Deep-Reinforcement-Learning-Based Multiobjective Optimization for Carbon Intelligent IIoT-Enabled Healthcare Buildings

The optimization of modern healthcare facilities presents unique challenges at the intersection of medical service quality, energy efficiency, and environmental impact. By integrating carbon-intelligent Industrial Internet of Things (IIoT) technologies with healthcare operations, our approach enables real-time monitoring and optimization of carbon emissions while maintaining medical service quality. Specifically, this article presents a novel deep-reinforcement-learning-based multiobjective optimization algorithm (HC-DMOPSO) for IIoT-enabled healthcare building management. By integrating healthcare-specific constraints with an enhanced swarm intelligence framework, our approach optimizes building operations while considering medical device power demands, patient comfort, and environmental requirements. The proposed algorithm combines dual-distance metrics—population average distance and crowding distance—with deep Q-networks to effectively explore the complex solution space. Experimental results demonstrate HC-DMOPSO’s superior performance across multiple metrics: The integration of carbon intelligent IIoT sensors and actuators enables HC-DMOPSO to achieve 24.8% reduction in energy consumption while maintaining 99.92% medical power reliability, 32.5% decrease in peak load with only 0.38°C average temperature deviation, and 28.7% improvement in carbon reduction compared to baseline methods.