GA-LNS Optimization for Helicopter Rescue Dispatch

Abstract

Aviation emergency rescue has become one of the most effective means for natural disaster relief due to its flexible and timely characteristics. A reasonable emergency dispatch plan can guarantee the effective implementation of all the rescue measures. Most of previous studies in this area focused on the scheduling and routing but ignored the impact of the specific rescue process, for example the fuel consumption of various helicopters. In this paper, a multi-helicopter-multi-trip Aviation Rescue Routing Problem (ARRP) is analysed which covers the whole rescue process. In addition, a time-domain procedural simulation model is built which can consider different helicopters, refueling or not, various resource locations, multiple disaster sites and other operation factors. Based on that, a Genetic Algorithm (GA) hybridized Large Neighborhood Search (LNS) algorithm (GA-LNS) is proposed for optimization. In ARRP, single search algorithm may lead to the local optimum due to complexity. In contrast, the distance greedy strategy and the load ratio strategy are combined in GA-LNS which can fix the local optimum problem. More specifically, based on the helicopter-tagged-task-sequenced chromosome, the single-point crossover operator is used in GA and then, the worst removal strategy and the first/last insertion strategy are adopted in LNS. Finally, the numerical experiments are exercised to verify the effectiveness of the proposed GA-LNS algorithm which is compared with three traditional basic heuristic algorithms and a stateof-the-art memetic algorithm.