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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.20474 |
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| _version_ | 1866913859951919104 |
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| author | Zhang, Yi Zhang, Zhenzhen |
| author_facet | Zhang, Yi Zhang, Zhenzhen |
| contents | The growing aging population has significantly increased demand for efficient home health care (HHC) services. This study introduces a Vehicle Routing and Appointment Scheduling Problem (VRASP) to simultaneously optimize caregiver routes and appointment times, minimizing costs while improving service quality. We first develop a deterministic VRASP model and then extend it to a stochastic version using sample average approximation to account for travel and service time uncertainty. A tailored Variable Neighborhood Search (VNS) heuristic is proposed, combining regret-based insertion and Tabu Search to efficiently solve both problem variants. Computational experiments show that the stochastic model outperforms the deterministic approach, while VNS achieves near-optimal solutions for small instances and demonstrates superior scalability for larger problems compared to CPLEX. This work provides HHC providers with a practical decision-making tool to enhance operational efficiency under uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Optimization of Service Routing and Scheduling in Home Health Care Zhang, Yi Zhang, Zhenzhen Optimization and Control The growing aging population has significantly increased demand for efficient home health care (HHC) services. This study introduces a Vehicle Routing and Appointment Scheduling Problem (VRASP) to simultaneously optimize caregiver routes and appointment times, minimizing costs while improving service quality. We first develop a deterministic VRASP model and then extend it to a stochastic version using sample average approximation to account for travel and service time uncertainty. A tailored Variable Neighborhood Search (VNS) heuristic is proposed, combining regret-based insertion and Tabu Search to efficiently solve both problem variants. Computational experiments show that the stochastic model outperforms the deterministic approach, while VNS achieves near-optimal solutions for small instances and demonstrates superior scalability for larger problems compared to CPLEX. This work provides HHC providers with a practical decision-making tool to enhance operational efficiency under uncertainty. |
| title | Joint Optimization of Service Routing and Scheduling in Home Health Care |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.20474 |