This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.
A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.
Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.
The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.
Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.
A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administration Region, China (UGC/FDS14/E05/18).
Qin, Y., Ma, H.-L., Chan, F.T.S. and Khan, W.A. (2020), "A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider", Industrial Management & Data Systems, Vol. 120 No. 9, pp. 1635-1657. https://doi.org/10.1108/IMDS-03-2020-0131Download as .RIS
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