This ongoing research develops a comprehensive model for power-constrained, multi-period hybrid data center network design and planning. The proposed framework fundamentally addresses the location-allocation problem by integrating both fixed IT infrastructure and relocatable modular units. The model simultaneously optimizes strategic capacity decisions—including site activation, fixed resource expansion, and modular unit deployment and relocation—alongside operational workload routing under stringent power and configuration constraints. To accurately capture the impact of congestion on service quality, we incorporate queueing-based delay directly into the formulation. The inclusion of this queueing term introduces significant non-linear and non-convex elements, which are central to balancing capital and operational expenditures against unmet demand and latency penalties. The problem is formulated as a mixed-integer second-order cone program (MISOCP). To efficiently solve large-scale instances of this model, we are currently investigating Lagrangian relaxation, with plans to explore and benchmark additional advanced solution methodologies in future work.

The COVID-19 pandemic is undoubtedly one of the most formidable health crises in modern history. It has impacted hundreds of millions of people worldwide, with millions of lives lost. Research groups worldwide in artificial intelligence (AI) and machine learning (ML) have made significant strides in addressing various facets of the COVID-19 crisis. Their efforts have spanned epidemiological areas, such as prediction, control, and forecasting; molecular research, including molecular modeling and drug target identification; and medical applications, like AIdriven diagnostics and treatment development. In this work, we performed a systematic literature review on AI and ML applications in addressing various challenges in COVID-19 management.

The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices.

Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades.

When it comes to logistics management, the distribution of finished goods from depots to customers is both a practical and difficult problem to solve. Because more customers can be served in a shorter period of time, better routing and scheduling decisions can result in higher levels of customer satisfaction.