A Monte Carlo simulation-based simulated annealing algorithm for predicting the minimum staffing requirement at a blood donor centre

Abstract

Australian Red Cross Lifeblood collects blood from non-remunerated voluntary donors. Thus, it is important to ensure that donors experience good service so they will return to donate blood again. Donor experience is adversely influenced by prolonged waiting times, but they may be reduced by determining the staffing demand over the day. In this paper, we propose a Monte-Carlo simulation-based simulated annealing algorithm that seeks the minimum number of employees to meet demand over a single day while ensuring the system’s predicted average waiting time does not exceed a specified threshold. To enhance the efficiency of our simulated annealing algorithm, we develop a novel neighbourhood search method based on the staff occupancy levels. We use data from four different Australian Red Cross Lifeblood donor centres, demonstrating that our methodology can be adapted to any donor centre to determine the minimum staffing demand. Since these staffing demands ensure the donor waiting time target is met for each donor centre, they have the potential to improve both donor and staff satisfaction as well as streamline the donor flow.