Enhancing Vessel Planning with Smart Stow

A leading Emirati multinational logistics company revolutionized its outbound vessel planning process with the Smart Stow Application. 

Leveraging AI-driven Deep Q Reinforcement Learning, the application transformed their manual container planning system, drastically reduced the planning time, and enhanced overall productivity.

This Dubai-based multinational logistics company is a key player in global container traffic. 

Offering services such as cargo logistics, port terminal operations, maritime services, and free trade zones, the company operates across 140 countries and handles 92 million containers annually. Its extensive operations make it a critical contributor to the global supply chain.

The client faced inefficiencies in planning outbound vessel operations. With multiple large vessels arriving simultaneously, the manual planning process consumed significant time and resources. 

Major challenges included: 

1. Balancing Stack Weight:

2. Minimizing Yard Shifting:

3. Maintaining Yard Load Balance:

These complexities often resulted in delays and suboptimal resource allocation.

Our smart stow application used advanced data analytics and AI learning techniques to optimize container planning, significantly reducing manual effort and enhancing operational efficiency for the client.

1. Problem Analysis and Requirement Gathering:

This included identifying pain points such as time delays, inefficiencies in crane allocation, and challenges in balancing yard loads. Detailed requirements were gathered to ensure the solution addressed these specific challenges effectively.

2. Design and Simulation:

A Deep Q Reinforcement Learning Model was designed to optimize container matching and yard balancing. Simulations were run to construct a Deep Q Table, allowing the system to learn and predict optimal decision pathways based on real-world scenarios. This phase ensured the application could handle diverse operational complexities.

3. Development and Integration:

The Smart Stow Application was developed with a focus on seamless compatibility with the Zodiac system, the client’s existing operational framework. This integration enabled real-time communication and automated decision-making without disrupting existing workflows.

4. Testing and Optimization:

Extensive testing was conducted to validate the application’s efficiency in real-world settings. Parameters like time savings, accuracy in crane allocation, and yard load balance were rigorously monitored. Iterative refinements were made to enhance system performance.

5. Deployment and Training:

The application was deployed in a phased manner to minimize operational disruption. Training sessions were conducted for the client’s operations team, ensuring smooth adoption and effective utilization of the application.