Joint Optimization of Time-Aware Condition-Based Maintenance and Repair Resource Management for Gantry Crane Clusters Based on Improved MADDPG - Shiao Yao; Daofang Chang; Haitao Song; Congming Wu; Jingsen Huang (IEEE Access)
Conventional maintenance strategies for port cranes often lack intelligence, flexibility, and global optimization, with insufficient consideration of time awareness. To optimize condition-based maintenance and resource management for crane clusters, this study decouples maintenance decisions for individual cranes from the overall cluster resource management. We formulate a decision-making model, incorporating uncertainties in procurement lead times, costs, equipment downtime, and spare parts shortages. To improve the model-solving process, we present the evolutionary multi-head attention critic with adaptive strategy–multi-agent deep deterministic policy gradient (EMACAS-MADDPG) algorithm, an enhanced version of the multi-agent deep deterministic policy gradient (MADDPG) algorithm. This algorithm initially evolves policy network parameters through a genetic algorithm and subsequently refines them using experience buffer data. Furthermore, a multi-head self-attention mechanism is embedded into the critic network, and an adaptive exploration strategy is utilized during action execution. The implementation of the EMACAS-MADDPG algorithm in the joint optimization model significantly reduces the average maintenance cost by 22.37% compared to the original MADDPG and by 51.73% compared to the Independent Proximal Policy Optimization (IPPO) algorithm.