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Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

A machine learning based EMA-DCPM algorithm for production scheduling.

Long Wang^1,2, Haibin Liu³, Minghao Xia^1,2

¹College of Mechanical and Energy Engineering, Beijing University of Technology, Pingleyuanstreet, Beijing, 100124, China.

Scientific Reports

|September 6, 2024

Summary

This summary is machine-generated.

This study introduces the EMA-DCPM model for complex manufacturing environments, improving project time prediction and management. The new methods enhance planning and control for mixed-line production in sophisticated projects.

Keywords:

EMA-DCPM Mixed production lines Multiple varieties Production scheduling Variable batches

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Area of Science:

Manufacturing Engineering
Operations Research
Artificial Intelligence

Background:

Specialized manufacturing sectors like aerospace face challenges with mixed R&D and batch production.
Resource conflicts and unpredictable operating times hinder effective project management.
Controlling large-scale projects with numerous components is complex.

Purpose of the Study:

To develop novel methods for managing complex, mixed-line manufacturing projects.
To improve the prediction of product job times and overall project timelines.
To enhance the robustness and efficiency of project planning and control algorithms.

Main Methods:

Proposed the EMA-DCPM (Enterprise Resource Planning and Mechanical Engineering Society Dynamic Critical Path Method) model.
Integrated machine learning and attention mechanisms for predictive job time analysis.
Enhanced the Critical Path Method (CPM) control algorithm and introduced the "5+X" modeling approach.

Main Results:

The EMA-DCPM model demonstrated predictive advantages through attention mechanism data comparison.
Improved CPM algorithm enhanced robustness in managing complex project schedules.
The "5+X" method proved effective for mixed-line planning in sophisticated manufacturing.

Conclusions:

The proposed EMA-DCPM model and "5+X" method offer effective solutions for mixed-line planning in complex manufacturing.
These advancements address challenges in resource conflict and project control.
The methods hold significant practical value for industries like aerospace.