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Static scheduling method for aircraft flat-tail assembly production based on improved bi-level genetic algorithm.

Tengda Li¹, Min Hua², Junliang Wang³

¹Department of Industrial Energy Products, China Mobile Shanghai Industry Research Institute, Shanghai, 201206, China.

Scientific Reports

|March 21, 2025

Summary

This summary is machine-generated.

This study introduces an improved bi-level genetic algorithm to optimize aircraft flat-tail assembly scheduling. The novel approach effectively addresses frame assignment and task sequencing to minimize delays in complex production environments.

Keywords:

Flat-tail assembly production Flexible flow-shop scheduling problem Improved bi-level genetic algorithm Variable neighborhood search

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Area of Science:

Manufacturing Engineering
Operations Research
Aerospace Engineering

Background:

Aircraft flat-tail assembly involves complex, multi-process scheduling with inherent temporal uncertainties.
Flexible flow-shop scheduling problems (FFSP) are NP-hard, with limited research applied to aircraft assembly.
Human factors introduce variability, complicating production scheduling and increasing potential delays.

Purpose of the Study:

To develop an optimized scheduling solution for aircraft flat-tail assembly.
To address the challenges of frame assignment and assembly task sequencing within FFSP.
To minimize the maximum delay penalty cost in aircraft production.

Main Methods:

An improved bi-level genetic algorithm was developed for FFSP.
A two-stage coding scheme was implemented for frame assignment and task sequencing.
Variable neighborhood search and elite retention were integrated to enhance algorithm performance.

Main Results:

The improved bi-level genetic algorithm demonstrated significant effectiveness in aircraft flat-tail assembly scheduling.
Experimental and case study evaluations confirmed the algorithm's impact on reducing production delays.
The approach successfully tackled the sub-problems of frame assignment and task sequencing.

Conclusions:

The proposed algorithm offers a novel and effective solution for FFSP in aircraft flat-tail assembly.
This research contributes to advancing scheduling methodologies for complex, large-scale equipment production.
The findings have potential applications for optimizing similar manufacturing processes.