Search Results (1,760)

This paper proposes an integrated framework to evaluate Corporate Social Responsibility (CSR) initiatives in peacebuilding settings using the Analytic Hierarchy Process (AHP). The model is structured around six criteria: conflict sensitivity, economic resilience, social inclusion, governance, education for peace, and sustainability, each subdivided into measurable subcriteria. A key methodological innovation is the introduction of objective grouping, which ensures that each alternative project is assessed only against the subcriteria where it generates tangible impact. Unlike the traditional AHP approach, where alternatives are evaluated against all criteria, objective grouping prevents irrelevant comparisons, reduces the cognitive burden on experts, and increases consistency in judgments. The method distinguishes between direct contributions (full weight allocation) and indirect contributions (partial weight allocation), while excluding unrelated dimensions. This refinement yields more transparent and context-sensitive prioritization, particularly relevant for fragile territories where CSR interventions must be both socially legitimate and economically viable. The empirical application shows that objective grouping highlights structural levers, such as grievance redress, local supply chain integration, peace education, and project scalability, as decisive for long-term peacebuilding. The framework thus improves decision-making by combining analytical rigor and stakeholder legitimacy, enhancing both business legitimacy and long-term societal resilience. Full article

Rapid energy planning in cities needs decision-support tools that can change based on the supply of renewable resources and the needs of stakeholders. This paper introduces an innovative adaptive decision-support framework that integrates Long Short-Term Memory (LSTM)-based short-term renewable energy forecasting with an interval-valued Pythagorean fuzzy Best-Worst Method–TOPSIS (IVPF-BWM–TOPSIS). This enables forecast-driven and temporally adaptive prioritisation of urban energy technologies, as opposed to static expert-based evaluation. Using criteria based on forecasted technical feasibility and scalability, the five green energy options that are looked at are rooftop solar, wind energy, smart grids, solar-integrated electric vehicle infrastructure, and battery energy storage. The best score is for rooftop solar (RDC = 0.65), followed by solar-integrated EV infrastructure (RDC = 0.566), and finally smart grids (RDC = 0.55). Wind energy gets the lowest score because it will not be very useful in cities. Sensitivity analysis (±20% weight change) and 15 scenario-based stress tests show that the framework is strong and does not change the order of the ranks. The results show that the proposed mixed AI and fuzzy method can be used to make plans for renewable energy in smart cities that are both based on data and can be used by many people. Full article

Multi-criteria decision-making (MCDM) problems in complex evaluation systems are often characterized by high uncertainty in expert judgments and dynamic variations in indicator importance. Traditional analytic hierarchy process (AHP) and entropy-based weighting methods typically suffer from two inherent limitations: the inability to explicitly quantify expert hesitation and the rigidity of static weight assignment under evolving data distributions. To address these challenges, this paper proposes a dynamic hybrid weighting framework that integrates an interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) with an entropy-triggered correction mechanism. First, interval-valued intuitionistic fuzzy numbers are employed to simultaneously model membership, non-membership, and hesitation degrees in pairwise comparisons, enabling a more comprehensive representation of expert uncertainty. Second, an entropy-triggered dynamic fusion strategy is developed by jointly incorporating information entropy and coefficient of variation, allowing adaptive adjustment between subjective expert weights and objective data-driven weights. This mechanism effectively enhances sensitivity to high-dispersion criteria while preserving expert knowledge in low-variability indicators. The proposed framework is formulated in a hierarchical fuzzy decision structure and implemented through a fuzzy comprehensive evaluation process. Its feasibility and robustness are validated through a concrete case study on teaching effectiveness evaluation for a university engineering course, leveraging multi-source data. Comparative analysis demonstrates that the proposed approach effectively mitigates the weight rigidity and evaluation inflation observed in conventional methods. Furthermore, it improves diagnostic resolution and decision stability across different evaluation periods. The results indicate that the proposed entropy-triggered IVIF-AHP framework provides a mathematically sound and practically applicable solution for dynamic MCDM problems under uncertainty, with strong potential for extension to other complex evaluation and decision-support systems. Full article

The degradation of building foundations, underground structures, and historical fabrics in sulfate-laden environments poses a persistent threat to the durability and safety of the built environment. Developing effective, sustainable repair materials is of paramount importance. This study presents the development, systematic optimization, and performance validation of a novel micro-expansive grout designed for high durability in aggressive sulfate conditions. The grout formulation utilizes industrial by-product fly ash, quicklime, and site-compatible soils, emphasizing sustainability. Nine chemical admixtures were screened for sulfate resistance enhancement. Laboratory experiments rigorously characterized the effects of water-to-solid ratio and admixture dosage on fresh-state properties (fluidity, setting time) and hardened-state performance (volumetric stability). To resolve a multi-objective optimization problem balancing injectability, dimensional compatibility, and cost-effectiveness, an integrated multi-criteria decision-making (MCDM) framework combining FAHP, MII, CRITIC, and TOPSIS was employed. This data-driven methodology identified an optimal formulation incorporating 3% disodium hydrogen phosphate (DSP) at a 0.58 water-to-solid ratio. The optimized grout exhibited a flow value of 75 mm, ensuring excellent injectability within the target range (40–120 mm), and an expansion rate of 7.67%, which falls within the safe range (0%–10%) to ensure dimensional compatibility. Accelerated durability tests via cyclic immersion in sodium sulfate solution demonstrated the optimized grout’s exceptional resistance to sulfate attack, retaining approximately 88% of its compressive strength after 15 aggressive cycles. The balanced properties and validated durability indicate strong potential for this grout in demanding repair scenarios. One key example is the repair of fissures in earthen heritage structures, which requires extreme material compatibility and long-term performance. Full article

Electromobility is increasingly recognized as a cornerstone of sustainable transport, yet its adoption remains uneven across regions. This study develops an integrated framework that combines geospatial analysis, multi-criteria decision-making (MCDM), and power system evaluation to identify and prioritize fast-charging sites at the national scale. Applied to Costa Rica’s national road network (NRN), encompassing both urban centers and peripheral regions, the framework integrates spatial suitability, socioeconomic priorities, and grid readiness across projected electric vehicle (EV) penetration scenarios. Critically, power system simulations reveal voltage instability at distribution nodes (as low as 89.88% p.u.) under 3% EV penetration despite 99% renewable generation, demonstrating that grid capacity, not planning methodology, constitutes the primary barrier to electric mobility adoption. This finding, derived from the first national-scale analysis that integrates equity-driven spatial prioritization with comprehensive grid validation using real fleet projections, challenges conventional assumptions in transport-focused infrastructure planning. The framework provides a transferable tool for countries seeking to align EV infrastructure planning with sustainability and decarbonization objectives, while highlighting that grid reinforcement must precede, not follow, the deployment of fast-charging infrastructure. Full article

There are several frameworks for corporate sustainability indicators (GRI, B-LAB, WBCSD, etc.) which allow for the selection of indicators based on convenience or subjective criteria. In this work, we aim to propose a common framework based on relevant indicators for companies operating in the Mexican context to provide a basis for their transition to sustainability. We analyzed five international corporate sustainability indicator frameworks and decided to use the CoP, BIA, and CSA evaluation tools. The set of indicators (n = 294) was evaluated by a panel of judges with profiles covering several fields of sustainability, followed by an analysis of agreement using Kendall’s W. The weighting for each judge was equally distributed, and the TOPSIS Multicriteria Decision Method (MCDM) was used to calculate the Relative Proximity Coefficient (RPC) for each indicator. The RPC values were stratified into priority levels and used to build a model to guide Mexican enterprises toward sustainability, which indicates that planet factors initially have the highest priority. As the company grows, it should incorporate more sustainable elements across the social, governance, and prosperity spheres. The model may serve as a roadmap for companies seeking sustainability. Full article

This study addresses the site selection problem for energy storage systems (ESSs) as a multi-criteria decision-making problem (MCDM) under conditions of uncertainty. First, potential candidate locations were identified using the K-means clustering algorithm based on the geographic coordinates of existing solar power plants (SPPs). As a result, six alternative locations representing spatial concentration were identified. These alternatives were then evaluated using the fuzzy TOPSIS method, a multi-criteria decision-making method (MCDM), taking into account the ten criteria defined for this study. Expert assessments were expressed and transformed into triangular fuzzy numbers to capture uncertainty and subjectivity in the decision-making process. The results show six alternative options, ranked from the one with the highest proximity coefficient to the one with the lowest. The findings demonstrate that the integrated use of machine learning (ML) and fuzzy TOPSIS methods provides an effective and robust decision support framework for ESS location selection problems. This approach also serves as a guide for other renewable energy planning practices. Full article

Background: Freight allocation is a vital decision in distribution logistics to minimize costs and gain environmental benefits. In this paper, we address the problem of freight allocation optimization on an HSR intermodal network with application for the Ottawa–Quebec City corridor where the HSR system will be constructed. Methods: We develop a novel allocation method in which GNNs encode the intermodal network topology and spatial features, while RL agents learn adaptive freight routing policies through reward optimization, which is enhanced by fractal accessibility metrics for spatial connectivity and MCDM for balancing cost, emissions, and service objectives as well as optimizing dynamic freight flows. The model incorporates geospatial data (population, distance), operational factors (demand, costs), and environmental or policy considerations. Addressing the gap in dynamic, multi-criteria cold-climate HSR freight allocation models for North America, we test our framework on the Ottawa–Quebec corridor. Results: The result shows that compared to traditional methods, the five-hub configuration reduces costs by 15–22% and emissions by 20–28%, while the 11-hub model maintains 94%+ service coverage with an 8–12% efficiency trade-off. Conclusions: The conclusion indicates that the HSR intermodal network is more efficient than road only. Sensitivity analysis highlights that key allocation offers policymakers and logistics planners actionable insights for balancing efficiency and accessibility in HSR freight networks. Full article

Against the backdrop of global climate change and dual carbon goals, the logistics industry, which accounts for about 14% of global greenhouse gas emissions, needs green transformation. Yet, existing evaluation systems have fragmented dimensions, excessive subjectivity, and insufficient objectivity. This study aims to construct a scientific evaluation framework. It uses two rounds of the Delphi method to screen 13 core indicators, forming an environment–economy–society–technology four-dimensional system, and it improves the three-interval TOPSIS method with interval-type ideal solutions, supplemented by Spearman and Pearson dual verification. Taking 24 core cities in the Yangtze River Delta as samples, the results show hierarchical and regional differences: 3 cities (Shanghai, Suzhou, Hangzhou) are excellent, 12 are good, and 9 are qualified. The evaluation system unifies standards for cross-regional and industry comparisons, which works as a quantitative tool for government policy-making and enterprise green transformation, contributing to the development of the logistics industry’s low-carbon intelligent and the achievement of dual carbon goals. Full article

Choosing sites for solar photovoltaic (PV) power plants in developing countries like India is a crucial task while considering multiple conflicting factors and sub-factors simultaneously. Multi-criteria decision-making (MCDM) is an optimisation method that provides a framework for handling such situations in an intuitionistic fuzzy environment. The complexity and uncertainty associated with the site selection model are dealt with professionally. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the relative importance of the criteria, identifying airflow speed as the most influential factor, followed by humidity ratio, level of dust haze, availability of labour and resources, and ecological effects. This shows that airflow speed plays an important role in the power plant’s efficiency and performance. The Vlse Kriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method is then used to prioritise the alternatives as potential locations for setting up a solar PV power plant in India. A new de-i-fuzzification method based on the relative difference between two real numbers is also proposed. Sensitivity analyses and comparative studies are conducted to assess the robustness and effectiveness of the framework. Overall, the results demonstrate that the proposed framework is useful and effective for optimising site selection for solar power plants in India. Full article

Green hydrogen production is a fundamental component of the sustainable energy transition; however, the success of such projects largely depends on the strategic selection of reliable and sustainable equipment suppliers. Supplier selection plays a critical role in aligning operational performance with long-term objectives, including technological competitiveness, environmental sustainability, and societal acceptance. Nevertheless, conventional multi-criteria decision-making (MCDM) approaches remain insufficient in adequately capturing the uncertainty, subjectivity, and group decision-making dynamics inherent in real-world supplier evaluation processes. To address this gap, this study proposes a group decision-making supported Spherical Fuzzy TOPSIS (SF-TOPSIS) framework for selecting sustainable green hydrogen production equipment suppliers. Within the model, ten evaluation criteria covering technical, economic, environmental, and social dimensions are defined to ensure alignment between supplier selection decisions and the strategic orientation of the business unit. The empirical findings, based on aggregated global fuzzy weights and relative closeness values, indicate that technical criteria such as electrolyzer efficiency and technical competence (C1), hydrogen safety (C2), and system robustness (C3) are decisive in the evaluation process. Moreover, the social criterion representing local supplier contribution and societal acceptance (C9) has been identified as playing a critical role, highlighting the increasing importance of social legitimacy and regional integration in sustainable hydrogen investments. These findings are derived directly from the model’s quantitative outputs, without relying on prior assumptions, reflecting the strategic significance of the criteria for operational reliability and long-term sustainability. The primary methodological contribution of this study lies in the development of a spherical fuzzy group decision-making framework capable of addressing multidimensional uncertainties across technical, economic, environmental, and social dimensions. This framework provides decision-makers with a reliable, systematic ranking tool for selecting sustainable hydrogen production equipment suppliers under complex uncertainty. From a practical perspective, the proposed model enables stakeholders to quantitatively assess trade-offs between technological performance and socio-economic impacts and serves as a guiding tool for strategic decision-making. Full article

Industrial Symbiosis (IS) represents a key strategy within the Circular Economy (CE) paradigm, enabling firms located near enhance competitiveness through the collective exchange and valorisation of resources. By fostering the reuse of water, energy, and materials, IS contributes to the sustainable optimization of manufacturing processes. Nevertheless, the implementation of IS, as a distinct business model, requires the active collaboration of heterogeneous stakeholders, which often generates critical challenges in aligning interests and achieving equitable benefits. This study explores an innovative approach to agri-food symbiosis by evaluating the incorporation of Olive Oil Mill Wastewater (OOMW), a by-product of olive oil production, into bread formulations. This strategy not only mitigates the environmental burden associated with OOMW disposal but also promotes resource efficiency within the olive oil supply chain. Bread samples were produced by varying the concentration of OOMW, and each formulation was assessed according to quality characteristics, consumer acceptability parameters, and sustainability aspects. The selection of the best-performing formulation was conducted through a Multi-Criteria Decision-Making (MCDM) framework, specifically applying the VIKOR method. The findings highlight how the integration of OOMW into bread production can generate a dual benefit, improving food quality while advancing sustainable practices in both the olive oil and bakery sectors. Full article

Effective management of Environmental, Social, and Governance (ESG) practices within the framework of transparency and accountability in businesses is crucial for enhancing their compliance capacity in the face of regulatory pressures and contributing to the early detection of environmental and social risks. This study aims to evaluate the ESG disclosure-based performance of businesses operating in the textile, clothing, and leather sectors in Turkey by examining their ESG indicators from a transparency and accountability perspective. The CRITIC (Criteria Importance Through Intercriteria Correlation) method was used to determine the relative importance levels of the indicators, while the MABAC (Multi-Attributive Border Approximation Area Comparison) and COPRAS (Complex Proportional Assessment) methods were used to rank the performance of businesses within the framework of these indicators. Finally, clustering analysis was used to classify businesses with similar characteristics. The findings show that corporate governance principles are the most important indicator, and that Kordsa Teknik Tekstil A.Ş. and Söktaş Tekstil Sanayi ve Ticaret A.Ş. exhibit a significant and positive difference in terms of transparency and accountability in their ESG practices compared to other businesses. The combined use of CRITIC, MABAC, COPRAS, and cluster analysis offers an innovative, robust decision-making approach and holistic methodological integration for assessing ESG disclosure-based performance in the context of transparency and accountability for businesses. Full article

Manufacturing industries are increasingly applying sustainable closed-loop supply chains (CLSCs) to meet economic, environmental, and societal goals. The increasing complexity and interdependence associated with the sustainability CLSCs make them highly vulnerable to disruption risks that threaten continuity and sustainability. However, prior studies fall short of guiding how disruption risks in sustainable CLSCs can be systematically prioritized under uncertainty in a stable and decision-relevant manner. To fill this literature void, this study develops a hybrid of the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) method and the genetic algorithm (GA) technique to prioritize disruption risks under uncertainty. Triangular fuzzy numbers are used to capture the imprecision of 13 experts from industry and academia, whereas the GA technique used aimed to improve stability and reduce the variability commonly observed in conventional fuzzy multi-criteria decision-making methods. The method is validated through a real-world case study, identifying supplier disruption risk, route disruption risk, and industrial accidents as the most critical risks. Moreover, sensitivity analysis is conducted to validate the robustness of GA-based Fuzzy-TOPSIS, demonstrating its superior stability and reliability compared to the classical Fuzzy-TOPSIS method in uncertain environments. The novelty of this study lies in embedding a GA-driven approach within the fuzzy-TOPSIS structure to explicitly address ranking instability under uncertainty in sustainable CLSCs. The study provides significant theoretical contributions by enhancing multi-attribute decision-making regarding disruption risk in sustainable CLSC literature, as well as practical insights for decision-makers to efficiently allocate resources by focusing mitigation investments on consistently high-priority risks instead of low-priority ones. Full article

In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation. Full article