A mixed integer linear programming model for quarantine-based home healthcare scheduling under uncertainty

Abstract

Home healthcare companies (HHC) have emerged as vital alternatives to traditional hospitals, particularly in meeting the healthcare needs of individuals within the comfort of their homes. The COVID-19 pandemic has amplified the significance of HHC services, offering a crucial alternative for patients and the elderly to follow quarantine protocols while receiving essential healthcare at home. Consequently, HHC companies must align their planning strategies with the World Health Organization (WHO) health guidelines. This research introduces a Mixed Integer Linear Programming (MILP) model tailored for home healthcare services during COVID-19, aiming to ensure strict adherence to quarantine protocols while enhancing service efficiency and quality. The proposed vehicle routing problem with pickup/delivery and time window formulation incorporates critical elements such as patient and caregiver classification, work and break regulations adherence, workload balancing, and multi-depot capabilities. The model addresses uncertain demand and service times through a stochastic programming approach to enhance practicality. K-means clustering is applied to streamline scenarios, with a sensitivity analysis determining the optimal number of clusters. Additionally, measures intrinsic to stochastic programming, such as the Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS), are computed for comprehensive analysis.