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Digital Twin Simulation for Container Operations

We developed a Digital Twin simulation system to replicate and analyze container operations in a real-world shipping environment.
By modeling vessel, crane, and container workflows through discrete-event simulation, it helps logistics teams visualize performance, identify bottlenecks, and optimize operations before implementation.

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Summary :

We contributed to the development of a Digital Twin simulation designed to model and optimize container operations in a shipping environment.
Using discrete-event simulation with SimPy, the system replicates real-world logistics conditions, helping stakeholders analyze performance, identify bottlenecks, and make data-driven operational decisions.

Problem

Problem

Objectives

  • Realistic Simulation and Analysis

    Build a Digital Twin of container operations for realistic simulation and analysis.

  • Data-Driven Workflow Optimization

    Enable data-driven optimization of port and shipping workflows.

  • Improved Decision-Making with Visualization

    Improve decision-making through visualization of container flow and delays.

  • Safe Testing of New Operational Strategies

    Test new operational strategies without interrupting live operations.

Challenges

  • Modeling highly dynamic container logistics with variable dependencies.
  • Ensuring real-time synchronization between simulation modules.
  • Maintaining accuracy while optimizing simulation speed.
  • Debugging and integrating multiple modules within an evolving system.

Solution

We developed and integrated multiple simulation modules using SimPy, a discrete-event simulation library in Python.
The Digital Twin accurately mirrored container movement, from loading and unloading to transport, queuing, and dispatch, capturing interdependencies and time-based events.
We enhanced system precision by resolving existing bugs, introducing new performance features, and aligning all modules within a unified simulation framework.
The result was a realistic, data-rich simulation capable of analyzing operational outcomes under different what-if scenarios.

Technologies

Architecture

Input Parameters
Operational variables such as vessel arrival times, port capacity, and loading rates are configured.
Simulation Modules
Individual modules simulate containers, cranes, queues, and transport processes.
Event Handling
SimPy manages events like loading, waiting, and dispatching with time-based precision.
Container Flow Simulation
Dynamic container movements are visualized to identify bottlenecks and inefficiencies.
Performance Visualization
Simulation results are displayed as metrics and graphs to support optimization decisions.

Results & Impact

Realistic Process Simulation Created a lifelike representation of shipping and container operations for strategic testing.

Enhanced Decision-Making Provided actionable data for route planning, scheduling, and resource allocation.

Operational Efficiency Identified process bottlenecks and reduced trial-and-error experimentation in live operations.

Modular and Scalable Design Easily adaptable to simulate new logistics processes or integrate with IoT systems.

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