Scheduling Chemotherapy Outpatient Appointments: A Self-adaptive Metaheuristic Approach

In this paper, we address the chemotherapy outpatient scheduling problem with the aim of reducing the total patient waiting time. Since the problem is characterized by several sources of uncertainty, a stochastic approach is adopted. A simulation model based on discrete-time recursive equations, which includes all the steps of the oncology process, was developed. In particular, the simulation model emulates the therapy preparation and transportation process. Since the oncology and pharmacy can be located in different buildings, the therapies are collected in batches and delivered by a courier service. The simulation model is embedded in a stochastic metaheuristic algorithm to evaluate the candidate solution for the chemotherapy outpatient scheduling problem. A novel metaheuristic algorithm, namely Self-Adaptive Harmony Search (SAHS), is here proposed and its effectiveness is demonstrated through an experimental comparison with a well-known Harmony Search (HS) and Greedy Randomized Adaptive Search Procedure (GRASP) algorithms. Specifically, non-parametric tests revealed that the difference between the performance of SAHS and HS is not statistically significant. Then, SAHS is preferable since it avoids the time-consuming calibration phase.