Blood Bank Inventory Control with Transshipments and Substitutions

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 636)


In this section, we focus on the combination of two flexibility instruments (also see Chap. 1) that are available for the control of multi-location multi-product inventory systems: Lateral stock transshipments Product substitutions

As mentioned in Sect. 2.3, the reasons for performing transshipments and substitutions (see Chap. 1) are often similar. They are summarized in Table 8.1

Both transshipments and substitutions can be differentiated into two types: preventive and reactive. Preventive (also: proactive, planned) transshipments are performed before a stock-out actually occurs, whereas reactive (also: emergency) transshipments are initiated after the location has run out of stock for a product (Herer et al., 2006). The latter require that the transshipment lead time is short enough to be able to fulfill the demand. Preventive substitution means that we start using substitutes before a stock-out of the requested product occurs, e.g., to reserve some stocks for high-priority demand. Reactive substitutions are performed if the requested product is already out of stock. These aspects are summarized in Table 8.2.


Blood Group Search Direction Blood Bank Small Hospital Empty Container 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Chang, H., Jula, H., Chassiakos, A., & Ioannou, P. (2008). A heuristic solution for the empty container substitution problem. Transportation Research Part E: Logistics and Transportation Review, 44(2), 203–216.CrossRefGoogle Scholar
  2. Chapman, J., Milkins, C., & Voak, D. (2000). The computer crossmatch: A safe alternative to the serological crossmatch. Transfusion Medicine, 10(4), 251–256.CrossRefGoogle Scholar
  3. Cohen, M., & Pierskalla, W. (1979). Target inventory levels for a hospital blood bank or a decentralized regional blood banking system. Transfusion, 19(4), 444–454.CrossRefGoogle Scholar
  4. Fink, A., & Reiners, T. (2006). Modeling and solving the short-term car rental logistics problem. Transportation Research Part E, 42(4), 272–292.CrossRefGoogle Scholar
  5. Georgsen, J., & Kristensen, T. (1998). From serological to computer cross-matching in nine hospitals. Vox Sanguinis, 74(2), 419–25.Google Scholar
  6. Hemmelmayr, V., Doerner, K., Hartl, R., & Savelsbergh, M. (2007). Delivery strategies for blood products supplies (Working paper). Vienna: Department of Business Administration, University of Vienna.Google Scholar
  7. Herer, Y., Tzur, M., & Yücesan, E. (2006). The multilocation transshipment problem. IIE Transactions, 38(3), 185–200.CrossRefGoogle Scholar
  8. Jennings, J. (1973). Blood bank inventory control. Management Science, 19(6), 637–645.CrossRefGoogle Scholar
  9. Katsaliaki, K., & Brailsford, S. (2007). Using simulation to improve the blood supply chain. Journal of the Operational Research Society, 58, 219–227.Google Scholar
  10. Lewis, R., & Torczon, V. (2000). Pattern search methods for linearly constrained minimization. SIAM Journal on Optimization, 10, 917–941.CrossRefGoogle Scholar
  11. Nahmias, S. (1982). Perishable inventory theory: A review. Operations Research, 30(4), 680–708.CrossRefGoogle Scholar
  12. Page, B., & Kreutzer, W. (2005). The Java simulation handbook – Simulating discrete event systems with UML and Java. Aachen, Germany: Shaker.Google Scholar
  13. Pereira, A. (2005). Blood inventory management in the type and screen era. Vox Sanguinis, 89(4), 245–250.CrossRefGoogle Scholar
  14. Pierskalla, W. (2005). Supply chain management of blood banks. In M. Brandeau, F. Sainfort, & W. Pierskalla (Eds.), Operations research and health care: A handbook of methods and applications (pp. 103–145). Boston: Kluwer.Google Scholar
  15. Prastacos, G. (1984). Blood inventory management: An overview of theory and practice. Management Science, 30(7), 777–800.CrossRefGoogle Scholar
  16. Scott, C., & Scott, J. (2006). Efficient allocation of online grocery orders. International Journal of Productivity and Quality Management, 1(1), 88–102.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Law, Business and Economics Chair of Operations ResearchTechnische Universität DarmstadtDarmstadtGermany

Personalised recommendations