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Simulation-optimization model for production planning in the blood supply chain.

Andres F Osorio1,2, Sally C Brailsford3, Honora K Smith4

  • 1Southampton Business School, University of Southampton, Southampton, SO17 1BJ, UK. Afo1e13@soton.ac.uk.

Health Care Management Science
|June 6, 2016
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Summary

This study introduces an integrated simulation-optimization model to improve blood supply chain production planning. The model enhances decision-making, reducing shortages and costs for better blood resource management.

Keywords:
Blood collectionBlood supply chainOptimizationProduction planningSimulation

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Healthcare Logistics

Background:

  • Blood supply chain management faces challenges due to uncertain supply and demand, blood group variations, and shelf-life limitations.
  • Effective decision-making requires advanced methodologies to balance complex operational factors.
  • Existing models may not fully integrate strategic and operational planning needs.

Purpose of the Study:

  • To develop and evaluate an integrated simulation-optimization model for blood supply chain production planning.
  • To support both strategic and operational decision-making processes.
  • To improve key performance indicators within the blood supply chain.

Main Methods:

  • Utilized discrete-event simulation to model blood supply chain flows (collection, production, storage, distribution).
  • Employed an integer linear optimization model with a rolling planning horizon for daily operational decisions.
  • Integrated simulation and optimization for a comprehensive approach.

Main Results:

  • The integrated model demonstrated significant improvements in key performance indicators.
  • Reduced blood unit shortages and minimized outdated units.
  • Optimized donor requirements and decreased overall operational costs.

Conclusions:

  • The proposed simulation-optimization model effectively enhances blood supply chain production planning.
  • It provides a robust framework for strategic and operational decision support.
  • Implementation leads to improved efficiency and resource utilization in blood centers.