Towards a Digital Twin Modeling Method for Container Terminal Port

• URL: http://arxiv.org/abs/2503.13511v1
• Date: Fri, 14 Mar 2025 08:36:03 GMT
• Title: Towards a Digital Twin Modeling Method for Container Terminal Port
• Authors: Faouzi Hakimi, Tarek Khaled, Mohammed Al-Kharaz, Arthur Cartel Foahom Gouabou, Kenza Amzil,
• Abstract summary: This paper advocates for the implementation of a digital twin-based methodology to streamline the operations of stacking cranes.<n>The proposed approach entails the creation of a virtual container yard that mirrors the physical yard within a digital twin system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel strategy aimed at enhancing productivity and minimizing non-productive movements within container terminals, specifically focusing on container yards. It advocates for the implementation of a digital twin-based methodology to streamline the operations of stacking cranes (SCs) responsible for container handling. The proposed approach entails the creation of a virtual container yard that mirrors the physical yard within a digital twin system, facilitating real-time observation and validation. In addition, this article demonstrates the effectiveness of using a digital twin to reduce unproductive movements and improve productivity through simulation. It defines various operational strategies and takes into account different yard contexts, providing a comprehensive understanding of optimisation possibilities. By exploiting the capabilities of the digital twin, managers and operators are provided with crucial information on operational dynamics, enabling them to identify areas for improvement. This visualisation helps decision-makers to make informed choices about their stacking strategies, thereby improving the efficiency of overall container terminal operations. Overall, this paper present a digital twin solution in container terminal operations, offering a powerful tool for optimising productivity and minimising inefficiencies.

Related papers

• Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production [2.087827281461409]
  We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant.<n>Our system is based on a micro-service edge-compute platform that ingests real-time sensor data from the process into a digital twin over a converged network infrastructure.<n>Key to our approach is a Deep Reinforcement learning-based agent used in our machine learning operation (MLOps) driven system to autonomously correlate the system state with its digital twin to identify correction actions that aim to optimize power settings for the plant.
  arXiv  Detail & Related papers  (2025-11-19T05:29:43Z)
• Forecasting Empty Container availability for Vehicle Booking System Application [0.0]
  This study focuses on developing and evaluating a data-driven approach for forecasting empty container availability at container terminal depots within a Vehicle Booking System (VBS) framework.<n>It addresses the gap in research concerning optimizing empty container dwell time and aims to enhance operational efficiencies in container terminal operations.
  arXiv  Detail & Related papers  (2025-03-14T08:29:04Z)
• Digital Twin-Enabled Real-Time Control in Robotic Additive Manufacturing via Soft Actor-Critic Reinforcement Learning [2.5709786140685633]
  This research presents a novel approach integrating Soft Actor-Critic (SAC) reinforcement learning with digital twin technology.<n>We demonstrate our methodology using a Viper X300s robot arm, implementing two distinct control scenarios.<n>Results show rapid policy convergence and robust task execution in both simulated and physical environments.
  arXiv  Detail & Related papers  (2025-01-29T22:06:53Z)
• Digital Transformation in the Water Distribution System based on the Digital Twins Concept [0.0]
  This paper describes the development of a state-of-the-art DT platform for water distribution systems.<n>It introduces advanced technologies such as the Internet of Things, Artificial Intelligence, and Machine Learning models.<n>In this view, the system will contribute to improvements in decision-making capabilities, operational efficiency, and system reliability.
  arXiv  Detail & Related papers  (2024-12-09T17:40:37Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System [6.87702244676681]
  We develop digital twins for real-time awareness, predictive analytics, and urban logistics optimization. Recent advancements in generative AI offer new opportunities to streamline digital twins. We propose a conceptual framework employing transformer-based language models to enhance an urban digital twin.
  arXiv  Detail & Related papers  (2024-10-08T05:53:20Z)
• Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins [53.70191138561039]
  We propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. We adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines.
  arXiv  Detail & Related papers  (2024-05-20T09:28:23Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing [46.65030115953947]
  We propose a framework consisting of a digital twin and reinforcement learning agents. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. We also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data.
  arXiv  Detail & Related papers  (2023-11-28T15:25:14Z)
• Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry [0.0]
  This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil and gas industry. The framework combines Bayesian inference, Monte Carlo simulations, transfer learning, online learning, and novel strategies to confer cognition to the DT.
  arXiv  Detail & Related papers  (2023-11-21T18:02:52Z)
• Digital Twins for Ports: Derived from Smart City and Supply Chain Twinning Experience [1.348973072687142]
  The digital twin is an emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes. The implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin.
  arXiv  Detail & Related papers  (2023-01-10T15:22:17Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• AdaViT: Adaptive Vision Transformers for Efficient Image Recognition [78.07924262215181]
  We introduce AdaViT, an adaptive framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use. Our method obtains more than 2x improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy.
  arXiv  Detail & Related papers  (2021-11-30T18:57:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.