Decision-making model to generate novel emergency response plans for improving coordination during large-scale emergencies

摘要

Developing joint emergency response plans is an effective method to coordinate multi-agency response endeavors. This study presents a novel emergency response plan structure that considers emergency command operation requirements, such as explicitly expressing the incident objective decomposition structure, formalizing decisions in a context-sensitive manner, supporting the synchronization of responding activities with variable interval, encoding complex temporal constraints, and providing temporal flexibility. A decision-making model is developed to generate these domain-specific action plans automatically based on integrating hierarchical task network (HTN) planning and scheduling technologies. This model presents several valuable contributions to existing state-based forwarding HTN planning paradigms. First, an enhanced HTN is designed to record traversed HTN exploration space for constructing of incident objective decomposition structure and decision-making contexts. Second, the model generates temporal flexible action plans that enable the handling of temporal uncertainty in the emergency response domain. A novel concurrency controlling mechanism to ensure the parallelism of response activities with variable intervals is also proposed based on the temporally enhanced planning state that represents a dynamic emergency situation. Finally, the proposed model explicitly represents the starting and ending time of all tasks in the task network to provide complete temporal flexibility. In particular, a dedicated temporal management method taking full advantage of the decomposition structure induced by the HTN planning process is proposed for propagating time constraints on the underling Simple Temporal Network (STN) incrementally. An empirical study on typhoon evacuation demonstrates that the presented model is suitable for solving real-world problems. Therefore, the decision-making model can be applied as a computational model for the development process of emergency response plans, and will be embedded as a reasoning logic in an emergency command decision support system.