Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty
Source abstract
Abstract
Optimizing the aircraft equipment usage scheme of different units according to their task intensity has great significance in improving aircraft reliability and health management. This paper studied the modeling and solving methods of the rotation and echelon usage problems of aircraft equipment measured by dual-life indexes, one of which cannot be controlled. In order to maximize the waste rate of the rotation quantity, echelon uniformity index, life matching index and life utilization index, a decision-making model of the equipment rotation and echelon usage problem under uncertainty was constructed, and an improved NSGA-III with a variable length chromosome was proposed. An improved segmented coding method and operators were proposed, and the repeated individual control mechanism was used to improve the population diversity. When the scale of the problem was large, this method could search a wider range in a short time and obtain more feasible solutions, which verified the feasibility of this method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Advanced Measurement and Detection Methods · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Equipment Management and UAV Engineering, Air Force Engineering University, Xi’an 710051, China
References (37)
Show source-provided references
1. Research on the aircraft usage decision based on BP neural network
2. Calendar life of aircraft based on annual flight frequency
3. Evaluation indexes for echelon usage control of military aircraft
4. Research in echelon usage of aircraft considering the calendar life
5. Formation and control method of motor hour echelon storage for a kind of armored equipment
6. A new genetic algorithm with agent-based crossover for the generalized assignment problem10.5755/j01.itc.48.3.21893 ↗
7. A multiple objective uncertainty assignment problem and the study of its algorithm
8. Generalized assignment model of talk allocation problem and its method
9. Uncertain random simulation algorithm with application to bottleneck assignment problem10.1007/s00500-018-3652-5 ↗
10. Uncertain random assignment problem10.1016/j.apm.2017.11.026 ↗
11. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints10.1109/TEVC.2013.2281535 ↗
12. An improved NSGA-III algorithm using genetic K-means clustering algorithm10.1109/ACCESS.2019.2960531 ↗
13. An improved NSGA-III algorithm for reservoir flood control operation10.1007/s11269-017-1759-6 ↗
14. Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm10.23919/CSMS.2022.0006 ↗
15. Many-objective optimization for scheduling of crude oil operations based on NSGA-III with consideration of energy efficiency10.1016/j.swevo.2020.100714 ↗
16. The optimization of reactive power for distribution network with PV generation based on NSGA-III10.24295/CPSSTPEA.2021.00017 ↗
17. Wang, S., Tao, F., and Shi, Y. (2018). Optimization of location–routing problem for cold chain logistics considering carbon footprint. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15010086 ↗
18. Dual-Objective Collision-Free Path Optimization of Arc Welding Robot10.1109/LRA.2021.3092267 ↗
19. Integrated production and multiple trips vehicle routing with time windows and uncertain travel times10.1016/j.cor.2018.10.011 ↗
20. Shao, C., Wang, H., and Yu, M. (2022). Multi-Objective Optimization of Customer-Centered Intermodal Freight Routing Problem Based on the Combination of DRSA and NSGA-III. Sustainability, 14.10.3390/su14052985 ↗
21. Tang, Q. (2017). An Improved NSGA-III Algorithm to the Many-Objective Optimal Power Flow of Power System. [Ph.D. Thesis, Xiamen University].10.1109/CAC.2018.8623181 ↗
22. Wang, S. (2019). I-NSGA-III Based Many-Objective Optimal Power Flow for Power System and Its Decision Support. [Ph.D. Thesis, Xiamen University].
23. Stratified opposition-based initialization for variable-length chromosome shortest path problem evolutionary algorithms10.1016/j.eswa.2020.114525 ↗
24. Yu, B.J. (2022). A Genetic Algorithm Framework using Variable Length Chromosomes for Vehicle Maneuver Planning. [Ph.D. Thesis, Massachusetts Institute of Technology].
25. Asim, M., and Abd El-Latif, A.A. (2021). Intelligent computational method for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systems. ISA Trans., in press.
26. Rodríguez-Molina, A., Solís-Romero, J., Villarreal-Cervantes, M.G., Serrano-Pérez, O., and Flores-Caballero, G. (2021). Path-planning for mobile robots using a novel variable-length differential evolution variant. Mathematics, 9.10.3390/math9040357 ↗
27. Ni, J., Wang, K., Cao, Q., and Khan, Z. (2017). A memetic algorithm with variable length chromosome for robot path planning under dynamic environments. Int. J. Robot. Autom., 32.10.2316/Journal.206.2017.4.206-4998 ↗
28. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm10.1016/j.eswa.2018.08.008 ↗
29. A variable-length chromosome genetic algorithm to solve a road traffic coordination multipath problem10.1109/ACCESS.2019.2935041 ↗
30. Comparative study of NSGA-II and NSGA-III on multi-objective optimization of heat exchanger network
31. Improved NSGA-Algorithm and BP Fuel-cost Prediction Network for Many-objective Optimal Power Flow Problems
32. Multi-objective optimization design of low specific speed centrifugal pumps based on NSGA-III algorithm
33. Improved NSGA-III with selection-and-elimination operator10.1016/j.swevo.2019.05.011 ↗
34. Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties10.1016/j.swevo.2019.02.003 ↗
35. An improved NSGA-III algorithm for feature selection used in intrusion detection10.1016/j.knosys.2016.10.030 ↗
36. Vesikar, Y., Deb, K., and Blank, J. (2018, January 18–21). Reference point based NSGA-III for preferred solutions. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.10.1109/SSCI.2018.8628819 ↗
37. Comparison of NSGA-III with NSGA-II for multi objective optimization of adiabatic styrene reactor10.1016/j.matpr.2021.12.047 ↗