Multi-resource constrained elective surgical scheduling with Nash equilibrium toward smart hospitals

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Elective surgical scheduling is complex due to multi-resource constraints.
• Optimizing schedules involves balancing patient recovery, staff workload, and costs.

Purpose of the Study:

• To develop an optimized scheduling model for elective surgeries.
• To minimize average patient recovery time, staff overtime, and total medical costs.
• To validate the proposed scheduling approach using digital twin technology.

Main Methods:

• Formulation as a mixed integer linear multi-objective optimization model.
• Development of the honey badger algorithm based on Nash equilibrium (HBA-NE).
• Implementation of digital twin (DT) technology for simulation and verification.

Main Results:

• The HBA-NE algorithm effectively addresses the multi-resource constrained elective surgical scheduling (MESS) problem.
• Experimental studies validated the performance of the proposed surgical scheduling scheme.
• Digital twin technology successfully integrated physical and virtual hospital spaces for surgical process simulation.

Conclusions:

• The proposed HBA-NE approach offers an effective solution for complex surgical scheduling.
• Digital twin technology enhances the verification and implementation of optimal surgical schedules in smart hospitals.