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<p>The Aircraft Maintenance Routing Problem (AMRP) involves assigning aircraft to the sequence of flights regarding maintenance considerations. This study proposes a bi-objective mixed integer model for cost minimization and fair assignment of aircraft to the flights taking into account their age and efficiency. The proposed model uses flight hours, the number of take-offs, and successive duty days passed from the last maintenance operation as the threshold values to initiate maintenance operations. Despite previous researches, the threshold values in this paper are not necessarily the same for all aircraft and are determined after performing the last maintenance operation as a non-deterministic parameter. To address the uncertainty, a robust optimization approach is used. Moreover, NSGA-II is employed as the solution method. To evaluate the efficiency of the solution technique, 31 test instances of various sizes are solved using the epsilon constraint method and the NSGA-II. The NSGA-II optimality gap does not exceed 0.07% for small and medium-sized problems. Meanwhile, the CPU time of large-scale problems has been improved by over 98%. The model is verified using a real-world case.</p>

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A condition-based maintenance strategy for the aircraft routing problem to cost minimization and fair assignment of aircraft: a robust optimization approach

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Hiwa Esmaeilzadeh,
  • Alireza Rashidi Komijan,
  • Hamed Kazemipoor,
  • Mohammad Fallah,
  • Reza Tavakkoli-Moghaddam

摘要

The Aircraft Maintenance Routing Problem (AMRP) involves assigning aircraft to the sequence of flights regarding maintenance considerations. This study proposes a bi-objective mixed integer model for cost minimization and fair assignment of aircraft to the flights taking into account their age and efficiency. The proposed model uses flight hours, the number of take-offs, and successive duty days passed from the last maintenance operation as the threshold values to initiate maintenance operations. Despite previous researches, the threshold values in this paper are not necessarily the same for all aircraft and are determined after performing the last maintenance operation as a non-deterministic parameter. To address the uncertainty, a robust optimization approach is used. Moreover, NSGA-II is employed as the solution method. To evaluate the efficiency of the solution technique, 31 test instances of various sizes are solved using the epsilon constraint method and the NSGA-II. The NSGA-II optimality gap does not exceed 0.07% for small and medium-sized problems. Meanwhile, the CPU time of large-scale problems has been improved by over 98%. The model is verified using a real-world case.