Quarantine-aware home healthcare routing and scheduling: a bi-objective approach

Abstract The COVID-19 pandemic has presented an unparalleled challenge to the healthcare sector, emphasizing the vital role of Home Healthcare (HHC) services in delivering essential medical care to patients and the elderly within their homes. This approach has proven to be the most effective means of adhering to quarantine protocols. In response, healthcare managers/decision-makers face the imperative of cost reduction, service quality enhancement, and the assessment of patient and nurse satisfaction. To address these pressing needs, our research introduces a Mixed Integer Linear Programming (MILP) model tailored to the COVID-19 era. The model's central objective is to augment the operational efficiency and patient satisfaction of HHC organizations while ensuring strict adherence to quarantine regulations. It builds upon the foundational Vehicle Routing Problem with Pickup/Delivery and Time Window formulation, encompassing critical aspects like patient and caregiver classification, work regulations, workload balancing, and multi-depot capabilities. The bi-objective model considers the primary constraints associated with quarantine conditions. For model resolution, we employ the augmented ɛ-constraint (AUGMECON) method and conduct several sensitivity analyses related to workload balancing's impact on other decision variables. To illustrate the problem’s complexity and assess the effectiveness of the proposed MILP model across various scenarios, 15 additional sample instances have been solved and documented in the Appendix. In conclusion, our research not only provides essential managerial insights but also highlights avenues for future research within this crucial domain.