Maintenance, repair and overhaul/operations service resource scheduling optimization for complex products in uncertain environment

A stochastic chance-constrained programming mathematical model under uncertain resource scheduling time and uncertain service execution time was built from a realistic perspective, based on the requirement analysis of the collaborative maintenance, repair and overhaul/operations (MRO) service resource scheduling of complex products. A hybrid intelligent algorithm composed of the stochastic simulation, the neural network and the discrete particle swarm optimization algorithm was proposed to solve the proposed optimization problem. The training sample set produced by stochastic simulation was used to train the neural network for the approximation of the optimization objective function. The trained neural network model was used to replace the optimization objective function to perform the optimization iterations of particle swarm algorithm. This hybrid intelligent algorithm can effectively improve the solving rate of bi-objective problem of collaborative MRO service resource scheduling of complex products under uncertain time variables. The results of case study showed that the proposed stochastic chance-constrained programming model and the hybrid intelligent algorithm were more suitable for solving the MRO service resource scheduling problem under uncertainty in the reality, compared with the optimization algorithm under certainty. The proposed scheduling scheme was more robust in practical implementation. © 2019, Zhejiang University Press. All right reserved.