1264. Scheduling Visits for Dynamically Prioritized Home Care Patients with Fuzzy Demands: A Mobile Health Facility Location, Fleet Sizing, and Routing Problem.
Invited abstract in session TB-21: Healthcare Analytics, cluster Healthcare Management.
Tuesday, 10:30-12:00Room: FENH201
Authors (first author is the speaker)
| 1. | Jamal Abdul Nasir
|
| Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong | |
| 2. | Yong-Hong Kuo
|
| Department of of Industrial and Manufacturing Systems Engineering, The University of Hong Kong |
Abstract
In this paper, we investigate a real-world home health care (HHC) problem, where there are insufficient caregivers to provide home care services to a large number of patients with uncertain demands. Since patients' health conditions fluctuate during a given planning period, we construct a priority function to define their priority level, then translated into a time-dependent potential healthcare cost, varying dynamically over the planning horizon. In practice, logistical services in the HHC supply chain network are frequently disrupted due to a variety of unforeseen events, necessitating resilient and adaptable HHC structures, easily implemented in response to long-lasting extreme weather events, pandemics such as COVID-19, and specialized door-to-door health campaigns. To address these concerns, the studied problem is defined as a multi-depot, multi-period fuzzy chance-constrained programming model with precedence constraints and demand quantities (drugs and vaccines), represented as fuzzy variables. The solution to the studied problem selects the locations for the appropriate number of base mobile health facilities, detects the required number of HHC vehicles, and creates scheduling plans for visiting patients based on patient-specific time windows. To solve the problem, we use a novel three-phased heuristic solution method, as well as stochastic simulation and local search approaches. Taking into account the problem structure, we first exploit the problem characteristics to create an excellent starting solution, then improved using a hybrid simulated annealing algorithm (HSA), various neighborhood structures, and a local search mechanism. The proposed model is tested using a realistic case of HHC services provision in Hong Kong. First, we conduct sensitivity analysis to determine the appropriate values for the threshold parameters regulating the credibility of patients’ assignments to vehicles and mobile health facilities, and then we evaluate the performance of the HSA algorithm. The computational experiments involving a deterministic version of the studied problem and Solomon's benchmark instances indicate the proposed solution method can tackle real-world problems efficiently and effectively. Moreover, the proposed model is more resilient compared to the deterministic model in the context of uncertain demand.
Keywords
- Healthcare Service Delivery
- Healthcare Logistics
- Transportation
Status: accepted
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