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Related Experiment Video

Updated: Aug 25, 2025

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Published on: November 30, 2015

Determining optimal locations for blood distribution centers.

Merel L Wemelsfelder^1,2, Dick den Hertog², Onno Wisman³

¹Donor Medicine Department, Sanquin Research, Amsterdam, The Netherlands.

|October 14, 2022

Summary

This summary is machine-generated.

Optimizing blood bank distribution center locations can reduce transportation costs by 10% while maintaining delivery reliability. This supply chain model ensures efficient blood product delivery to hospitals.

Keywords:

facility location linear programming optimization supply chain

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A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy

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Area of Science:

Operations Research
Healthcare Logistics
Supply Chain Management

Background:

Effective blood bank supply chain management is crucial for meeting hospital demand.
Distribution center (DC) location and quantity significantly impact transportation costs and emergency delivery times.

Purpose of the Study:

To develop and demonstrate an optimization model for determining ideal distribution center locations for blood banks.
To minimize transportation costs and maximize hospital reachability within time constraints.

Main Methods:

A mixed-integer linear programming approach was employed to optimize DC placement.
The model considers hospital locations and allows for extensions to incorporate user-specific needs.
Case studies were conducted for blood banks in the Netherlands and Finland.

Main Results:

Re-locating DCs can decrease transportation costs by approximately 10% without compromising delivery reliability.
The number of DCs can be reduced in both countries, leading to facility cost savings while maintaining service levels.
The model proved robust against variations in hospital ordering patterns.

Conclusions:

The developed optimization model is broadly applicable and valuable for enhancing blood supply chain efficiency.
Significant cost savings in both transportation and facility operations are achievable.
The open-source model code is publicly accessible for wider adoption and improvement.