Data driven multi-objective optimization of sustainable aviation emergency network for forest fire rescue

Forest fires occur abruptly and can be detrimental, posing significant threats to human safety, forest resources, and the overall environment. Timely detection and effective response are important to fight against forest fires. Aviation emergency rescue plays an increasingly important role in forest fire response due to the characteristics of fast response speed, low terrain requirements and less fire site restrictions. At present, forest fire aviation emergency has been paid more attention in the world, however, current researches provide limited support to actual situation due to the lack of systematization and pertinently. In this paper, based on remote sensing information and other multi-party data, a two-stage multi-objective stochastic optimization model of sustainable aviation emergency network for forest fire rescue is presented. The proposed model aims to minimize the maximum of effective operational distance for aerial emergency rescue efforts and the total cost, which includes ecological losses caused by forest fires. To solve the model, an algorithm incorporating the NSGA-II algorithm and SAA method is proposed. Further, a case of Hainan Province in China is studied, guiding the application of the proposed theoretical methods. The findings demonstrate considerable value in addressing forest fires and safeguarding forest resources, thereby contributing to the sustainable development of both the environment and society.