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Inverse scheduling method for aircraft flat-tail assembly production based on improved genetic algorithm
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Inverse scheduling method for aircraft flat-tail assembly production based on improved genetic algorithm
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Journal Article
Inverse scheduling method for aircraft flat-tail assembly production based on improved genetic algorithm
2025
Overview
The manufacturing process of the aircraft flat-tail assembly is complex and discrete. It typically involves manual assembly at fixed stations with variable shift teams. However, uncertainties can arise even after a scheduling scheme is created, leading to non-optimal or even infeasible schedules. To address this issue, a new scheduling strategy called ‘inverse scheduling’ has been proposed by incorporating the concept of inverse optimization. Notably, this is the first application of inverse scheduling in the complex manufacturing process of aircraft flat-tail assembly. This paper presents a multi-objective optimization model for the inverse scheduling problem of flat-tail assembly production. The scheduling objectives include minimizing the maximum delay penalty cost and minimizing the assembly time adjustment cost. To address the limitations of traditional mathematical planning methods in terms of efficiency and solution quality, an improved genetic algorithm is proposed. This algorithm combines the genetic algorithm with a local search strategy to solve the large-scale inverse scheduling problem. Additionally, an inverse scheduling strategy based on the self-adaptive tolerance-driving mechanism is designed to enhance the algorithm’s efficiency and effectively handle order delay exception events. The effectiveness of the self-adaptive tolerance driving mechanism and the inverse scheduling method is verified through case studies in enterprises. Comparative analysis demonstrates that the proposed method significantly outperforms traditional rescheduling strategies by avoiding high sequence adjustment and material handling costs, offering a more practical and efficient solution for managing disruptions in complex assembly systems.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
