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Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing.

Jianjia He^1,2, Jian Wu¹, Ye Zhang³

¹Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.

Computational Intelligence and Neuroscience

|July 28, 2022

Summary

This study introduces an intelligent multiagent method for optimizing large-scale customized medical 3D printing (M-3DP) production scheduling. The improved genetic algorithm enhances efficiency and load balancing, advancing intelligent manufacturing in M-3DP.

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Area of Science:

Medical Engineering
Manufacturing Technology
Artificial Intelligence

Background:

Three-dimensional (3D) printing, or additive manufacturing, offers advantages over traditional methods, gaining traction in medicine.
The demand for large-scale, customized medical 3D printing (M-3DP) has surged, especially during public health events.
Traditional scheduling methods struggle with M-3DP's complexity, particularly multimaterial printing and job-to-device matching.

Purpose of the Study:

To address the challenges in large-scale customized M-3DP production scheduling.
To propose an intelligent collaborative scheduling method using a multiagent system.
To optimize M-3DP production for reduced completion time and balanced device load.

Main Methods:

Development of a multiagent-based optimization model for M-3DP scheduling.
Implementation of an improved genetic algorithm incorporating product mix strategy and intelligent matching.
Evaluation of the method's effectiveness through numerical simulation.

Main Results:

The improved genetic algorithm significantly reduced M-3DP mass customization production scheduling time.
The proposed method achieved better load balancing between devices compared to other algorithms.
The approach demonstrated enhanced intelligent collaboration for M-3DP mass customization.

Conclusions:

The intelligent multiagent-based method effectively optimizes M-3DP production scheduling.
The improved genetic algorithm offers superior performance in reducing lead times and balancing workloads.
This advancement promotes the intelligent manufacturing of customized medical 3D printed products.