Publication: Hybrid simulation and optimization of emergency blood supply chain
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Date
2024-07-01
Authors
Madhuumithaa A/P Simon
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Abstract
The optimization of the emergency blood supply chain plays a vital role in ensuring an adequate blood unit inventory in hospital during disasters. This research presents an integrated approach combining Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) using Anylogic to minimize the total cost of the emergency blood supply chain. This approach considers the behaviors of blood centers, hospitals, and blood transportation vehicles, including both vehicles with mobile blood processing capabilities and those solely transporting whole blood. The study uses the Wenchuan earthquake disaster in 2008 as a case study and two scenarios were derived with varying weights assigned to a weighted optimization objective. The optimization model was solved using a Genetic algorithm (GA). The optimization experiments demonstrated that strategic adjustments in vehicle allocation and logistics planning could significantly reduce overall costs while ensuring timely and efficient blood supply to meet demand. Scenario 2 demonstrates a substantial reduction in total cost, achieving RMB 12,038.253 compared to RMB 47,061.209 for Scenario 1, representing a 74.4% improvement despite a 50% increase in patient arrivals and adjusted cost weightings. Specifically, the cost of waiting in the hospital was eliminated in both scenarios, demonstrating the acknowledgment of prioritisation in hospital operations. Upon further study, it has been discovered that there were an overlap in vehicle coverage, a predominance of vehicles equipped with blood processing capabilities, and the existence of shortest paths in vehicles. These factors may contribute to the superiority of the solution. This integrated model provides a robust tool for decision-makers to enhance the efficiency and responsiveness of the blood supply chain in emergency situations, and it demonstrates the benefits of hybrid simulation modeling and optimization in healthcare