Search Results (402)

Traffic signal control at urban intersections is one of the key determinants of the overall efficiency of the transportation system, given its direct impact on travel time, congestion levels, and emissions of exhaust fumes. This study proposes an integrated hybrid model that combines a metaheuristic Genetic Algorithm for generating potential signal timing plans with fuzzy multi-criteria decision-making (MCDM) for their evaluation and selection of the optimal solution. In order to determine the relative importance of criteria, the fuzzy methods F-AHP, F-FUCOM, and F-PIPRECIA were employed, thus providing stable assessments of criteria importance under conditions of uncertainty and expert subjectivity. The ranking of generated alternatives was performed by employing the F-TOPSIS, F-WASPAS, and F-ARAS methods, while the robust decision-making rule approach was employed to develop a robust decision-making rule by integrating multiple MCDM methods. The proposed model was tested using data collected from a real urban intersection. The results show that the integrated hybrid approach enables a significantly more reliable selection of the optimal signal timing plan and achieves higher traffic management efficiency compared to traditional methods. The proposed model provides a flexible and scalable framework that can be adapted to different types of intersections and traffic demand conditions, thereby significantly contributing to the development of modern intelligent traffic management systems. Full article

Flood events represent a major natural threat, and identifying the key factors contributing to flood occurrence has gained considerable attention in 2010 and 2022 in the Swat River, Pakistan. In this study, Google Earth Engine was utilized to extract flood-related indices for the Mohmand Dam catchment, Pakistan. Different types of datasets were used to calculate fourteen influencing parameters. These indices were processed and normalized in ArcMap 10.8 and Python to enhance their visual and analytical representation. Two multi-criteria analyses with AHP, FAHP, and five machine learning models, including logistic regression, K-nearest neighbors, random forest, support vector machine, and multi-layer perception, were applied to determine the relative importance of each parameter and produce a flood susceptibility map. The results indicate that rainfall, LULC, and soil texture are the most influential factors, each contributing 11.11% to flood susceptibility. The random forest approach demonstrated stronger predictive performance than the AHP and FAHP techniques. The flood susceptibility map reveals that approximately 31.67% (4320.40 km²) of the study area falls under high flood risk. This methodology provides valuable support for planners, policymakers, hydrologists, and disaster management authorities in developing effective flood mitigation, watershed management, and resilience strategies. Full article

This paper presents an active prevention and control technology for bed separation water inrush hazards, the effectiveness of which has been validated. Based on the hazard degree identification of such hazards and corresponding preventive measures, the Fuzzy Analytic Hierarchy Process (FAHP) and Expert Grading System (EGS) are adopted to analyze the prevention mechanisms and determine the indicator weights of different influencing factors. The results show that enhancing drainage capacity and accurately predicting bed separation water inflow are two effective measures to prevent water inrush or reduce the hazard risk coefficient. In addition, controlling the development of water-conducting fractured zones and optimizing drainage measures are also effective approaches to reducing the risk coefficient. The research results provide a theoretical basis and practical guidance for the prevention and control of bed separation water inrush hazards, and offer an effective and cost-efficient method for addressing such mining-induced hazards. Full article

Urban flood resilience is an important indicator for measuring a city’s capacity to respond to and recover from flood disasters. However, existing assessments often lack a long-term hydrological baseline. This study establishes the historical water system of Yuan Dadu (1267–1368 CE) as a control scenario to benchmark the flood resilience of modern Beijing. By integrating a historical geographic reconstruction with a hydrological–hydrodynamic simulation and the fuzzy analytic hierarchy process (FAHP), the research quantifies structural differences in resilience profiles between the nature-adapted historical system and the modern engineering-dominated system. The results indicate that Yuan Dadu’s urban flood resilience index (UFRI) is 3.44 and modern Beijing’s is 3.28. Despite modern Beijing’s significant advantage in drainage facility density (0.61 km/km²) and emergency management, the system exhibits a functional substitution failure, where gray infrastructure has failed to fully compensate for a 26% reduction in the unit area storage capacity (from 6.4 to 4.7 × 10⁴ m³/km²) and a 48.4% decline in the water system structural complexity. The findings indicate that, in rapidly urbanized cities on alluvial plains with high impervious coverage, expanding drainage networks alone may be insufficient to offset losses in a natural hydraulic buffering capacity. Accordingly, planning strategies are proposed that integrate distributed micro-storage and restore topological connectivity to recreate system-level hydraulic buffering functions. Full article

Tourism destinations exposed to chronic natural hazards require robust analytical frameworks to understand and prioritize the factors that sustain post-disaster resilience. This study examines Baños de Agua Santa (Ecuador), a volcano-exposed destination whose long recovery trajectory illustrates the complexity of socio-ecological adaptation. Using a multidimensional FAHP model grounded in expert judgments, eight dimensions and fifty-six criteria were evaluated through fuzzy triangular numbers and the extended analysis method of Chang to capture uncertainty and ambiguity in decision-making. Results show a consistent and hierarchical structure of resilience, with experiential, economic-entrepreneurial, and socio-community dimensions emerging as the most influential drivers of post-disaster adaptability. Fifteen criteria—primarily perceptual, community-based, and endogenous—achieved “very high impact” status, including risk perception, basic education, individual resilience capacities, institutional coordination, and entrepreneurial environment. Conversely, limited healthcare infrastructure, low economic diversification, and national-level vulnerabilities were identified as critical weaknesses. The study concludes that post-disaster recovery in Baños is shaped by a bottom-up dynamic that emphasizes agency, learning and socio-ecological memory. It also proposes an evidence-based Action Matrix for adaptive governance to guide prioritized, time-phased interventions. The FAHP model proves effective for transparent, context-sensitive prioritization in highly uncertain tourism environments. Full article

Forest parks are expected to balance ecological protection with high-quality ecotourism experiences, yet quantitative, multi-dimensional assessments at the regional scale remain limited. This study evaluates ecotourism experience quality across 37 national forest parks in Shaanxi Province, China, using a five-dimensional, 32-indicator framework (science popularization, landscape esthetics, ecological resources, cultural resources, and supporting conditions). Indicator weights were elicited through the Fuzzy Analytic Hierarchy Process (FAHP), and park performance was ranked via TOPSIS; key constraints were diagnosed using an obstacle degree model. Results show that landscape esthetics received the highest criterion weight (0.245), followed by ecological resources (0.215) and supporting conditions (0.195). The mean experience score across parks was 0.559, with high-quality parks concentrated in the Guanzhong region. Obstacle analysis indicates that insufficient cultural resource protection and interpretation, inadequate public service facilities, and weak science-education functions are the most persistent barriers to improving experience quality. The proposed FAHP–TOPSIS–obstacle framework supports benchmarking and priority setting for sustainable management and targeted investments in forest parks. Full article

Cold vaccine delivery is often known as a high-cost logistic process, which forces many pharmaceutical manufacturers, particularly small- and medium-sized enterprises (SMEs), to subcontract logistics operations of vaccines to third-party logistics (3PL). It is clear that maintaining the traceability and trackability of vaccines in this dynamic collaborative environment is fundamental for guaranteeing the safety of product. However, the lack of a unified vaccine logistics platform holds back comprehensive supervision and traceability, posing significant challenges to the development of useful cold chain logistics systems. To address these challenges, in this study we propose a blockchain-enabled platform for the evaluation and selection of 3PL providers in vaccine supply chains. We leveraged consortium blockchain technology to guarantee data integrity, transparency, and decentralization, facilitating trust among four main players of vaccine supply chain. We utilized smart contracts as a main part of this platform, which are responsible for automating key operational processes, including 3PL evaluation, contract execution, and monitoring. In this respect, the Fuzzy Analytic Hierarchy Process (FAHP) engine is integrated into the proposed platform to enable a data-driven, multi-criteria decision-making framework for selecting the most suitable 3PL providers. We evaluated the proposed platform through case study and gas consumption analysis; the results of the case study validate high operational accuracy (93.21%), precision (90.23%), recall (94.50%), and an F1-score of 92.32% for the platform, which offers a robust solution to enhance accountability, reliability, and decision-making in vaccine distribution networks. 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

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

This paper proposes a two-stage framework integrating Data Envelopment Analysis (DEA) and fuzzy multi-criteria decision-making methods to evaluate the performance of logistics firms in Vietnam. In the first stage, DEA models (CCR, BCC, and SBM) are employed to measure relative efficiency and identify benchmark firms among 15 leading logistics companies. In the second stage, FAHP–FTOPSIS is used to incorporate qualitative and sustainability-oriented criteria and to provide a comprehensive ranking of the efficient firms. The results indicate that a considerable proportion of firms operate below the efficiency frontier, implying substantial opportunities for resource optimization. Environmental and technological dimensions are found to be the most influential factors, while companies implementing green distribution strategies and strong data security practices consistently achieve higher rankings. Sensitivity analysis confirms the robustness and stability of the proposed framework. This study contributes by bridging operational efficiency assessment with broader strategic and sustainability considerations, overcoming the limitations of single-method evaluations used in prior research. The integrated DEA–FAHP–FTOPSIS approach offers managers a practical tool to diagnose weaknesses, prioritize improvement actions, and benchmark against top performers. In addition, it offers policymakers valuable insights to support digital transformation and green logistics initiatives in developing economy contexts. Full article

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

The safe utilization of underground spaces constitutes a critical challenge for densely populated cities, making geosafety risk prevention in underground development a focal point for both academic research and governmental governance. As the pivotal link and ultimate objective in geological safety management, risk prevention facilitates the transition from theoretical research to administrative practice. This study establishes a management-oriented technical framework for geological risk preventive zoning in underground space utilization, addressing the current research gap where zoning methodologies inadequately integrate with governmental decision-making processes due to insufficient consideration of multidimensional attributes from both researcher and administrator perspectives. Taking Xiong’an New Area in China as a case study, the framework employs a tri-level analytical structure, restrictive tier, limiting tier, and influencing tier, with phased weighting methodologies, CRITIC-EWM for objective weighting vs. AHP-FAHP for subjective weighting. The scientifically validated results demonstrate the framework’s feasibility and scalability. Limitations and future research directions are identified to guide subsequent studies in this field. Full article

Directional swelling pressure is a critical yet often overlooked factor governing the instability of expansive soil slopes. Most existing studies simplify swelling behavior as a uniform or purely vertical stress, thereby underestimating the distinct contribution of horizontal swelling pressure. In this study, an integrated framework combining the Fuzzy Analytic Hierarchy Process (FAHP), multivariate regression analysis based on 35 expansive soil samples, and three-dimensional strength-reduction numerical modeling was developed to systematically evaluate the mechanistic roles of vertical and horizontal swelling pressures in slope deformation. The FAHP and regression analyses indicate that water content is the dominant factor controlling both the free swell ratio and swelling pressure, leading to predictive relationships that link swelling behavior to fundamental physical indices. These empirical correlations were subsequently incorporated into a three-dimensional numerical model of a representative Neogene expansive soil slope. The simulation results demonstrate that neglecting swelling pressure results in substantial discrepancies between predicted and observed displacements. Vertical swelling pressure induces moderate surface uplift but exerts a limited influence on overall failure patterns. In contrast, horizontal swelling pressure markedly amplifies downslope displacement—by more than four times under saturated conditions—reduces the factor of safety by 24.7%, and promotes the progressive development of a continuous slip surface. These findings clearly demonstrate that horizontal swelling pressure is the dominant driver of progressive failure in expansive soil slopes. This study provides new mechanistic insights into swelling-induced deformation and offers a quantitative framework for incorporating directional swelling stresses into slope stability assessment, design optimization, and mitigation strategies for geotechnical structures in expansive soil regions. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article