Automated guided vehicles transporting containers in a futuristic port.

Smarter Shipping: How Dynamic Time Estimation is Revolutionizing Container Terminals

Lena Kashyap in Tech & Innovation September 2025 • 4 min read.

"Discover how dynamic time estimation enhances AGV dispatching for quicker, more efficient automated container terminals."

Imagine a world where the movement of goods is seamless, efficient, and almost entirely automated. This is the vision of modern international logistics, and at the heart of this transformation lies the Automated Container Terminal (ACT). These terminals, equipped with cutting-edge technology, are designed to handle the ever-increasing flow of cargo with unprecedented speed and precision.

Within these bustling hubs, Automated Guided Vehicles (AGVs) play a pivotal role. These driverless vehicles navigate the complex network of the terminal, transporting containers from ship to storage and back again. The challenge? Optimizing their routes and schedules to minimize delays and maximize efficiency. The solution lies in advanced dispatching algorithms, and one of the most promising is dynamic time estimation.

Traditionally, AGV dispatching has relied on static distance calculations. However, this approach often overlooks the unpredictable nature of real-world conditions, such as congestion. A new study introduces a dynamic time estimation-based AGV dispatching algorithm that promises to revolutionize container terminal operations. Let's dive into how this technology works and its potential impact on the future of shipping.

AGV Dispatching: From Static to Dynamic

Automated guided vehicles transporting containers in a futuristic port.

Traditional AGV dispatching algorithms typically rely on static data, such as the physical distance between two points. While simple to implement, this approach fails to account for the dynamic nature of a container terminal. Congestion, unexpected delays, and varying traffic conditions can significantly impact travel times, leading to inefficiencies and bottlenecks.

The new approach uses a dynamic time estimation-based system is a game-changer. Instead of relying on fixed distances, this algorithm continuously estimates the time it will take for an AGV to travel a specific route, taking into account real-time conditions. By dynamically adjusting dispatching decisions based on these estimations, the system can:

Reduce start-stop frequency
Minimize road network congestion
Enhance the utilization rate of quay cranes (QCs) and yard cranes (YCs)
Shorten overall task completion time

The algorithm uses real-time data and predictive modeling to estimate travel times, improving operational efficiency. This system creates a more responsive and adaptive transport network within the container terminal. By considering current conditions, such as traffic and potential obstructions, the dynamic algorithm optimizes routes for AGVs. It significantly outperforms static models, decreasing delays and improving resource use, particularly for quay and yard cranes.

The Future of Automated Container Terminals

The implementation of dynamic time estimation in AGV dispatching represents a significant step forward in the evolution of automated container terminals. By moving beyond static calculations and embracing real-time data and predictive modeling, these algorithms are paving the way for more efficient, resilient, and intelligent logistics operations. As technology continues to advance, we can expect even more sophisticated solutions that further optimize the flow of goods around the world.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.23919/chicc.2018.8483770, Alternate LINK

Title: Dynamic Time Estimation Based Agv Dispatching Algorithm In Automated Container Terminal

Journal: 2018 37th Chinese Control Conference (CCC)

Publisher: IEEE

Authors: Zhenming Yang, Chenghao Li, Qianchuan Zhao

Published: 2018-07-01

Everything You Need To Know

What role do Automated Guided Vehicles (AGVs) and dynamic time estimation play in optimizing Automated Container Terminal (ACT) operations?

Automated Guided Vehicles (AGVs) are driverless vehicles that transport containers within an Automated Container Terminal (ACT). The challenge is optimizing their routes to minimize delays. Dynamic time estimation is an advanced dispatching algorithm that continuously estimates AGV travel time, considering real-time conditions like congestion. This contrasts with traditional methods using static distance calculations.

How does dynamic time estimation differ from traditional AGV dispatching methods, and what are the shortcomings of using static data?

Traditional AGV dispatching algorithms rely on static data, like the physical distance between points. This doesn't account for dynamic conditions like congestion and unexpected delays. Dynamic time estimation algorithms use real-time data and predictive modeling to estimate travel times, adjusting dispatching decisions based on current conditions.

What key benefits can a container terminal expect to see by implementing a dynamic time estimation-based system for AGV dispatching?

A dynamic time estimation-based system can significantly improve container terminal operations by reducing start-stop frequency, minimizing road network congestion, enhancing the utilization rate of quay cranes (QCs) and yard cranes (YCs), and shortening overall task completion time. This leads to a more responsive and adaptive transport network.

In what ways does dynamic time estimation specifically improve the dispatching process for Automated Guided Vehicles (AGVs) at container terminals, and why is this important?

Dynamic time estimation enhances AGV dispatching by using real-time data and predictive modeling to estimate travel times. This helps optimize routes based on current conditions such as traffic and potential obstructions. This approach is especially beneficial as it decreases delays and improves resource use, particularly for quay and yard cranes, by providing a more accurate and responsive transport network.

How is dynamic time estimation shaping the future of Automated Container Terminals (ACTs) and what broader implications does it have for logistics operations?

The shift towards dynamic time estimation represents a significant advancement for Automated Container Terminals (ACTs). By incorporating real-time data and predictive modeling, these terminals can achieve greater efficiency, resilience, and intelligence in their logistics operations. Future advancements are expected to bring even more sophisticated solutions to further optimize the movement of goods.