A Deep Learning-Based Multi-Objective Optimization Method for Intelligent Maintenance Work Order Allocation

The fire protection maintenance market is expanding rapidly, but the industry faces critical resource optimization challenges due to the inefficiency of existing scheduling methods. Current approaches often exhibit limited feature representation capabilities, inadequate handling of hard constraints (e.g., certification requirements), and poor balancing of multi-objective conflicts, compromising safety, compliance, and efficiency. To address these gaps, this paper proposes a deep learning-based multi-dimensional constraint feature intelligent dispatch algorithm (DL-MFIDA). The method constructs a deep feature learning framework to capture complex relationships between personnel skills and task requirements, incorporates a constraint-aware attention mechanism to strictly enforce hard constraints, and employs a multi-objective hierarchical optimization strategy to dynamically balance spatiotemporal cost minimization, skill matching gain maximization, and load balancing. Experimental results demonstrate that DL-MFIDA achieves an 89.5% assignment success rate even under high-load scenarios (task volume at five times personnel capacity), significantly outperforming traditional methods in key metrics such as allocation success rate, resource utilization, and constraint violation rate. This work provides an effective solution to the “few personnel, many tasks” dilemma in fire protection maintenance, ensuring robust performance in practical applications.