Symmetry-Driven Multi-Objective Dream Optimization for Intelligent Healthcare Resource Management and Emergency Response

Abstract

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time.