# Inverse scheduling method for aircraft flat-tail assembly production based on improved genetic algorithm

## TL;DR

This paper introduces an inverse scheduling method using an improved genetic algorithm to optimize aircraft flat-tail assembly production and handle disruptions efficiently.

## Contribution

The novel inverse scheduling strategy is first applied to aircraft flat-tail assembly, combining genetic algorithms with local search and self-adaptive tolerance mechanisms.

## Key findings

- The inverse scheduling method significantly reduces sequence adjustment and material handling costs.
- The improved genetic algorithm outperforms traditional rescheduling strategies in complex assembly systems.
- Case studies confirm the practical efficiency of the proposed method in managing disruptions.

## Abstract

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.