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基于改进非支配排序遗传算法 的维修资源优化配置
引用本文:王文双,赵建印,赵建忠,张鑫.基于改进非支配排序遗传算法 的维修资源优化配置[J].海军航空工程学院学报,2017,32(1):121-128, 166.
作者姓名:王文双  赵建印  赵建忠  张鑫
作者单位:海军航空工程学院科研部,山东烟台264001,海军航空工程学院兵器科学与技术系,山东烟台264001,海军航空工程学院兵器科学与技术系,山东烟台264001,海军航空工程学院兵器科学与技术系,山东烟台264001
摘    要:针对在武器装备维修保障过程中出现的资源短缺、资源冲突等问题,构建了多目标的维修资源优化配置模型,并且基于加权思想将多目标转化为单目标以方便求解。针对遗传算法求解多目标优化问题存在的解空间过大、收敛速度慢、计算效率低等问题,提出了基于约束的改进非支配排序遗传算法对资源优化配置模型进行求解。实例分析结果验证了模型及算法的可行性和有效性。

关 键 词:武器装备  维修资源  资源配置  多目标进化算法  非支配排序遗传算法

Optimal Configuration of Maintenance Resource Configuration Based on Improved Non-Dominated Sorting Genetic Algorithm
WANG Wenshuang,ZHAO Jianyin,ZHAO Jianzhong and ZHANG Xin.Optimal Configuration of Maintenance Resource Configuration Based on Improved Non-Dominated Sorting Genetic Algorithm[J].Journal of Naval Aeronautical Engineering Institute,2017,32(1):121-128, 166.
Authors:WANG Wenshuang  ZHAO Jianyin  ZHAO Jianzhong and ZHANG Xin
Institution:Naval Aeronautical and Astronautical University Department of Scientific Research,Naval Aeronautical and Astronautical UniversityDepartment of Ordnance Science and Technology, Yantai Shandong 264001, China,Naval Aeronautical and Astronautical UniversityDepartment of Ordnance Science and Technology, Yantai Shandong 264001, China and Naval Aeronautical and Astronautical UniversityDepartment of Ordnance Science and Technology, Yantai Shandong 264001, China
Abstract:Aiming at the problems of resource conflict, shortage, resource requirement and available resource diversifica? tion in the maintenance support process, the multi-objective optimization models of resource static and dynamic configura? tion were built. To address the disadvantages of genetic algorithm, such as the large solution domain, slower convergence speed, and low computing efficiency, the constraint-based improved non-dominated sorting genetic algorithm (CINSGA-I) was raised. The example analysis results validated the effectiveness and superiority of the model and algorithm.
Keywords:weapon equipment  maintenance resource  resource configuration  multi-objective evolutionary algorithm  
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