Distributionally Robust and Learning-Based Vehicle Routing for Disaster Blood Logistics
In humanitarian disasters, delivering blood and blood products rapidly and reliably is critical but logistically complex. This paper proposes a hybrid framework that integrates Distributionally Robust Optimization (DRO) for offline anticipative planning with Contextual Bandits for online adaptive ve...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11059272/ |
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| Summary: | In humanitarian disasters, delivering blood and blood products rapidly and reliably is critical but logistically complex. This paper proposes a hybrid framework that integrates Distributionally Robust Optimization (DRO) for offline anticipative planning with Contextual Bandits for online adaptive vehicle dispatch. The system coordinates a heterogeneous fleet of ambulances, drones, and robots to navigate uncertain demand, infrastructure degradation, and time-sensitive deliveries. We formulate a two-level approach: a robust vehicle routing problem accounting for moment-based uncertainty in demand, and an online dispatch policy that learns context-aware allocation decisions in real time. Extensive simulations evaluate the proposed method against DRO-only, CB-only, and stochastic routing baselines. Results show that the hybrid approach outperforms all others in robustness, service rate, and delay, while maintaining reasonable computational efficiency. |
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| ISSN: | 2169-3536 |