The article discusses the necessity of building trust in AI for railway applications, highlighting that AI is currently limited to non‑safety critical uses due to stringent industry standards. It proposes focusing on three key areas—robustness, Operational Design Domain (ODD), and explainability—to meet compliance and safety requirements. By integrating these domains within a safe MLOps environment, the authors argue that regulatory acceptance and public confidence can be achieved, enabling broader AI adoption in mission‑critical railway systems.
By Lefebvre Renard Cl\'ement, L\'eb\'e Vincent, Da Silva Ribeiro Pereira Ricardo, Sundell Johan, Jaoul Arnaud Saiah Kenza, Mijatov\'ic Nenad
arXiv:2607. 17694v1 Announce Type: new Abstract: Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence.
By Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf
The study explores machine learning and deep learning techniques to predict travel time for transportation and logistics within supply chain systems. By leveraging extensive historical data, it aims to build an accurate model that estimates travel times for inventory movement. The research emphasizes the importance of precise travel time predictions for improving logistics consistency, performance, and planning across the supply chain.
By Balaji Venkateswaran
arXiv:2606. 06535v1 Announce Type: cross Abstract: Context.
By Faezeh Amou Najafabad, Markus Haug, Keerthiga Rajenthiram, Justus Bogner, Ilias Gerostathopoulos
arXiv:2607. 19676v1 Announce Type: new Abstract: Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the network edge.
By Wenbin Li, Zhongtian Liao, Bolin Liu, Yongjie Zhou, Jingling Wu, Xiaoyong Lin, Jing Chen
arXiv:2605. 02640v2 Announce Type: replace Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge.
By Ruta Binkyte, Ivaxi Sheth, Zhijing Jin, Mohammad Havaei, Bernhard Sch\"olkopf, Mario Fritz
The article "Supply Chain Analytics: A Data-Driven Approach" presents a mathematically rigorous framework that integrates statistical learning with robust decision-making for logistics and operations management. It covers topics from empirical demand forecasting to optimal inventory and network control under uncertainty, including sample minimization, dynamic programming for inventory replenishment, network fulfillment, and distributionally robust optimization using transport theory. The work also links predictive models with prescriptive algorithms such as column generation for vehicle routing and non‑homogeneous queueing regimes, offering both theoretical foundations and algorithmic guidance for building resilient, automated supply chain systems.
By Elioth Sanabria
The study surveyed maritime professionals on their attitudes toward AI‑supported decision assistants in collision‑avoidance scenarios. Results show a generally positive disposition toward maritime technology, stable trust across scenarios, and nuanced, scenario‑sensitive ratings of explanation quality. Open‑ended feedback highlighted the importance of decision‑support, situational awareness, and confidence‑building, while raising concerns about AI reliability, over‑reliance, and loss of expertise.
By Doreen Jirak, Armeen Saroukanoff, Dirk van Rooy
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.
By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun
The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.
By I. Esra Buyuktahtakin
Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-level hazard-oriented perception, understanding, and r...
arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.
By Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang