Staff scheduling in blood collection problems

• Published in: Annals of operations research Vol. 316; no. 1; pp. 365 - 400
• Main Authors: Li, Xiang, Fan, Haoyue, Liu, Jiaming, Xun, Qifeng
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer; Springer Nature B.V
• Subjects: Analysis; Blood; Blood & organ donations; Blood banks; Blood collection and preservation; Business and Management; Collection; Combinatorics; Demand; Human resource management; Learning models (Stochastic processes); Linear transformations; Mathematical models; Methods; Operations research; Operations Research/Decision Theory; S.I. : Scalable Optimization and Decision Making in OR; Scheduling (Management); Task scheduling; Theory of Computation; China; Blood collection; Staff scheduling; Staff transfer; Stochastic demand
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-020-03688-4

To improve the blood collection volume and reduce the staff costs, this study investigates the staff scheduling problem faced by blood donation center under variant situations derived from reality. Staff scheduling for blood donation center is a complex task due to the stochastic arriving of blood donors. In this study, we propose the deterministic demand model and stochastic demand model of blood donors according to the number of donors arriving at donation sites. Then, based on the deterministic demand and the stochastic demand models, we consider the number of staff in the blood donation center and group blood donation. Five blood collection scenarios are proposed using the combination of donors and staff. To solve the proposed models, linear transformation and lexicographic order optimal method are applied. To verify the effectiveness of the proposed models, both large-scale and small-scale numerical experiments are conducted and its the stability is also validated using the 10 times of random numerical experiments. All of the experimental results show that the proposed models need few staff when facing adequate staff scenarios, and reduce the number of donors when facing inadequate staff scenarios.