Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains

作     者：Zhang, Jie He, Junnan Ren, Shihao Zhou, Pei Guo, Jun Song, Mingyue 

作者机构：Inner Mongolia Agr Univ Coll Energy & Transportat Engn Hohhot 010010 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430070 Peoples R China Inner Mongolia Agr Univ Coll Forestry Hohhot 010010 Peoples R China 

出 版 物：《SCIENTIFIC REPORTS》 (Sci. Rep.)

年 卷 期：2025年第15卷第1期

页      面：1-15页

核心收录：

基　　金：the Key R&D and Achievement Transformation Plan Projects of Inner Mongolia Autonomous Region [BR230146] Basic scientific research projects of universities directly under Inner Mongolia Autonomous Region [2022YFSH0129] Key R&D and Achievement Transformation Plan Projects of Inner Mongolia Autonomous Region 

主　　题：Emergency relief Improved genetic algorithms Vehicle scheduling Chaos Search 

摘      要：In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm s solution efficiency and accuracy, through the northern pristine forest area of Daxing anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. It enriches the solution method of forest fire emergency rescue vehicle dispatching problem in Great Khingan area, which is of great significance to improve the emergency rescue capability in case of sudden forest fire. Through simulation experiments, the proposed Improved Genetic Algorithm (IGA) achieved an average rescue time reduction of 8.5% compared to conventional Genetic Algorithm (GA) and 3.5% compared to Improved Artificial Bee Colony (IABC) algorithm, with an average solution time of 9.4 ms.