The aircraft workpiece paint shop scheduling problem: A case study

Bolin Chen, Jie Zhang, Tengfei Long, Yukan Hou, Shoushan Jiang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The aircraft workpiece paint shop (AWPS) plays a significant role in the aircraft manufacturing system because of its anti-rust and anti-corrosion effect on workpieces. Various workpieces in the AWPS with varied sizes and required delivery dates need to be loaded on halftones with limited area first and then sent to robots for painting. Production scheduling is facing huge challenges because it requires minimizing the number of halftones to load workpieces while reducing the cost caused by the completion of workpieces before or after their delivery dates. Therefore, this paper investigates an AWPS scheduling problem to minimize the total weighted penalty (TWP) and makespan, considering the geometric factor and delivery date of each workpiece. An improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed for solving this problem. Firstly, a three-layer chromosome model is designed as the basis for the hybrid optimization of workpiece layout and operation sequence. Afterwards, considering the geometric factor of workpieces and the variable number of halftones, a series of optimization operators are designed to improve the convergence ability and ensure the feasibility of the scheduling solutions. Moreover, through the application of different customer orders from a real AWPS and the comparison test with different algorithms, it can be effectively verified that the proposed algorithm can not only be stably applied to industrial cases with different scales but also improve the productivity and reduce the production cost of the AWPS.
Original languageEnglish
Pages (from-to)426-442
Number of pages17
JournalJournal of Manufacturing Systems
Volume68
Early online date12 May 2023
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Paint shop scheduling
  • Workpiece layout
  • Aircraft workpiece painting
  • Multi-objective optimization
  • NSGA-II

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