Issue |
Manufacturing Rev.
Volume 10, 2023
|
|
---|---|---|
Article Number | 11 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/mfreview/2023010 | |
Published online | 27 June 2023 |
Research article
A new algorithm of the scheduling of a flexible manufacturing system based on genetic algorithm
1
Chengdu SIWI High-Tech Industrial Co, Ltd, Chengdu, PR China
2
School of Mechanical Engineering, Sichuan University 610065, Chengdu, PR China
*e-mail: liuxiaoyu@scu.edu.cn
Received:
21
March
2023
Accepted:
23
May
2023
In the flexible manufacturing system, a reasonable production scheduling is crucial in shortening the processing completion time and improving the equipment utilization. Traditional manual scheduling cannot effectively solve the complex workshop scheduling problems and cannot provide a scheduling solution that meets the requirements in a short period of time, which can lead to a decrease in processing efficiency. Aiming at the complex job shop scheduling problem, the genetic algorithm is used to find the optimal scheduling solution in this study, taking the number of overdue jobs, the total overdue time, the job completion time, the comprehensive load rate and the maximum load rate of the machine tool as the performance indicators of the scheduling algorithm. The chromosomes are designed as process gene chain and equipment gene chain to improve the diversity and the robustness to scheduling problems of chromosome through crossover, variation, selection and other processes. The impact of different parameter settings on the performance indicators of each scheduling algorithm is researched by adjusting the four algorithm-related parameters, and there has been a certain improvement in the results of the scheduling problems. This study provides a reference for the design and optimization of production scheduling algorithm based on genetic algorithm.
Key words: Flexible manufacturing system / production scheduling / genetic algorithm / chromosome pairs coding
© B. Bao et al., Published by EDP Sciences 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.