Reliability-oriented and Cost-Saving Stochastic Selective Maintenance Optimisation Using a Learning-Driven Multi-Objective Artificial Bee Colony Algorithm

Selective maintenance problems are intensively investigated by both scholars and practitioners. Nonetheless, existing research on such problems usually overlooks the comprehensive consideration of diverse decision criteria and uncertain maintenance environments in terms of stochastic mission and break durations. This work proposes a stochastic multi-objective selective maintenance problem that considers stochastic mission and break durations and puts forward the corresponding optimisation method. First, a stochastic multi-objective mixed-integer nonlinear chance-constrained programming model is constructed to maximise mission reliability and minimise maintenance cost under maintenance time constraints. Second, a learning-driven multi-objective artificial bee colony algorithm is tailored to tackle the proposed model. In the designed approach, a Q-learning method in the employed bee phase and an iterative local search method in the onlooker phase are deployed to enhance the exploration and exploitation abilities. Finally, through conducting comparison experiments between the customised method and five popular methods on several real-world cases, the experimental results confirm its feasibility and superiority in addressing the problem of interest.