abstract
- The increasing demand for home healthcare (HHC) services due to population aging and hospital capacity issues requires innovative solutions to improve operational efficiency and service delivery. Contrary to the previous related works, this research firstly develops a novel optimization model for home healthcare vehicle routing and scheduling with electric autonomous vehicles (EAVs). With the integration of cyber risk awareness, the model models digital infrastructure and autonomous technology vulnerabilities. The developed problem considers a capacitated vehicle routing problem with time windows (CVRPTW), including limitations from battery capacity and service duration, and patient priority requirements. The optimization aims to minimize total energy expenses while maintaining reliable service delivery. The proposed solution uses a Quantum-Inspired Genetic Algorithm (QiGA) framework and a new chromosome decoding method. The simulation results from real-world-inspired scenarios demonstrate that QiGA performs better than other metaheuristics and that including cyber risk assessments during planning enhances system robustness at a low additional cost. The proposed framework enables healthcare logistics to become smarter and greener, and more secure, which creates opportunities for reliable EAV deployment in essential public health services.