An enhanced artificial bee colony algorithm for the multi-mission selective maintenance problem with learning-forgetting effects

Complicated systems routinely serve to carry out a series of planned missions separated by predetermined breaks, with maintenance activities constrained by limited resources. To develop a more pragmatic maintenance model, a multi-mission selective maintenance optimization model is formulated, incorporating both learning and forgetting effects. It aims to reach the minimal phased-mission reliability maximization under maintenance time constraints. To address the proposed model, an enhanced artificial bee colony algorithm is specifically tailored. Applied to a real-life coal transportation system, the developed approach demonstrates superior effectiveness compared to two alternative methods. The experimental results provide well-informed and high-quality maintenance strategies for decision-makers.