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

Tengda Li1,2, Min Hua3, Junliang Wang4

  • 1Shanghai Jiao Tong University, USC-SJTU Institute of Cultural and Creative Industry, Shanghai, 200240, China.

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|November 17, 2025
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Summary
This summary is machine-generated.

This study introduces inverse scheduling for aircraft flat-tail assembly, optimizing production by minimizing delay and adjustment costs. The novel approach enhances efficiency and handles disruptions effectively in complex manufacturing.

Keywords:
Flat-tail assembly productionImproved genetic algorithmInverse schedulingSelf-adaptive driving mechanism

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Area of Science:

  • Aerospace Engineering
  • Manufacturing Systems
  • Operations Research

Background:

  • Aircraft flat-tail assembly manufacturing is complex, manual, and prone to scheduling disruptions.
  • Existing scheduling methods struggle with uncertainties, leading to suboptimal or infeasible production plans.
  • Disruptions in complex assembly systems necessitate advanced scheduling strategies.

Purpose of the Study:

  • To develop and apply an inverse scheduling strategy for aircraft flat-tail assembly production.
  • To present a multi-objective optimization model for inverse scheduling, focusing on delay and adjustment costs.
  • To introduce an improved genetic algorithm for solving large-scale inverse scheduling problems.

Main Methods:

  • Formulated a multi-objective optimization model for inverse scheduling.
  • Developed an improved genetic algorithm (GA) integrating local search for efficiency.
  • Designed a self-adaptive tolerance-driving mechanism for handling exceptions and enhancing GA performance.
  • Validated the inverse scheduling strategy and mechanism through enterprise case studies.

Main Results:

  • The proposed inverse scheduling method significantly outperforms traditional rescheduling strategies.
  • Achieved reductions in sequence adjustment and material handling costs.
  • Demonstrated the effectiveness of the self-adaptive tolerance-driving mechanism in managing order delays.
  • Verified the practical applicability and efficiency of the inverse scheduling approach.

Conclusions:

  • Inverse scheduling offers a more practical and efficient solution for disruptions in complex aircraft assembly.
  • The improved GA with a self-adaptive mechanism effectively addresses large-scale inverse scheduling problems.
  • This research provides a novel and effective approach to enhance production scheduling in aerospace manufacturing.