Large-Scale Helicopter Medical Rescue Task Allocation Method Based on Improved NSGA-II Algorithm

Helicopter-based medical rescue can provide quick response and a wide coverage area; these two points are important in large-scale disaster rescue operations. Scientific helicopter scheduling affects both rescue efficiency and operating cost. To address the helicopter scheduling problems under large-scale disaster scenarios for aerial medical rescue, this paper studies the process and features of aerial medical rescue and establishes a helicopter scheduling model. The goal is to minimize both the time taken for the mission and the amount of money spent on it, and at the same time, make sure that everyone who needs help gets rescued. To solve the shortcomings of the traditional NSGA-II algorithm, such as being prone to falling into local optimum and low efficiency, a feasibility-first NSGA-II algorithm is proposed to assist with helicopter scheduling. This approach uses Latin Hypercube Sampling (LHS) for initialization to improve its global search capability and performs a feasibility check before sorting to reduce redundancy in the solution space and improve the optimality of the outcomes and the speed of computation. The simulated experiment shows that the proposed algorithm has higher efficiency, stability, and better performance than the traditional heuristic algorithm.