Analysing Uncertainty and Delays in Aircraft Heavy Maintenance

  • Leandro Salazar Rosales

Student thesis: Phd


Analysing Uncertainty and Delays in Aircraft Heavy MaintenanceThe University of ManchesterLeandro Julian Salazar RosalesDoctor of PhilosophyDecember, 2015This study investigates the influence of unscheduled maintenance activities on delays and disruptions during the execution of aircraft heavy maintenance services by developing a simulation model based on Systems Dynamics (SD) and supported by an Evidential Reasoning (ER) rule model.The SD model studies the complex interrelationship between scheduled and unscheduled tasks and its impact on delays during a maintenance service execution. It was found that the uncertain nature of the unscheduled maintenance tasks hinders the planning, control and allocation of resources, increasing the chances to miss deadlines and incur in cost overruns. Utilising causal loop diagrams and SD simulation the research explored the relevance that the resource allocation management, the precise estimation of the unscheduled tasks and their prompt identification have on the maintenance check duration. The influence that delays and attitudes in the decision-making process have on project performance was also investigated.The ER rule model investigates the uncertainty present during the execution of a maintenance check by providing a belief distribution of the expected unscheduled maintenance tasks. Through a non-parametric discretisation process, it was found that the size and array of distribution intervals play a key role in the model estimation accuracy. Additionally, a sensitivity analysis allowed the examination of the significance that the weight, reliability and dependence of the different pieces of evidence have on model performance. By analysing and combining historical data, the ER rule model provides a more realistic and accurate prediction to analyse variability and ambiguity.This research extends SD capabilities by incorporating the ER rule for analysing system uncertainty. By using the belief distributions provided by the ER model, the SD model can simulate the variability of the process given certain pieces of evidence.This study contributes to the existing knowledge in aircraft maintenance management by analysing, from a different perspective, the impact of uncertain unscheduled maintenance activities on delays and disruptions through an integrated approach using SD and the ER rule. Despite the fact that this research focuses on studying a particular problem in the airline industry, the findings and conclusions obtained could be used to understand and address problems embodying similar characteristics. Therefore, it can be argued that, due to the close similarities between the heavy maintenance process and complex projects, these contributions can be extended to the Project Management field.
Date of Award1 Aug 2016
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJian-Bo Yang (Supervisor) & Yu-Wang Chen (Supervisor)


  • Unscheduled activities
  • Uncertainty
  • Evidential Reasoning Rule
  • Heavy maintenance
  • Aircraft maintenance
  • Non-routines
  • System Dynamics

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