An Event-based Optimization Method for Building Evacuation with Queuing Network Model

This paper presents an online evacuation policy optimization framework for escape movement of large populations through geometrically complex building spaces with active guides. Considering the uncertainty of the evacuation process, a queuing network model is introduced to formulate the evacuation problem. Evacuation policy is expressed as an online updating guide to the evacuation direction of people in each node. Then an event-based optimization method is used to improve the evacuation policy, in which actions are taken only when local congestion statuses are changed, under reinforcement learning in partially observable environments. Numerical results show that this method is effective in improving the efficiency of evacuation.