A Hybrid Genetic Algorithm Approach based on Patient Classification to Optimize Home Health Care Scheduling and Routing

This study aims to solve the multi-objective problem of home healthcare scheduling and routing. The former’s objectives are to upgrade the travel distance, the workload balance, and the waiting time of caregivers. A novel approach was proposed based on patient and caregiver clustering with the K-means++ algorithm in the first step and a hybrid genetic algorithm to optimize the global operation in the second step. The problem was solved regarding the deterministic and the uncertain aspect. The uncertain parameter investigated is the number of patients. A numeric study was conducted to prove the performance of the recommended approach using the Solomon Benchmark.