This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction.
The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts.
Manufacturing firms face increasing pressure to enhance their competitiveness, penetrate new markets, and prioritise customer satisfaction in an increasingly dynamic global business environment. To remain competitive, these firms must adopt innovative strategies that address the evolving demands of customers. In this context, a firm’s capacity to innovate is critical, as it directly influences both the development and implementation of strategic initiatives. Innovation capacity in manufacturing companies is shaped by numerous interrelated factors, each contributing to a firm's ability to respond to technological advancements, market shifts, and changing consumer expectations. This study aims to identify the key determinants of innovation capacity in manufacturing firms based in Ordu Province, Turkey, with a focus on the role of corporate identity. A multi-criteria decision-making (MCDM) approach, specifically the Criteria Importance Assessment (CIMAS) technique, is employed to determine the relative importance of these factors. The findings suggest that “clustering and international networking activities” emerge as the most significant factor influencing innovation capacity, while the “level of entrepreneurship” is found to have the least impact. These results underscore the importance of collaboration, international connections, and strategic partnerships in driving innovation, while highlighting the comparatively limited role of entrepreneurship in fostering innovation within the studied region. The findings have significant implications for manufacturing firms, particularly in terms of strategy development, resource allocation, and the identification of key areas for improvement in innovation processes. Additionally, the research provides valuable insights for policymakers seeking to enhance the innovation capacity of manufacturing sectors in emerging markets.
Evaluating renewable energy policies is crucial for fostering sustainable development, particularly within the European Union (EU), where energy management must account for economic, environmental, and social criteria. A stable framework is proposed that integrates multiple perspectives by synthesizing the rankings derived from four widely recognized Multi-Criteria Decision Analysis (MCDA) methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and Multi-Objective Optimization by Ratio Analysis (MOORA). This approach addresses the inherent variability in individual MCDA techniques by applying Copeland’s compromise method, ensuring a consensus ranking that reflects the balanced performance of renewable energy systems across 16 EU countries. To further enhance the reliability of the framework, the Stochastic Identification of Weights (SITW) approach is employed, optimizing the criteria weights and strengthening the consistency of the evaluation process. The results reveal a strong alignment between the rankings generated by individual MCDA methods and the compromise rankings, particularly among the highest-performing alternatives. This alignment highlights the stability of the framework, enabling the identification of critical drivers of renewable energy policy performance—most notably energy efficiency and environmental sustainability. The compromise approach proves effective in balancing multiple, sometimes conflicting perspectives, offering policymakers a structured tool for informed decision-making in the complex domain of energy management. The findings contribute to the development of advanced frameworks for decision-making by demonstrating that compromise rankings can offer robust solutions while maintaining methodological consistency. Furthermore, this framework provides valuable insights into the complex dynamics of renewable energy performance evaluation. Future research should explore the applicability of this methodology beyond the EU context, incorporating additional dimensions such as social, technological, and institutional factors, and addressing the dynamic evolution of energy policies. This framework offers a solid foundation for refining policy evaluation strategies, supporting sustainable energy management efforts in diverse geographic regions.
The integration of Electric Vehicles (EVs) into modern power grids presents both challenges and opportunities. This study investigates the influence of slack bus compensation on the stability of voltage levels within these grids, particularly as EV penetration increases. A comprehensive simulation framework is developed to model various grid configurations, accounting for different scenarios of EV load integration. Historical charging data is meticulously analysed to predict future load patterns, indicating that heightened levels of EV integration lead to a notable decrease in voltage stability. Specifically, voltage levels were observed to decline from 230 V to 210 V under conditions of 100% EV penetration, necessitating an increase in slack bus compensation from 0 MW to 140 MW to sustain system balance. Advanced machine learning techniques are employed to forecast real-time load demands, significantly reducing both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), thereby optimising slack bus performance. The results underscore the critical role of real-time load forecasting and automated control strategies in addressing the challenges posed by EV integration into power grids. Furthermore, the study demonstrates that intelligent systems, coupled with machine learning, can enhance power flow management and bolster grid stability, ultimately improving operational efficiency in the distribution of energy. Future research will focus on refining machine learning models through the utilisation of more granular data sets and exploring decentralized control methodologies, such as federated learning, thereby providing valuable insights for grid operators as the adoption of EVs continues to expand.
Bibliometric analysis is a quantitative research method employed to measure and assess the impact, structure, and trends within academic publications. It aims to uncover patterns, connections, and research gaps either within a specific field or across interdisciplinary domains. This study utilizes bibliometric methods to investigate research gaps within the digital business domain, focusing on qualitative insights identified in existing literature. A systematic literature review (SLR) approach is adopted to ensure a rigorous synthesis of relevant studies. The analysis follows three key phases: data collection, bibliometric evaluation, and data visualization. Through these phases, trends, thematic gaps, and areas for future exploration are identified, offering a clearer understanding of the evolution and direction of digital business research. The insights derived are intended to inform sustainable business practices, with implications for environmentally conscious business models, value-driven marketing strategies, and the integration of sustainable operations. Moreover, the findings highlight potential avenues for enhanced technological innovation and interdisciplinary collaboration in digital business. This study provides a robust framework for scholars seeking to explore uncharted areas within digital business and offers actionable guidance on key research themes requiring further investigation. The use of bibliometric tools ensures comprehensive coverage of existing literature and fosters the development of a coherent research agenda aligned with emerging trends in the field.
Container-based virtualization has emerged as a leading alternative to traditional cloud-based architectures due to its lower overhead, enhanced scalability, and adaptability. Kubernetes, one of the most widely adopted open-source container orchestration platforms, facilitates dynamic resource allocation through the Horizontal Pod Autoscaler (HPA). This auto-scaling mechanism enables efficient deployment and management of microservices, allowing for rapid development of complex SaaS applications. However, recent studies have identified several vulnerabilities in auto-scaling systems, including brute force attacks, Denial-of-Service (DoS) attacks, and YOYO attacks, which have led to significant performance degradation and unexpected downtimes. In response to these challenges, a novel approach is proposed to ensure uninterrupted deployment and enhanced resilience against such attacks. By leveraging Helm for deployment automation, Prometheus for metrics collection, and Grafana for real-time monitoring and visualisation, this framework improves the Quality of Service (QoS) in Kubernetes clusters. A primary focus is placed on achieving optimal resource utilisation while meeting Service Level Objectives (SLOs). The proposed architecture dynamically scales workloads in response to fluctuating demands and strengthens security against autoscaling-specific attacks. An on-premises implementation using Kubernetes and Docker containers demonstrates the feasibility of this approach by mitigating performance bottlenecks and preventing downtime. The contribution of this research lies in the ability to enhance system robustness and maintain service reliability under malicious conditions without compromising resource efficiency. This methodology ensures seamless scalability and secure operations, making it suitable for enterprise-level microservices and cloud-native applications.
The traditional manufacturing sector in China is increasingly challenged by rising labour costs and the diminishing demographic advantage. These issues exacerbate existing inefficiencies, such as limited value addition, high resource consumption, prolonged production cycles, inconsistent product quality, and inadequate automation. To address these challenges, a production scheduling framework is proposed, guided by three key objectives: the prioritisation of high-value orders, the reduction of total processing time, and the earliest possible completion of all orders. This study introduces a multi-objective constrained greedy model designed to optimise scheduling by balancing these objectives through maximum weight allocation, shortest processing time selection, and adherence to the earliest deadlines. The proposed approach incorporates comprehensive reward and penalty factors to account for deviations in performance, thus fostering a balance between operational efficiency and product quality. By implementing the optimised scheduling strategy, it is anticipated that significant improvements will be achieved in production efficiency, workforce motivation, product quality, and organisational reputation. The enhanced operational outcomes are expected to strengthen the core competitiveness of enterprises, particularly within the increasingly complex landscape of pull production systems. This research offers valuable insights for manufacturers seeking to transition towards more efficient, automated, and customer-centric production models, addressing both short-term operational challenges and long-term strategic objectives.
Logistics performance plays a pivotal role in fostering economic growth and enhancing global competitiveness. This study aims to evaluate the logistics performance of G8 nations through multi-criteria decision-making (MCDM) models. Standard Deviation (SD) has been applied to determine the weights of evaluation criteria, while the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) has been employed to rank the countries based on their performance. The findings indicate that Timeliness emerges as the most critical factor influencing logistics efficiency. Among the G8 nations, Germany achieves the highest logistics performance, reflecting the robustness of its logistical infrastructure and operational efficiency. The results reinforce the premise that logistics performance is instrumental to both international trade and economic competitiveness. Nations demonstrating strong logistical capabilities are better positioned to excel in global markets, while those with underdeveloped logistics systems may face increased economic vulnerabilities. Enhancing logistical frameworks, including infrastructure and systems, is therefore essential for nations striving to improve their global standing. The insights presented underscore the importance of strategic investment in logistics infrastructure as a key policy instrument for enhancing economic resilience and international trade potential.
The efficiency of utility vehicle fleets in municipal waste management plays a crucial role in enhancing the sustainability and effectiveness of non-hazardous waste disposal systems. This research investigates the operational performance of a local utility company's vehicle fleet, with a specific focus on waste separation at the source and its implications for meeting environmental standards in Europe and beyond. The study aims to identify the most efficient vehicle within the fleet, contributing to broader goals of environmental preservation and waste reduction, with a long-term vision of achieving "zero waste". Efficiency was evaluated using Data Envelopment Analysis (DEA), where key input parameters included fuel costs, regular maintenance expenses, emergency repair costs, and the number of minor accidents or damages. The output parameter was defined as the vehicle's working hours. Following the DEA results, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was employed to assign weightings to the criteria, ensuring an accurate reflection of their relative importance. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was then applied to rank the vehicles based on their overall efficiency. The analysis, conducted over a five-year period (2019-2023), demonstrated that Vehicle 3 (MAN T32-J-339) achieved the highest operational efficiency, particularly in 2020. These findings underscore the potential for optimising fleet performance in waste management systems, contributing to a cleaner urban environment and aligning with global sustainability objectives. The proposed model provides a robust framework for future applications in similar municipal settings, supporting the transition towards more eco-friendly waste management practices.
This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature.
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