Determining optimum staged-evacuation schedule considering total evacuation time, congestion severity and fire threats.

• Introducing a model to automatically design optimized staged-evacuation plan. • Total Evacuation Time, fire exposure risk for evacuees, and congestion severity are considered to evaluate the scenarios. • GA used to search for the optimized departure time and SFM utilized to simulate the suggested staged-evacuation scenarios. • A parallel computing technique is hired to handle high number of SFM simulations. • Results show that the congestion severity was considerably mitigated. For many years, simultaneous evacuation of the entire facility was a common approach. The deficiencies of this approach were revealed after many cases of casualties and evacuation efficiency drop that were observed during some evacuations caused by crowd congestion. To tackle this issue, staged-evacuation process (also called phased-evacuation), which was mainly aimed to mitigate the congestions, was introduced. In this study, a bi-level framework consisted of a pedestrian simulation model (Social Force Model) and a metaheuristic optimization model (GA) was developed to automatically determine the optimum schedule for a staged-evacuation process. In this platform, metaheuristic algorithm iteratively searches for a dominant evacuation schedule, and SFM simulates the evacuees' attributes and evaluates the safety of the evacuation scenario which was proposed by the metaheuristic algorithm. Total Evacuation Time (TET), fire exposure threat, and congestion severity were also considered as the safety objectives which were tried to be minimized. An opportunity was also provided to cover the effects of many pedestrians' exceptional behaviors by executing a high number of microscopic simulations during the metaheuristic iterations. Moreover, more reliable input materials were provided by the microscopic simulator for the optimization algorithm. In this regard, executing multiple simulations in every metaheuristic search demands significant computation efforts. To overcome this challenge, a parallel computing technique was hired which resulted in a considerable reduction in the computation time. Finally, an MCDM method was employed to select the best-satisfying evacuation scenario among the Pareto-frontier solutions. Implementing the proposed framework in a case study enabled us to evacuate the examined facility based on the achieved optimized schedule so that the congestion severity tremendously mitigated while fire exposure threat and TET remained compromisingly at their lowest. [ABSTRACT FROM AUTHOR]