Title: A maintenance optimisation approach based on genetic algorithm for multi-component systems considering the effect of human error

Authors: Hagag Maher; Mohamed F. Aly; Islam H. Afefy; Tamer F. Abdelmaguid

Addresses: Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt ' Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt ' Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt ' Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt

Abstract: The total maintenance cost can be reduced by grouping maintenance actions of several components. This paper contributes to the existing literature by introducing an enhanced maintenance optimisation approach that considers the effect of maintenance crew loading due to grouping on the maintenance decisions of multi-component systems. A modified mathematical model is firstly developed for evaluating the failure probability function of each component, the remaining useful life and the maintenance cost. Economic and structural dependencies are taken into consideration. A simulation is secondly implemented to provide estimates of the associated costs with changes in the decision variables. Using the simulation model, an optimisation approach based on a genetic algorithm is thirdly developed to minimise the long-term mean maintenance cost per unit time. Computational results show that the proposed maintenance optimisation approach provides considerable maintenance cost savings and emphasises the importance of considering the effect of maintenance crew constraints in maintenance scheduling.

Keywords: maintenance grouping; multi-component systems; genetic algorithm; maintenance human constraints; maintenance process simulation.

DOI: 10.1504/IJISE.2022.120803

International Journal of Industrial and Systems Engineering, 2022 Vol.40 No.1, pp.51 - 78

Received: 24 Oct 2019
Accepted: 09 Jan 2020
Published online: 09 Feb 2022 *

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