Outpatient Appointment Optimization: A Case Study of a Chemotherapy Service

Abstract

In this paper, we study a complex outpatient planning problem in the chemotherapy department. The planning concerns sequences of patients’ treatment sessions subject to exact in-between resting periods (i.e., exact time-lags). The planning is constrained by the hospital infrastructure and the availability of medical staff (i.e., multiple time-varying resources’ availability). In order to maximize the patients’ service quality, the objective of the function considered is to minimize the total wait times, which is equivalent to the criteria for minimizing the total completion time. Our main contribution is a thorough analysis of this problem, using the Hybrid Flow Shop problem as a theoretical framework to study the problem. A novel Mixed Integer Linear Programming (MILP) is introduced. Concerning the resolution methods, priority-based heuristics and an adapted genetic algorithm (GA) are presented. Numerical experiments are conducted on historical data to compare the performances of the approximate resolution methods against the MILP solved by CPLEX. Numerical results confirm the performances of the proposed methods.

Robust Appointment Scheduling in Healthcare

“…This model proposed in this paper generates a workforce schedule that minimizes overtime and balances a doctor's workload over a year, satisfying weekly patient demand. The objective function minimizes the addition between the workload of each pair of patients and the number of yearly overtime hours of the workforce as well [40][41][42][43][44]. By assuming that the input is only patients with a single session and that consultation and installation time are zero, the considered problem without resource constraints is reduced to the two stages as given in the flow shop problem, which is NP-hard [45].…”