Abstract:Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints. Existing algorithms almost always allow for preemptive evacuation schedules, which are less desirable in practice. This paper proposes, for the first time, a constraint-based scheduling model that optimizes the evacuation flow rate (number of vehicles sent at regular time intervals) and evacuation phasing of widely populated areas, while ensuring a nonpreemptive evacuation for each residential zone. Two optimization objectives are considered: (1) to maximize the number of evacuees reaching safety and (2) to minimize the overall duration of the evacuation. Preliminary results on a set of real-world instances show that the approach can produce, within a few seconds, a non-preemptive evacuation schedule which is either optimal or at most 6% away of the optimal preemptive solution.
Abstract:Evacuation planning and scheduling is a critical aspect of disaster management and national security applications. This paper proposes a conflict-based path-generation approach for evacuation planning. Its key idea is to generate evacuation routes lazily for evacuated areas and to optimize the evacuation over these routes in a master problem. Each new path is generated to remedy conflicts in the evacuation and adds new columns and a new row in the master problem. The algorithm is applied to massive flood scenarios in the Hawkesbury-Nepean river (West Sydney, Australia) which require evacuating in the order of 70,000 persons. The proposed approach reduces the number of variables from 4,500,000 in a Mixed Integer Programming (MIP) formulation to 30,000 in the case study. With this approach, realistic evacuations scenarios can be solved near-optimally in real time, supporting both evacuation planning in strategic, tactical, and operational environments.