Utility vehicle fleet, Efficiency, Sustainability, Waste management, Data Envelopment Analysis (DEA), Criteria Importance Through Intercriteria Correlation (CRITIC), Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Urban waste

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 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.

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.