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兵工学报 ›› 2023, Vol. 44 ›› Issue (5): 1431-1442.doi: 10.12382/bgxb.2022.0007

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陆军战术级作战任务分配及优化方法

杜伟伟1,2, 陈小伟3,4,*()   

  1. 1 北京理工大学 机电学院, 北京 100081
    2 北方自动控制技术研究所, 山西 太原 030006
    3 北京理工大学 爆炸科学与技术国家重点实验室, 北京 100081
    4 北京理工大学 前沿交叉科学研究院, 北京 100081

Task Assignment and Optimization Method of Tactical-Level Army Operations

DU Weiwei1,2, CHEN Xiaowei3,4,*()   

  1. 1 School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
    2 North Automatic Control Technology Institute, Taiyuan 030006, Shanxi, China
    3 State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
    4 Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
  • Received:2022-01-04 Online:2022-06-01

摘要:

对作战力量的合理使用是作战任务优化分配问题的难点,相关研究较少,为此提出一种基于多维度建制力量拆分与使用的作战任务分配方法。对作战单元力量进行规范化描述,并对建制力量的复合作战能力进行模糊化表示。对作战任务进行资源需求分析,计算完成任务所需作战资源的可行方案,建立从作战资源到建制力量的映射关系,生成初步方案。对作战任务建制力量分配问题考虑单元力量、编制编成以及力量使用方法等因素计算复合力量的综合战斗力指数。建立多约束条件和多优化目标的数学模型,将任务聚合规则和建制拆分原则转化为可计算代价的惩罚项对目标函数进行正则化,并引入改进的遗传算法对模型进行迭代优化,生成最终的任务分配方案。仿真验证结果表明,该方法能够实现作战任务与建制力量的优化匹配,在合成旅渡海登岛典型作战场景中,相比基于传统列表规划的组合匹配方法明显提高了作战力量利用率。

关键词: 任务规划, 作战任务分配, 多目标优化, 模糊逻辑, 遗传算法

Abstract:

There has been a large number of studies on combat task assignment. However, only a few of them have focused on army force efficiency. This work attempts to address the problem of general combat task assignment from the standpoint of army force utilization. First, the army force is described using single units and compound units in a standardized manner. Second, resource demand analysis is performed and potential resources needed for each task are listed. A mapping relationship is established between the resource and the army force. Then, we formulate the task combination problem and the force dividing code to computational functions mathematically, and introduce the multi-objective optimization method and genetic algorithm to find the optimum task assignment solution. The method is validated using a landing battle example, and the experimental results demonstrate the effectiveness and efficiency of the proposed method.

Key words: mission planning, operational task assignment, multi-objective optimization, fuzzy logic, genetic algorithm