Designing an efficient supply chain network for public cord blood bank and quality prediction to improve performance: a case study in Iran

Zahra Mohammadian-Behbahani, Behrooz Karimi (), Morteza Zarrabi and Ashkan Mozdgir
Additional contact information
Zahra Mohammadian-Behbahani: Amir-Kabir University of Technology (Tehran Polytechnic)
Behrooz Karimi: Amir-Kabir University of Technology (Tehran Polytechnic)
Morteza Zarrabi: Royan Institute for Stem Cell Biology and Technology
Ashkan Mozdgir: Kharazmi University

Operational Research, 2025, vol. 25, issue 3, No 15, 40 pages

Abstract: Abstract Cord blood (CB), a vital source of hematopoietic stem cells, is increasingly used to treat various hematologic diseases. However, the limited number of CB cells remains a problematic factor restricting their use. Managing public cord blood banks and transplantation centers is a significant challenge for all countries. This study introduces a multi-objective mixed-integer linear programming model for designing the public cord blood supply chain. The model is developed to optimize performance by reducing costs and unmet demands, as well as by improving accessibility. Various machine learning algorithms have also been incorporated to evaluate and predict CB quality. A solution approach based on Lagrangian relaxation and the augmented ε-constraint method has been developed to overcome problem complexity and solve large-scale instances. The proposed model's practicality is validated through a case study in Iran. The results demonstrate the optimal location and allocation of network facilities. Comparative analysis reveals that the proposed supply chain model fulfills 37% more demand than the current system. Among the four quality predictive models analyzed, the K-nearest neighbor (KNN) method stood out, delivering an impressive 98.39% accuracy and a 90.85% AUC (Area Under the Curve) score. Implementing this method could potentially reduce 3% of the total costs of the supply chain. Sensitivity analyses are also provided to assess the trade-offs between objective functions and to offer valuable insights for decision-makers.

Keywords: Cord blood supply chain network design; Quality prediction; Machine learning algorithms; Lagrangian relaxation (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-025-00947-9 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:operea:v:25:y:2025:i:3:d:10.1007_s12351-025-00947-9

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-025-00947-9

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

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