Francesco Nucci and Gabriele Papadia
Adv. Artif. Intell. Mach. Learn., 4 (4):3114-3124
1. Francesco Nucci: University of Salento
2. Gabriele Papadia: Department of Innovation Engineering, University of Salento
DOI: 10.54364/AAIML.2024.44178
Article History: Received on: 20-Oct-24, Accepted on: 21-Dec-24, Published on: 28-Dec-24
Corresponding Author: Francesco Nucci
Email: francesco.nucci@unisalento.it
Citation: Francesco Nucci and Gabriele Papadia. Optimizing Healthcare Ecosystem Performance - A Computational Study of Integrated Patient Assistance in Primary Care. Advances in Artificial Intelligence and Machine Learning. 2024;4(4):178.
Home health care professionals provide medical services to patients in their homes. With rising demand, it’s crucial to manage operational costs effectively while ensuring satisfaction for patients. This study presents a bi-objective optimization model aimed at resolving routing and scheduling challenges in home health care, with a focus on both system efficiency and patient accessibility. A Mixed-Integer Linear Programming Model (MILP) is developed. To tackle computational time challenges, we propose a Non-dominated Sorting Genetic Algorithm II to solve the multi-objective optimization problems. The evaluation of Pareto fronts demonstrates the method’s efficiency. We apply the method in a real-world case study to provide managerial implications.