An adaptive scheduling heuristic with memory for the block appointment system of an outpatient specialty clinic

This work analysed the appointment system of outpatient clinics serving multiple patient classes with different flow sequences through the multi-phase-multi-server service system. Scarce resources are doctors, nurses and medical professionals with different start times and availability. Block appointment systems are typically used in public hospitals to help regulate patient flow while minimising patient waiting time, staff overtime and waiting room congestion. The patient scheduling problem in this complex environment is formulated by a mixed integer programme (MIP). Making use of waiting time information, an adaptive scheduling heuristic is designed to improve an initial schedule iteratively by identifying procedures with large average waiting times and reassigning their related patient classes to less congested time blocks probabilistically. An impact index based on the weighted multi-objective function is developed to allow servers select an available patient for the next treatment. A memory of distinct solutions is maintained to avoid recycling. Experiments are conducted based on a case study of an eye clinic in a public hospital. Performance is evaluated by comparing with the MIP and well-known dispatching rules for job shop scheduling problems. Sensitivity analysis is conducted for increase in appointment quota, two alternative staffing plans and changes in patient class distribution.