A Bi-Objective Home Health Care Routing and Scheduling Problem Under Uncertainty

Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. To this end, this paper proposes a bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care. It extends the classical constraints of the vehicle routing problem by considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed for the problem. Motivated by the challenge of computational time, an adaptive large neighborhood search embedded in an improved multi-directional local search framework is proposed. To be more practical, uncertain service times for patients are considered and handled by using a scenario-based sample average approximation method. Our data set for the computational experiment is based on the Solomon data set. Three kinds of metrics for evaluating the Pareto fronts reveal that our proposed method can fit different structures of instances. A set of experiments for sensitivity analysis validate the robustness of the proposed model and method. We apply the method to a real-life case and provide managerial insights for decision-making