (DDF) are introduced to evaluate the crisp value of the PLTSs. An MSC model is constructed to optimize the sustainable medicinal supply chain and overcome various barriers to MSC problems. Conclusions: Additionally, sensitivity analysis and comparative analysis were conducted to check the robustness and flexibility of the system. Finally, the conclusion section determines the optimal weighted criteria for the MSC problem and identifies the best possible solutions for MSC using PLTS-based MCDM methodologies. Full article

Finding optimal locations for wind farms requires a delicate balance between maximizing energy generation potential and addressing the socio-economic implications for local communities, particularly in regions facing socio-economic challenges. While existing research often focuses on technical and economic aspects of wind farm siting, this study addresses a crucial research gap by integrating sustainable supply-chain-management principles into a comprehensive site-selection framework. We present a novel approach that combines Geographic-Information-System-based spatial analysis, the Fuzzy Analytic Hierarchy Process, and multi-criteria decision-making techniques to identify and prioritize optimal wind farm locations in Tunisia. Our framework considers not only traditional factors, like wind speed, terrain slope, and road and grid infrastructure, but also crucial socio-economic indicators, such as unemployment rates, population density, skilled workforce availability, and land cost. Based on the spatial analysis, it was revealed that 33,138 km² was appropriate for deploying large-scale wind systems, of which 6912 km² (4.39% of the total available area) was categorized as “most suitable”. Considering the SSCM evaluation criteria, despite the minor variations, the ARAS, COPRAS, EDAS, MOORA, VIKOR, and WASPAS techniques showcased that Kasserine, Kebili, and Bizerte stood as ideal locations for hosting large-scale wind systems. These rankings were further validated by the Averaging, Borda, and Copeland methods. By incorporating this framework, the study identifies locations where wind energy development can be a catalyst for economic growth, social upliftment, and improved livelihoods. This holistic approach facilitates informed decision making for policymakers and investors, thus ensuring that wind energy projects contribute to a more sustainable and equitable future for all stakeholders. Full article

Contemporary ports are facing a variety of challenges due to technological advancements, economic pressures, and changing policies. Key issues include the effects of globalization, rapid advancements in information and communication technologies (ICTs), and the changing nature of port services. In order to tackle these challenges and achieve operational excellence, adapt to the shifting of activities, and meet new business demands, smart ports have been proposed as a comprehensive solution. These challenges arise because port success is often measured by traditional metrics such as port size and performance. To accurately assess the intelligence of a port, there is a need for a systematic and scientifically sound smart port evaluation method. This paper provides an overview of the concept of a smart port and develops a multi-criteria assessment framework of port smartness based on neutrosophic cognitive maps (NCMs). The unique and valuable characteristic of NCMs lies in their ability to manage the uncertainty associated with the relationship between two concepts, indicating their effects on each other in neutral states. This structure enables the NCM to provide results with a greater degree of sensitivity than fuzzy cognitive maps (FCMs) and allows for a greater degree of freedom of intuition for an expert to express not only the potential impacts but also the uncertainty associated with those impacts. Our methodology can make decisions using incomplete, uncertain, and inconsistent data during the assessment process, providing a rigorous quantitative framework for the assessment of port “smartness”. The proposed solution has the potential to act as a valuable tool in a group decision support environment and can be used to accelerate an organization’s development, improve productivity, and reinforce efforts to achieve strategic and sustainability objectives. To achieve this, an appropriate framework for such a methodology is demonstrated through an illustrative example offering actionable insights for improving port operations. Full article

This study examines the barriers to implementing sustainable mobility strategies in small municipalities by integrating participatory and multi-criteria decision-making methods. A triangulated approach combines the nominal group technique (NGT), focus groups (FGs), and the fuzzy analytic hierarchy process (FAHP) to systematically identify, refine, and rank key barriers. The NGT enables stakeholders to list and prioritize barriers individually, ensuring balanced participation. FG discussions then refine and contextualize these barriers, addressing qualitative depth. Finally, the FAHP quantitatively ranks the barriers while accounting for uncertainty in stakeholder judgments. The results highlight systemic constraints, such as financial limitations and regulatory inefficiencies, alongside local challenges like inadequate infrastructure and public resistance. Integrating the NGT, FGs, and the FAHP enhances the analytical rigor by merging structured decision-making with participatory engagement. This methodological innovation strengthens the reliability of barrier assessment and offers a replicable framework for urban mobility planning. The findings underscore the need for locally tailored strategies that balance stakeholder inclusion with structured prioritization, contributing to improved governance in sustainable transport planning. Full article

Quality of life (QoL) as a category, which is an overarching goal of sustainable development, dependent on many factors both objective and subjective, should be subjected to constant monitoring in various spatial, temporal and thematic arrangements. This study assesses the spatial differentiation of European Union countries in terms of QoL and housing conditions (HCs) of their populations. Interactions between the studied phenomena were also determined. A multi-criteria decision-making (MCDM) method—the TOPSIS method—and Spearman rank correlation coefficients were used to achieve the objectives of this study. The analysis was conducted using 2019 and 2022 data from the Eurostat database (including the EU-SILC survey) and TheGlobalEconomy.com. The research showed that the housing conditions and QoL of the populations of EU countries vary spatially, being more favorable in Austria, Ireland and Slovenia and the Scandinavian countries of Denmark, Finland and Sweden and less favorable in Greece and some of the countries that joined the EU in 2004 and in 2007, viz. Bulgaria, Hungary and Romania. This study noted a very strong positive correlation between the positions of countries in the rankings created with QoL in 2019 and 2022 (0.947) and with living conditions in the years under study (0.828), as well as a rather weak correlation between QoL and HCs in both 2019 (0.272) and 2022 (0.292). This article fills a research gap because, to our knowledge, the indicated phenomena have not been analyzed to date in the contexts presented in this article. Full article

Eco-design is an innovative design methodology that focuses on minimizing the environmental footprint of industries, including aviation, right from the conceptual and development stages. However, rising industrial demand calls for a more comprehensive strategy wherein, beyond environmental considerations, competitiveness becomes a critical factor, supported by additional pillars of sustainability such as economic viability, circularity, and social impact. By incorporating sustainability as a primary design driver at the initial design stages, this study suggests a shift from eco-driven to sustainability-driven design approaches for aircraft components. This expanded strategy considers performance and safety goals, environmental impact, costs, social factors, and circular economy considerations. To provide the most sustainable design that balances all objectives, these aspects are rigorously quantified and optimized during the design process. To efficiently prioritize different variables, methods such as multi-criteria decision-making (MCDM) are employed, and a sustainability index is developed in this framework to assess the overall sustainability of each design alternative. The most sustainable design configurations are then identified through an optimization process. A typical aircraft component, namely a hat-stiffened panel, is selected to demonstrate the proposed approach. The study highlights how effectively sustainability considerations can be integrated from the early stages of the design process by exploring diverse material combinations and geometric configurations. The findings indicate that the type of fuel used, and the importance given to the sustainability pillars—which are ultimately determined by the particular requirements and goals of the user—have a significant impact on the sustainability outcome. When equal prioritization is given across the diverse dimensions of sustainability, the most sustainable option appears to be the full thermoplastic component when kerosene is used. Conversely, when hydrogen is considered, the full aluminum component emerges as the most sustainable choice. This trend also holds when environmental impact is prioritized over the other aspects of sustainability. However, when costs are prioritized, the full thermoplastic component is the most sustainable option, whether hydrogen or kerosene is used as the fuel in the use phase. This innovative approach enhances the overall sustainability of aircraft components, emphasizing the importance and benefits of incorporating a broader range of sustainability factors at the conceptual and initial design phases. Full article

The decarbonization of the electricity system coupled with the electrification of transport, heat, and industry represents a practical and cost-effective approach to deep decarbonization. A key question is as follows: where to build new solar and wind farms? This study presents a cost-based approach to evaluate land parcels for solar and wind farm suitability using colour-coded heatmaps that visually depict favourable locations. An indicative cost of electricity is calculated and classified for each pixel by focusing on key factors including the resource availability, proximity to transmission infrastructure and load centres, and exclusion of sensitive areas. The proposed approach mitigates the subjectivity associated with traditional multi-criteria decision-making methods, in which both the selection of siting factors and the assignment of their associated weightings rely highly on the subjective judgements of experts. The methodology is applied to Australia, South Korea, and Indonesia, and the results show that proximity to high-voltage transmission and load centres is a key factor affecting site selection in Australia and Indonesia, while connection costs are less critical in South Korea due to its smaller land area and extensive infrastructure. The outcomes of this study, including heatmaps and detailed statistics, are made publicly available to provide both qualitative and quantitative information that allows comparisons between regions and within a region. This study aims to empower policymakers, developers, communities, and individual landholders to make informed decisions and, ultimately, to facilitate strategic renewable energy deployment and contribute to global decarbonization. Full article

As the dual carbon goals are being approached, there has been an increase in the number of energy-saving renovation projects for existing buildings. However, building renovation also brings about environmental impacts and incremental costs, which need to be addressed urgently. This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. To enhance the robustness of the methodology, a comparative analysis of four different ML models—light gradient boosting machine (LightGBM), fast random forest (FRF), multivariate linear regression (MVLR), and artificial neural network (ANN)—was conducted, with LightGBM demonstrating the best performance in terms of accuracy, adaptability, and efficiency. Using the heuristic optimization algorithm and entropy-weighted method, the framework achieved average energy savings of 56.62%, a reduction in carbon emissions of 51.60%, and a 24.27% decrease in life-cycle costs. Compared to local ultra-low-energy building standards, the optimal solutions resulted in a 2.60% reduction in carbon emissions and a 15.85% decrease in life-cycle costs. This integrated framework demonstrates the potential of combining machine learning surrogate models, evolutionary generation, and entropy-weighted methods in building energy retrofitting optimizations, offering a novel, efficient, and adaptable approach for researchers and practitioners seeking to balance energy consumption, carbon emissions, and life-cycle costs in renovation projects. Full article

Biodiversity is essential for the health and stability of our planet, contributing to ecosystem services like pollination, nutrient cycling, and climate regulation. However, it faces significant threats from human activities, including habitat destruction and pollution. Transportation infrastructure, if not carefully managed, can fragment habitats and disrupt wildlife migration, exacerbating biodiversity loss. Thus, incorporating environmental and biodiversity considerations into transport planning is crucial for promoting long-term sustainability. Accordingly, the goal of this paper is to define a framework for evaluating and ranking intermodal transport routes based on their impact on the environment and biodiversity. The study employs a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) model, combining input from interactive GIS maps and stakeholders with a novel hybrid approach. The MCDM part of the model combines fuzzy Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the routes. This methodology application on several Trans-European Transport Network (TEN-T) routes revealed that the Hamburg/Bremerhaven–Wurzburg–Verona route had the least environmental and biodiversity impact. The study identified the Rotterdam–Milano route as the optimal choice, balancing sustainability, ecological preservation, and transport efficiency. The route minimizes ecological disruption, protects biodiversity, and aligns with European Union strategies to reduce environmental impact in infrastructure projects. The study established a framework for evaluating intermodal transport routes based on environmental and biodiversity impacts, balancing efficiency with ecological responsibility. It makes significant contributions by integrating biodiversity criteria into transport planning and introducing a novel combination of GIS and MCDM techniques for route assessment. Full article

This research provides a comparative analysis of different methods of weighting criteria used in the investigation of site suitability of existing onshore wind farm projects. The ranking of this suitability was performed by integrating various multi-criteria decision-making (MCDM) techniques. The assessments of the site suitability of such projects considered several criteria, including wind velocity, distance from high-electricity grids, slope, distance from road networks, installed capacity, distance from protected areas, years of operation, and distance from settlements. Both subjective and objective methods were used to compute criteria weights and compare the results, which is the main contribution of the paper. This is especially significant, as criteria weighting in the wind farm siting literature is mainly focused on subjective methods, and therefore the criteria weights are provided by subjective judgments. In this study, 374 existing onshore wind farm projects in Greece served as alternatives, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank their suitability. The results show very high positive correlations in the rankings of both the evaluation criteria and the alternatives when subjective methods are used. Using objective weighting methods may provide a robust solution when expert judgement is missing, and the CRITIC method seems to present a high correlation with subjective MCDM methods regarding the ranking of alternatives. Various MCDM methods could be used to assess the weighting of criteria in challenges related to site suitability of renewable energy projects, as they can aid in the selection of the most sustainable sites while minimizing the downsides and maximizing the benefits of each method. Full article

The implementation of predictive maintenance (PM) in aviation presents unique challenges due to strict safety requirements, complex operational environments, and regulatory constraints. This paper develops a comprehensive decision-making framework for evaluating the feasibility of implementing PM for aircraft components, addressing the critical need for systematic integration of technical, economic, and regulatory considerations. Through expert surveys involving 78 aviation maintenance professionals and the application of multi-criteria decision analysis, this study identifies and validates 14 key criteria across four categories: technical and operational, economic and feasibility, regulatory and compliance, and organizational and human factors. The analytic hierarchy process is employed to establish criteria weights, with flight safety impact, reliability predictability, and data sufficiency emerging as primary drivers. The framework’s effectiveness is demonstrated through case studies comparing turbofan engines and avionics units, validating its ability to discriminate between components suitable for PM implementation. Results indicate that successful PM implementation requires not only technological readiness but also organizational alignment and regulatory compliance. This study contributes to aviation maintenance practice by providing a structured, evidence-based approach to PM implementation decisions, while establishing a foundation for future innovations in maintenance strategies. The framework’s practical applicability is enhanced through a detailed implementation roadmap and validation methods, ensuring its relevance for maintenance decision-makers while maintaining alignment with aviation safety standards. Full article