Improved Genetic Algorithm for Solving the Order Scheduling Problem of Structural Parts Workshop

In view of the problem that the workshop of intelligent terminal structural parts is faced with the disturbance caused by urgent orders affecting the production efficiency and delivery punctuality of the workshop, combined with the initial scheme minimization index and the order insertion rescheduling delay minimization index, a mathematical model of order insertion scheduling in the production workshop of intelligent terminal structural parts is established; And genetic algorithm is used to solve it, dynamically adjusting the probability of non-uniform crossover and non-uniform mutation to improve the coverage of the search space and avoid getting stuck in local optimal solutions. The results show that compared with the traditional manual decision-making scheduling, the proposed scheduling scheme improves by 7.67%, 3.4%, 10.89% and 15.5% respectively on different orders. The scheduling scheme can effectively cope with the disturbance impact caused by order insertion and shorten the production cycle.

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Publication Details

Journal
Journal of Computer Science and Artificial Intelligence
Published
2025-03-27
DOI
https://doi.org/10.54097/zq54fd50
Primary Topic
Manufacturing Process and Optimization
Type
article
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article

Improved Genetic Algorithm for Solving the Order Scheduling Problem of Structural Parts Workshop

Jiajun Zhang, Yinghua Liao
Journal of Computer Science and Artificial Intelligence
Manufacturing Process and Optimization
article

Improved Genetic Algorithm for Solving the Order Scheduling Problem of Structural Parts Workshop

Jiajun Zhang, Yinghua Liao
article en

Abstract

In view of the problem that the workshop of intelligent terminal structural parts is faced with the disturbance caused by urgent orders affecting the production efficiency and delivery punctuality of the workshop, combined with the initial scheme minimization index and the order insertion rescheduling delay minimization index, a mathematical model of order insertion scheduling in the production workshop of intelligent terminal structural parts is established; And genetic algorithm is used to solve it, dynamically adjusting the probability of non-uniform crossover and non-uniform mutation to improve the coverage of the search space and avoid getting stuck in local optimal solutions. The results show that compared with the traditional manual decision-making scheduling, the proposed scheduling scheme improves by 7.67%, 3.4%, 10.89% and 15.5% respectively on different orders. The scheduling scheme can effectively cope with the disturbance impact caused by order insertion and shorten the production cycle.

Journal of Computer Science and Artificial IntelligenceVol. 2(3)
Openalex Percentile: Top 92%
Manufacturing Process and Optimization
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