Emergency resources allocation is essential to the emergency rescue effectiveness, and it has become a research focus for emergency rescue. This paper proposes a multiresource dynamic allocation model of emergency rescues and corresponding solving method. The object of the proposed model is to maximize the overall emergency rescue effectiveness of allocated resources and minimize the allocating costs of resources. The model considers the dynamic nature that the casualties of trapped victims change over time. At last, a numerical example is presented to test the model and its algorithm.
The robustness and reliability capacities of highways are particularly critical when dealing with emergencies in order to ensure user safety following disaster events. Assessing the robustness and reliability of highways under multi-hazard scenarios and evaluating the impact of planning on them have become urgent topics. In this study, we use the Xinjiang Production and Construction Corps’ (XPCC) existing and planned arterial highway networks in China for research. Based on the multi-hazard information, we established and employed four attack strategies on the existing and planned arterial highway networks. The results show that the exposure susceptibility coefficient (ESC) strategy has a higher destruction capability than the random attack strategy, which is close to the greedy algorithm coefficient (GAC) strategy. In addition, attacks have negligible impacts on connectivity reliability and robustness but significantly affect travel time reliability and robustness. When the number of removed edges reaches 20 using the ESC strategy, the travel time reliability drops to 0.4 for the existing highway network. In addition, the planned highway network significantly improves the reliability and robustness with regard to multi-hazard scenarios, especially for travel time reliability. Travel time reliability is improved by 10% under the historical damage records coefficient (HDRC) and ESC attacks. Our study shows that planning promotes the construction of a resilient transportation system in multi-hazard scenarios, providing valuable information for resilient transportation construction.
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