Packaging Simulator Guidance for Stable Cargo Loading Sequences
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Solution Overview
Problem
Conventional logistics loading methods rely heavily on worker experience, leading to inefficiencies and safety issues, particularly when loading variable-sized and shaped cargo, and do not ensure optimal cargo loading efficiency.
Innovation Solution
A logistics loading work control system and method that uses a server to mirror facility data, a packaging simulator to calculate optimal cargo deployment sequences, and a worker terminal to provide graphical instructions for efficient cargo loading, leveraging digital twin technology and wireless communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional cargo loading methods depending on worker experience are used, then workers can perform loading operations, but cargo loading efficiency varies and cannot be optimized for different cargo vehicles, aircraft, and vessels
Solution Approach 1:
The system creates a digital twin (virtual copy) of the actual cargo container and uses a packaging simulator to replicate the loading process in a virtual environment. This allows the algorithm to calculate optimal loading sequences without affecting real operations, and the virtual model can be adapted to different cargo vehicles, aircraft, and vessels by updating the digital twin representation.
Solution Approach 2:
The loading algorithm dynamically adjusts loading parameters based on the specific characteristics of different cargo vehicles, aircraft, and vessels. The system calculates optimal loading sequences by changing parameters such as cargo placement positions, loading order, and space utilization based on the target vehicle's dimensions, weight limits, and structural features.
2Productivity
If conventional cargo loading methods are used, then loading operations can be performed, but loading stability deteriorates causing cargo damage or relocation
Solution Approach 1:
The packaging simulator performs preliminary calculations in a virtual environment to determine the optimal loading sequence before actual loading begins. The system pre-calculates the best placement positions and loading order to ensure stability, preventing cargo damage or relocation during the actual loading process by following the pre-determined optimal sequence.
Solution Approach 2:
The system provides real-time feedback to workers through the worker terminal, displaying the calculated optimal loading sequence and current loading status. This feedback mechanism ensures workers follow the stability-optimized sequence, and the system can adjust recommendations based on the actual loading progress and any deviations from the plan.
3Ease of operation
If conventional cargo loading methods are used, then workers can load cargo, but loading work man-hours, time, and cost increase due to inefficiency
Solution Approach 1:
The system introduces an intermediary algorithm and digital twin technology between the loading plan and actual execution. The loading algorithm acts as a mediator that translates cargo and vehicle data into optimized loading sequences, while the digital twin serves as an intermediary simulation environment. This intermediary layer eliminates the need for workers to manually plan loading sequences, reducing loading time while maintaining operational simplicity through automated guidance.
Data Source
AI summary
A logistics loading work control system includes a server configured to mirror facility data of an actual logistics facility and a virtual environment, for example, based on a digital twin. The system also includes a packaging simulator configured to calculate cargo information corresponding to a loading area received from the server through a loading algorithm and predict priority cargo information in a sequence of highest loading efficiency with respect to a current work-target cargo container, and a worker terminal configured to assist a logistics loading work of a worker by deriving a scenario branch-type loading sequence and an expected loading rate based on a logistics loading situation inside the loading area through the packaging simulator and providing a recommended cargo logic according to the derived result.