A Conflict-Based Path-Generation Heuristic for Evacuation Planning
This work addresses evacuation planning for disaster management, offering a practical improvement for handling massive scenarios like floods, though it is incremental in optimizing existing methods.
The paper tackles the problem of evacuation planning for large-scale disasters by proposing a conflict-based path-generation heuristic, which reduces the number of variables from 4,500,000 to 30,000 in a case study involving 70,000 people, enabling near-optimal real-time solutions.
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.