A Hybrid AI-Driven Framework for Resilient Blood Supply Chain Optimization Under Uncertainty
DOI:
https://doi.org/10.23055/ijietap.2026.33.1.11245Keywords:
Blood Supply Chain, Hybrid AI; LSTM, Fuzzy Optimization, Resilience, NSGA-IIAbstract
Designing a resilient and sustainable blood supply chain is vital for maintaining continuous access to life-saving blood products, particularly during uncertain and disruptive conditions. This study presents a hybrid framework that combines deep-learning-based demand forecasting with a multi-objective robust optimization model. A Long Short-Term Memory network generates fuzzy forecasts under optimistic, base, and pessimistic scenarios. These forecasts are then defuzzified and used as inputs for an optimization model that aims to minimize operational cost, reduce blood shortages, and lower environmental impact through improved facility location, donor allocation, and transportation planning. The framework is tested using large-scale evolutionary optimization and validated on smaller instances with an exact mathematical solver. A real case study from Fars Province, Iran, shows improved forecasting accuracy, stronger resilience under capacity disruptions, and balanced trade-off solutions for decision-makers. The proposed approach offers a practical tool for enhancing blood supply chain performance under uncertainty.
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