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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2016  >  16(6): 63-71
LU Xiao-hua, ZUO Hong-fu, BAI Fang. Multi-objective optimization inspection decision-making method based on delay-time model[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 63-71.
Citation: LU Xiao-hua, ZUO Hong-fu, BAI Fang. Multi-objective optimization inspection decision-making method based on delay-time model[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 63-71.
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Multi-objective optimization inspection decision-making method based on delay-time model

  • 1.

    School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

  • 2.

    The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, Jiangsu, China

More Information
  • Author Bio:

    LU Xiao-hua(1978-), male, doctoral student, +86-25-84896262, luxiaohua@nuaa.edu.cn

    ZUO Hong-fu(1959-), male, professor, PhD, +86-25-84891098, aerodiag@nuaa.edu.cn

  • Received Date: 2016-07-13
  • Publish Date: 2016-12-25
    Abstract
  • In accordance with the preventive check plans, the defects detected in the course of inspection, and the repairing and renewing records in a certain cycles for the HPTACC system of a type of aeroengine of an airline freight fleet, the feasibilities for regarding the delayed time for the defects detected and the inspection and repairing costs as safety and economy optimization objectives were analyzed respectively.Under the inspection and repairing strategy of defects detected at the moments of preventive inspection, the probability expressions of expected number of defects and the delayed time for defects detected based on the delay-time model at any inspection momentwere deduced.Under the inspection and repairing strategy of defects degrading into failure and then being found timely and renewed at once, the probability expression of expected number of failure occurring based on delay-time model in every inspection interval was deduced.Based on the probability expressions under 2inspection and repairing strategies, the likelihood function for the system in a given life cycle was build.The double optimization objective functions including inspection and repairing costs and expected delayed time for detected defects were formed.A Pareto optimal solution set of double objective functions were derived by using the improved nondominated sorting genetic algorithm.According to the deciders' objective preference options and the empirical estimates of inspection and repairing costs and the delayed time for detected defects corresponding to their boundary values, the objective preference functions of inspection and repairing costs and the delayed time for detected defects in certain life cycles were determined respectively.The preference interval for every value in the Pareto optimal solution set was determined by using the objective preference function.Based on the collected inspection and repairing data and the proposed methods, an example was analyzed, in which the objective preferences of the delayed time of detected defects and the inspection and repairing costs for deciders were general and good respectively.Analysis result shows that the optimal inspection intervals are 67, 70 or 77landing and take-off cycles, which could provide detailed and more accurate decision-making reference of multi-objective relative optimization for deciders.

     

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