An efficient centralized master echocardiography schedule in a distributed hospital/clinic network

Appointment scheduling systems are applied in a broad variety of healthcare environments to reduce costs and increase quality of services. This study is concerned with the problem of appointment scheduling in a distributed multi-hospital network of echocardiography departments. In this paper, a centralized master schedule is presented to maximize profit margin through maximizing the number of performed echoes and minimizing overtime. Developing such a schedule requires handling shift scheduling and capacity allocation problems simultaneously. Based on real-world settings, a mixed integer linear programming model is proposed for the research problem. Since this model requires a large amount of time and memory to provide good solutions, and fails to find feasible solutions for most of the test problems, two metaheuristics are proposed with different approaches. The first one is combined variable neighborhood search with simulated annealing (VNS-SA) and the second one is hybrid particle swarm optimization (HPSO). Also two lower bounding techniques based on patients’ assignment ( ) and specialists’ assignment ( ) are presented. Then the efficiency of the proposed model and algorithms is evaluated using a set of practical-sized test problems. The results showed that VNS-SA is capable of providing high quality solutions in reasonable amount of time for all test problems and outperforms HPSO. Furthermore, the superiority of over and the lower bound provided by the mathematical model was shown from both the quality and computational time points of view. Finally, some managerial notes and suggestions for extension are presented.