Optimization of non-ergodic maintenance resource allocation driven by system resilience

System resilience theory is gradually being applied in post-disaster maintenance resource allocation, but it also raises the demand for more scientific and timely decision-making. An increasing number of various types of disasters have exposed some shortage of research in this field. To improve the rationality and efficiency of resource allocation, this paper proposes a general resource allocation decision-making framework for emergency maintenance scenarios that is driven by system resilience. Furthermore, a stepwise approach to resilience characterization and calculation is proposed to guide the determination of optimization objectives. Then the allocation decision-making optimization model is formulated based on mixed-integer linear programming to maximize the overall recovery phase resilience. Finally, a medium-sized dataset is generated and used for the experiments. The experimental result shows that our proposed model can effectively find the optimal solution, which can better plan the maintenance resources as well as its coordinated transport routes.