Background: The global logistics industry is facing looming challenges related to labor shortages and low-efficiency problems due to the lack of logistics facilities and resources, resulting in increased logistics delays. The Physical Internet is seen as a way to take logistics into the next generation of transformation. This research proposes a Physical Internet-enabled system that allows multiple companies to efficiently share warehouses and trucks to achieve operational efficiency and reduce CO₂ emissions. Methods: We propose a novel demography-weighted combinatorial optimization model utilizing a genetic algorithm and the Lin–Kernighan heuristic. The model is tested with real data simulations to evaluate its performance. Results: The results show that compared to the existing model presented in a previous study, our proposed model improves location optimality and distributive routing efficiency and reduces CO₂ emissions by 54%. Conclusions: By providing a well-founded novel model, this research makes an important contribution to the implementation of the Physical Internet by computing optimal logistics hubs and routes as well as providing a solution to cut CO₂ emissions by half.

Graph-based retrieval-augmented generation (GraphRAG) represents an innovative advancement in natural language processing, leveraging the power of large language models (LLMs) for complex tasks such as policy generation. This research presents a GraphRAG model trained on YouTube data containing keywords related to logistics issues to generate policy proposals addressing these challenges. The collected data include both video subtitles and user comments, which are used to fine-tune the GraphRAG model. To evaluate the effectiveness of this approach, the performance of the proposed model is compared to a standard generative pre-trained transformer (GPT) model. The results show that the GraphRAG model outperforms the GPT model in most prompts, highlighting its potential to generate more accurate and contextually relevant policy recommendations. This study not only contributes to the evolving field of LLM-based natural language processing (NLP) applications but also explores new methods for improving model efficiency and scalability in real-world domains like logistics policy making.

The rapid expansion and interdisciplinary nature of Physical Internet (PI) research have resulted in fragmented knowledge, limiting the ability of stakeholders to identify emerging trends, actionable insights and genuine research gaps. This study introduces a novel knowledge management approach that uses Graph Retrieval-Augmented Generation (GraphRAG) to systematically organize and integrate PI-related literature. A comprehensive knowledge graph was constructed by extracting and semantically modeling entities and relationships from 2835 academic papers, conference proceedings and international roadmaps. The developed system incorporates fuzzy semantic search and multiple retrieval strategies, including local, global and hybrid approaches, enabling nuanced, context-aware access to information. Stakeholder-specific prompts, tailored to the needs of industry, government and academia, demonstrate how GraphRAG can support the discovery of business model innovations, policy design and underexplored research areas. A comparative evaluation using cosine similarity and BERTScore confirms that graph-based strategies outperform standard LLM retrieval in providing relevant and comprehensive answers while also revealing connections that would be missed in manual reviews. The results demonstrate that the proposed GraphRAG model is a scalable and extensible framework for addressing knowledge gaps and promoting collaboration in PI research synthesis for sustainable logistics. The model also shows promise for application in other complex domains.

Background: Logistics challenges, such as driver shortages, are a major global issue, with many countries struggling to find effective solutions. YouTube, as a social networking platform, has a growing user base and is increasingly used not only for entertainment but also for social interaction, such as commenting, searching, and browsing, and it can thus potentially be used as an indicator of the topic under discussion. Methods: This study collects YouTube data containing keywords related to logistics issues—particularly the 2024 problem—and applies natural language processing (NLP) techniques to explore potential solutions. It is the first study to analyze both subtitle and comment data extracted from YouTube audio as large-scale text data in the field of logistics. Results: The analysis identified four primary areas of concern in logistics: time management, driver welfare, technological investment, and policy transparency. Sentiment analysis revealed a predominant negative sentiment in user discussions, highlighting dissatisfaction with current logistics policies and operations. Conclusions: The findings provide new insights that could inform the development of effective logistics policies and improve services for logistics companies while also proposing innovative research methods using NLP.

Rice crop residue burning (RCRB) in India constitutes a major annual environmental crisis, contributing significantly to regional air pollution, greenhouse gas emissions, and public health deterioration across the Indo-Gangetic Plain. Despite growing policy attention, a systematic, data-driven understanding of the diverse perspectives—agricultural, environmental, economic, and socio-political—expressed across multiple textual sources remains lacking. This study proposes a large language model (LLM)-driven topic modeling pipeline leveraging TopicGPT, an instruction-tuned prompting framework, to extract and evaluate high-level thematic insights from heterogeneous text corpora related to RCRB in India. Our pipeline integrates four sequential stages—topic generation, topic refinement, multi-label topic assignment with grounded evidence, and assignment correction—operated via a locally deployed LLM through the Ollama inference framework. Post-extraction, we evaluate topic quality using ten quantitative metrics encompassing embedding-based coherence, inter-topic diversity, Silhouette score, Davies–Bouldin index, Calinski–Harabasz score, and distribution entropy, among others. Results demonstrate that the proposed pipeline effectively recovers semantically coherent and diverse topic structures from multi-source text data, offering actionable insights for policymakers and researchers addressing RCRB.