A Scheduling Method for Maintenance Tasks of Damaged Equipment Based on Digital Twin and Robust Optimization

Aiming at the problems that traditional maintenance task scheduling schemes for damaged equipment have, poor adaptability to changes in uncertain factors and difficult-to-deal-with emergency scenarios, this paper proposes a maintenance task scheduling method for battle-damaged equipment based on digital twin (DT) and robust optimization. The purpose is to realize the dynamic synchronization between physical entities and virtual models through DT technology, and to leverage the anti-interference characteristics of robust optimization. The method involves constructing a multi-objective optimization model that maximizes the comprehensive importance of damaged equipment and minimizes maintenance time, and solving the model using the discrete particle swarm optimization (DPSO) algorithm. Simulation results show that this method can improve the efficiency of maintenance scheduling and the anti-interference ability in emergency situations. Through the comparison of three indicators, DT-DPSO performs the best in the maintenance scheduling of battle-damaged equipment: its convergence speed is 33.3% faster than that of DPSO and 20% faster than that of DT-non-dominated sorting genetic algorithm II (DT-NSGAII); its robustness is 16.3% higher than that of DPSO and 10.7% higher than that of DT-NSGAII; its dynamic reallocation speed is more than 40% faster than that of DPSO and more than 30% faster than that of DT-NSGAII. This method is suitable for maintenance scheduling requirements of high speed, stability, and anti-interference.