Resource-Constrained Emergency Scheduling for Forest Fires via Artificial Bee Colony and Variable Neighborhood Search Combined Algorithm

Limited resources are a prevalent and challenging problem in the field of emergency management. Emergency scheduling is an effective way to make full use of resources. However, designing an effective emergency plan to minimize rescue time is a major challenge. This study focuses on large-scale emergency scheduling for fighting forest fires with multiple rescue centers (depots) and limited fire-fighting resources, which aims to determine the optimal rescue route of fire-fighting teams at multiple rescue centers to minimize the total completion time of all fire-fighting tasks. For this problem, we first assign rescue priorities to different fire points according to the speed of the fire spread. Then, we formulate it into a mixed-integer linear programming (MILP) model and analyze its NP-hard complexity. To deal with large-scale problems, a new fast and effective artificial bee colony algorithm and variable neighborhood search combined algorithm is proposed. Extensive experimental results for large-scale randomly generated instances confirm the favorable performance of the proposed algorithm by comparing it with MILP solver CPLEX, genetic algorithms, and particle swarm optimization algorithms. We also derive some management insights to support emergency management decision-making.