Efficient Route Plan for Intra-Hospital Patient Transport Using Multi-Modal Optimization Methods.

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Intra-Hospital Patient Transport involves complex, multi-objective route planning challenges.
• Task uncertainties and operational demands impact multiple stakeholders and service quality.
• Efficient coordination is crucial for optimizing transporter utilization and patient care.

Purpose of the Study:

• To develop and evaluate multimodal methods for automated Intra-Hospital Patient Transport route planning.
• To optimize transporter numbers, travel times, and idle times for maximal operational flow.
• To account for uncertainties and practical constraints in route optimization.

Main Methods:

• Employed a combination of metaheuristics and Discrete Event Simulation (DES).
• Integrated Mixed Integer Programming and a scoring strategy for route plan improvement.
• Utilized retrospective hospital data to model uncertainties and practical characteristics.

Main Results:

• Discrete Event Simulation demonstrated 45% - 65% faster computational solutions compared to other methods.
• Multimodal optimization methods successfully built statistical models for different datasets.
• The proposed strategies support hospital managers by automating efficient, multi-objective route plans.

Conclusions:

• Multimodal optimization approaches, guided by perceptible strategies, effectively address complex route planning.
• Discrete Event Simulation is a computationally efficient tool for optimizing intra-hospital patient transport.
• Automated route planning supports better resource allocation and improved service quality in hospitals.