A resilience-based maintenance optimisation framework using multiple criteria and Knapsack methods

Ahmed Abdou Karar*, Ashraf Labib, Dylan Jones

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Business fluctuations and pandemics such as COVID 19 have revealed the need for more resilient approaches and processes in the asset management domain. This research aims to design a resilience-based maintenance optimisation (RbMO) framework that absorbs the fluctuations in the operating context and sustains asset performance at optimum maintenance cost and acceptable risk. The paper proposes a framework that employs the analytical hierarchy process (AHP) to translate the different operating context parameters into risk aspects with relative weights that differ from one operating scenario to another. Then, the Knapsack method uses these relative weights to define the risk reduction of each maintenance task and pick the optimum ones within the allocated maintenance budget. Additionally, the approach introduces the nested criticality grid (NCG), which graphically demonstrates the inherent, Knapsack and residual risk profile from the failure mode level up to the unit level enabling an informative decision-making process, where the asset owner can wisely distribute the maintenance budget or achieve efficient cost savings.
Original languageEnglish
Article number109674
JournalReliability Engineering and System Safety
Early online date3 Oct 2023
Publication statusPublished - 1 Jan 2024


  • analytical hierarchy process
  • Knapsack method
  • maintenance optimisation
  • data-driven decision making

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