Research on Module Division of Commercial Aircraft Based on Analytic Hierarchy Process and Gray Fuzzy Comprehensive Evaluation

The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain scientifically rational modular division solutions for commercial aircraft, this study establishes an Analytic Hierarchy Process–Gray Fuzzy Comprehensive Evaluation (AHP-GFCE) model by integrating hierarchical analysis method and gray fuzzy evaluation theory. This model develops a comprehensive evaluation methodology for aircraft modular division schemes. The proposed method was applied to evaluate the structural modular division scheme of the nose structure section of a certain type of aircraft. Results demonstrate that the AHP-GFCE model successfully identified the optimal nose structure modular division scheme. Compared with traditional installation processes, this optimal solution achieves a 40% improvement in overall assembly efficiency and a 25% reduction in total production cycle duration while better aligning with the engineering and manufacturing requirements of nose structure fabrication, thus revealing the superiority of the AHP-GFCE model in modular division evaluation. This research provides novel insights for modular division schemes of complex system products like commercial aircraft, and the methodology can be extended to modular maintenance domains of sophisticated products such as aero-engines. Although there remains room for model refinement, the findings carry significant theoretical and practical implications for modular division of complex system products.

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Publication Details

Journal
Aerospace
Published
2025-05-28
DOI
https://doi.org/10.3390/aerospace12060485
Citations
1
Primary Topic
Advanced Measurement and Detection Methods
Type
article
Field-Weighted Citation Impact
0.73
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article

Research on Module Division of Commercial Aircraft Based on Analytic Hierarchy Process and Gray Fuzzy Comprehensive Evaluation

Haizhao Xu, Lijun Yang
1 citations
Aerospace
Advanced Measurement and Detection Methods
0.73
article

Research on Module Division of Commercial Aircraft Based on Analytic Hierarchy Process and Gray Fuzzy Comprehensive Evaluation

Haizhao Xu, Lijun Yang
article en
1 citations

Abstract

The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain scientifically rational modular division solutions for commercial aircraft, this study establishes an Analytic Hierarchy Process–Gray Fuzzy Comprehensive Evaluation (AHP-GFCE) model by integrating hierarchical analysis method and gray fuzzy evaluation theory. This model develops a comprehensive evaluation methodology for aircraft modular division schemes. The proposed method was applied to evaluate the structural modular division scheme of the nose structure section of a certain type of aircraft. Results demonstrate that the AHP-GFCE model successfully identified the optimal nose structure modular division scheme. Compared with traditional installation processes, this optimal solution achieves a 40% improvement in overall assembly efficiency and a 25% reduction in total production cycle duration while better aligning with the engineering and manufacturing requirements of nose structure fabrication, thus revealing the superiority of the AHP-GFCE model in modular division evaluation. This research provides novel insights for modular division schemes of complex system products like commercial aircraft, and the methodology can be extended to modular maintenance domains of sophisticated products such as aero-engines. Although there remains room for model refinement, the findings carry significant theoretical and practical implications for modular division of complex system products.

AerospaceVol. 12(6)
Commercial Aircraft Corporation of China (China) (CN), Harbin Institute of Technology (CN)
Openalex Percentile: Top 28%
Advanced Measurement and Detection Methods
0.73
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