[HTML][HTML] Towards autonomous supply chains: Definition, characteristics, conceptual framework, and autonomy levels
Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical
conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring …
conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring …
Integrating Machine Learning Into Vehicle Routing Problem: Methods and Applications
The vehicle routing problem (VRP) and its variants have been intensively studied by the
operational research community. The existing surveys and the majority of the published …
operational research community. The existing surveys and the majority of the published …
Quantum Artificial Intelligence: A Brief Survey
Abstract Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and
AI, a technological synergy with expected significant benefits for both. In this paper, we …
AI, a technological synergy with expected significant benefits for both. In this paper, we …
Multi-step look ahead deep reinforcement learning approach for automatic train regulation of urban rail transit lines with energy-saving
Y Zhang, S Li, Y Yuan, L Yang - Engineering Applications of Artificial …, 2025 - Elsevier
Urban rail transit train operations are frequently disturbed by external factors, making
schedule adherence challenging. These disturbances can cause cascading train delays …
schedule adherence challenging. These disturbances can cause cascading train delays …
Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking
V Jannelli, S Schoepf, M Bickel, T Netland… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper explores how Large Language Models (LLMs) can automate consensus-seeking
in supply chain management (SCM), where frequent decisions on problems such as …
in supply chain management (SCM), where frequent decisions on problems such as …
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
Deep reinforcement learning (RL) has been shown to be effective in producing approximate
solutions to some vehicle routing problems (VRPs), especially when using policies …
solutions to some vehicle routing problems (VRPs), especially when using policies …
A Coalition Game for On-demand Multi-modal 3D Automated Delivery System
F Moosavi, B Farooq - arxiv preprint arxiv:2412.17252, 2024 - arxiv.org
We introduce a multi-modal autonomous delivery optimization framework as a coalition
game for a fleet of UAVs and ADRs operating in two overlaying networks to address last …
game for a fleet of UAVs and ADRs operating in two overlaying networks to address last …
A Multi-Objective Vehicle-Cargo Matching Decision Method Considering Market Supply–Demand Fluctuations and Diverse Stakeholder Interests
Z Li, Y Shao, G Liu - Arabian Journal for Science and Engineering, 2025 - Springer
The vehicle-cargo matching (VCM) sector within the freight industry faces significant
challenges, including substantial fluctuations in vehicle-cargo supply and demand quantities …
challenges, including substantial fluctuations in vehicle-cargo supply and demand quantities …
Traffic forecasting using LSTM and SARIMA models: A comparative analysis
Transportation management, urban planning, and intelligent transportation systems require
traffic forecasts. This study compares LSTM and SARIMA traffic prediction models. LSTM, a …
traffic forecasts. This study compares LSTM and SARIMA traffic prediction models. LSTM, a …
Multi-Agent Reinforcement Learning for WEEE Recycling Vehicle Path Planning Based on Graph Attention Networks
Z Qv, Q Liang, M Li, Y Zhang, F Meng… - … Conference on New …, 2024 - ieeexplore.ieee.org
With the global surge in WEEE generation, optimizing vehicle routing for recycling has
become a critical area of research. This paper introduces a MARL method built upon graph …
become a critical area of research. This paper introduces a MARL method built upon graph …