1701. Resilient Home Healthcare Planning during Disruptions with Interagency Collaboration
Invited abstract in session TA-11: Home Health Care, stream OR in Healthcare (ORAHS).
Tuesday, 8:30-10:00Room: Clarendon SR 1.03
Authors (first author is the speaker)
| 1. | Mahla Tayarani Zare
|
| School of Information Technology, Deakin University | |
| 2. | Dhananjay Thiruvady
|
| School of Information Technology, Deakin University | |
| 3. | Mahdi Bashiri
|
| Coventry University | |
| 4. | Benny Tjahjono
|
| Coventry University |
Abstract
Demand for Home healthcare (HHC) services has increased due to an aging population. Disruptions like disasters can interrupt HHC care continuity, placing vulnerable groups' conditions at risk. Resilient HHC planning is vital for recovery from disruptions; without it, agencies risk losing patients to hospitals, causing financial losses and higher hospitalizations. A two-stage stochastic model for resilient HHC planning is developed, integrating pre- and post-disruption phases with agency collaboration, unlike existing studies focusing solely on post-disruption without collaboration. In the first stage, agencies hire on-call temporary caregivers before disruptions under patients' uncertainty. The second stage optimizes daily caregiver routing and scheduling, integrating full-time, temporary, and shared caregivers whose availability remains uncertain until each day. The objective function minimises costs and over-qualification time, which is the time caregivers spend on tasks below their expertise. It ensures skilled caregivers are available for critical cases of other agencies, even if it incurs higher costs. Small instances are solved using Gurobi, while a heuristic algorithm is developed to handle large instances efficiently. Numerical analysis shows that caregiver sharing between agencies increases the total number of patients visited compared to the classic HHC models. The effectiveness of the proposed model is tested under disruptions with greater uncertainty.
Keywords
- Health Care
- Programming, Stochastic
- Scheduling
Status: accepted
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