Abstract

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The previous studies aimed to develop a dynamic agent-based simulation model in post-earthquake emergency response operations combining rescuers and victims using geospatial information systems and multi-agent systems (GISs and MASs, respectively). We use multi-agent system engineering (MaSE) to construct this model and cluster points are proposed to assemble rescuers and victims. We also proposed an approach for dynamic task allocation and for establishing collaboration among agents based on contract net protocol (CNP) and the [Formula: see text]-means clustering method. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and expert evaluation is used to sort the priorities of cluster points. The experimental background is set in the central area of Chengdu, Sichuan Province, China. 200 victims with different demands and 30 rescuers with the same rescue capability are randomly distributed in this area. We use different victim speeds to simulate different degrees of seismic damage. Compared with normal search and rescue, the ERSCIED can make some use of the capabilities of the victims for the improvement of emergency effectiveness when the victims and rescuers have a similar speed (1:1) or the victims are not much slower than the rescuers (1:2). When the victims are much slower than the rescuers (1:10), the rescuers have to wait for a long time at the cluster points and it will lead to a waste of resources. The parameters of rescuers and victims can be changed to meet different emergencies and the ERSCIED can provide effective auxiliary decision-making information for decision-makers.

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