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A post-disaster emergency logistics location-routing optimization method considering road risks
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AI
DOI：10.1080/19427867.2025.2528818.png)
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Abstract
En 中文
Sudden natural disasters pose significant threats to lives, property, and infrastructure, underscoring the critical need to determine optimal locations for emergency reserve depots and plan efficient transportation routes. Therefore, this paper focuses on the location-routing optimization in emergency logistics following a disaster. In addition to the traditional considerations of rescue time and cost, we propose a novel approach that incorporates risk factors of transportation routes. The aim is to avoid secondary disasters during transport and improve rescue efficiency. Using the 2008 Wenchuan earthquake as a case, results show the hybrid machine learning CPSO-XGBoost approach employed as our road segment risk identification model achieves 0.9668 accuracy. Compared to NSGA-II, the improved ACONSGA-II algorithm shows superior optimization capability. Unlike traditional emergency location-routing models that overlook road risks, the proposed approach identifies safer location-routing solutions for material transport while maintaining rescue time and cost.
Keywords:
Emergency logistics
location-routing optimization
road segment risk
CPSO-XGBoost
ACONSGA-II
Journal
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Papers:
88
Citations:
1