MODELING AND OPTIMIZING BLOOD SUPPLY CHAIN INVENTORY MANAGEMENT USING BEE COLONY AND GENETIC ALGORITHMS

Resumen

This study explores the optimization of blood supply chain inventory management through innovative approaches, specifically Bee Colony Optimization (BCO) and Genetic Algorithms (GA). The research addresses challenges in healthcare logistics, emphasizing the 
integration of organizational units involved in blood sourcing, production, distribution, and marketing. Key considerations include the potential conflicts between cost minimization in sourcing decisions and the focus on throughput in production and distribution. The study highlights the significance of achieving an optimal balance to ensure a reliable and efficient blood supply for patient care. Bee Colony Optimization and Genetic Algorithms, inspired by natural processes, offer promising solutions to the complexities of blood inventory management. BCO mimics collaborative foraging behavior, creating optimal paths marked by pheromones. Genetic Algorithms replicate natural selection to iteratively enhance solutions. The research aims to provide valuable insights into the application of these algorithms, contributing to the evolution of efficient blood supply chain management. The anticipated outcomes include improved healthcare logistics, ensuring timely access to blood products and enhancing patient safety and outcomes.