Demand-responsive optimization of blood allocation under perishability and import constraints

Blood shortages remain a global challenge due to donor scarcity, seasonal fluctuations, demographic influences, and the perishability of blood products. Existing models often fail to address these gaps, leading to inefficiencies such as oversupply, wastage, and reliance on emergency imports. This study develops a demand-responsive blood allocation framework that explicitly incorporates expiration and importation variables to minimize waste and improve supply–demand alignment. A Multiple Knapsack Assignment (MKA) model was employed to ensure transfusion compatibility and balanced stock levels, while artificial data generation was used to fill missing values. Advanced metaheuristic algorithms, including Symbiotic Organism Search (SOS), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a hybrid SOSGA approach, were implemented and validated using real-world blood bank data. The Iterative Hybrid SOSGA alternates between SOS and GA in successive cycles, where SOS enhances global exploration to reduce blood expiration, and GA refines solutions to minimize importation, thereby improving overall balance between the two objectives. Results show that the Iterative Hybrid SOSGA algorithm achieved the best performance, reducing both expired and imported blood units compared to individual techniques. Overall, this research enhances blood bank operations by aligning supply with demand, minimizing wastage, and reducing reliance on emergency imports, thereby supporting sustainable healthcare delivery and improved patient outcomes.