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

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.