Heuristics to solve appointment scheduling in chemotherapy

This paper studies meta-heuristic methods in solving an appointment-scheduling problem in a hospital in Canada. Our paper proposes a two-step algorithm: The first step creates an initial solution with a Greedy Algorithm considering many strategies. The second step consists in choosing the best strategy and improving it with Tabu Search. Our algorithm performed well for the appointment-scheduling problem. The data used was collected from a hematology-oncology department in a hospital. It includes information about patients, nurses, treatments and fixed appointments. Patients must be assigned to nurses with specific treatment seats. We focus on 3 main objectives: maximize the number of patients scheduled over a planning horizon, minimize overtime and provide a more balanced workload between nurses. Our algorithm succeeds in scheduling more patients over a planning horizon without overtime and with a better balanced workload between nurses.