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

Jiao Zhao, Tao Wang and Thibaud Monteiro ()
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Jiao Zhao: INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, 69621 Villeurbanne, France
Tao Wang: Université Jean Monnet Saint-Etienne, INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, DISP UR4570, 42300 Roanne, France
Thibaud Monteiro: INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, 69621 Villeurbanne, France

IJERPH, 2024, vol. 21, issue 3, 1-27

Abstract: 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. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations.

Keywords: home health care; routing and scheduling problems; bi-objective optimization; adaptive large neighborhood search; uncertainty (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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