The daily routing and scheduling problem of home health care: based on costs and participants’ preference satisfaction

Abstract

AbstractImproving the level of service and reducing total costs are the most important objectives for the decision support system of home health care companies. But these objectives are often in conflict. In this study, we aim to minimize the total costs and maximize the participants’ preference satisfaction simultaneously. The problem is formulated as a bi‐objective mixed‐integer linear programming model that considers the skill requirements of patients, hard time windows for the service start time, and the working duration of a nurse. Patient preference satisfaction captures the nurse skill level and nurse–patient familiarity, and nurses’ preference satisfaction captures the overtime duration. To solve this problem, a novel hybrid elitist nondominated sorting genetic algorithm (hybrid NSGA‐II) is developed by embedding a local search algorithm into the basic NSGA‐II framework. Computation results on a set of benchmarking instances show that the developed hybrid NSGA‐II can obtain approximate Pareto‐optimal solutions within a shorter computation time when compared with the ‐constraint method for small instances; it can also perform better than the basic NSGA‐II and SPEA‐II with the size increasing for middle and large instances. Using a small instance, this study also analyzes the problem properties and the trade‐off between total costs and participants’ preference satisfaction.