Abstract
China’s geographical location is unique, and natural calamities occur frequently. In 2021, a total of 107 million people suffered from various natural disasters, and the direct economic loss reaches as high as 334.02 billion yuan. As a result, dealing with emergencies is a significant burden for the government. Improving the timeliness of emergency logistics response is a critical strategy to safeguard the national economy and people’s livelihood in times of crisis. The important data in the field of emergency logistics is unstructured or poorly structured, and there is a shortage of key information in the sector. Worse, the “data-information-knowledge” dilemma has not been sufficiently transformed. A structured semantic knowledge base is referred to as a knowledge graph. Currently, knowledge graph technology is used in a variety of industries, including medical care, e-commerce, and so on. This research provides a decision framework for emergency logistics based on knowledge graph to realize the intelligence of emergency logistics response.
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Acknowledgements
This paper was funded by the National Natural Science Foundation of China (72101033 and 71831001), the Beijing Key Laboratory of Intelligent Logistics Systems (BZ0211), the Canal Plan-Youth Top-Notch Talent Project of Beijing Tongzhou District (YHQN2017014), the Scheduling Model and Method for Large-scale Logistics Robot E-commerce Picking System based on Deep Reinforcement Learning (KZ202210037046), the Fundamental Research Funds for the Central Universities No. 2015JBM125 and the Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2018KF-01).
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He, L., Li, J., Zhao, M., Yuan, R. (2023). Research on Emergency Logistics Decision Platform Based on Knowledge Graph. In: Shang, X., Fu, X., Ma, Y., Gong, D., Zhang, J. (eds) LISS 2022. LISS 2022. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-2625-1_15
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