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

Achini Wellalage (), Mark Fackrell () and Lele Zhang ()
Additional contact information
Achini Wellalage: The University of Melbourne
Mark Fackrell: The University of Melbourne
Lele Zhang: The University of Melbourne

Annals of Operations Research, 2024, vol. 342, issue 3, No 27, 1945-1990

Abstract: 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.

Keywords: Staffing demand; Blood donor centre; Simulation; Simulated annealing; Neighbourhood search; Stochastic optimisation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05297-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05297-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05297-3

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().