任务主体二元约束下作战任务分解EVA方法

doi:10.12305/j.issn.1001-506X.2022.07.16

摘要/Abstract

摘要：

针对复杂作战任务分解中存在的随意性、不确定性问题, 综合考虑任务主体能力属性和结构特征等二元约束, 提出了一种由子任务集提取(extraction, E)、约束检验(verification, V)、子任务集调整(adjustment, A)等步骤递进循环形成的任务分解EVA方法。首先, 构建了全局任务空间, 提出基于任务匹配的子任务集提取方法; 其次, 针对任务主体能力属性和结构特征的二元约束, 建立了子任务集调整模型, 通过改进精英保留策略, 引入任务分解粒度和交叉变异概率动态调整策略, 提出了一种引进的非支配排序遗传算法-Ⅱ(improved non-dominated sorting genetic algorithm-Ⅱ, INSGA-Ⅱ)算法; 最后, 进行仿真实验, 验证了算法相较于传统多目标优化算法在解集多样性、收敛性和时间性能上的优势。研究结果表明, 所提方法能够使决策者依据任务主体实际自主调控任务分解结果, 在一定程度上克服了传统方法过度依赖主观经验, 忽略任务主体能力属性、结构特征约束的问题。

关键词: 作战任务分解, 任务主体, 二元约束, 任务提取-约束检验-子任务集调整方法, 多目标优化, 任务分解

Abstract:

Aiming at the arbitrariness and uncertainty in the decomposition of complex combat tasks, a extraction-verification-adjustment (EVA) task decomposition method is proposed, which is formed by the extraction, verification, and adjustment of the sub task set, considering the binary constraints such as the capability attributes and structural characteristics of the task subject. First, the global task space is constructed, and the subtask set extraction method based on task matching is proposed. Secondly, according to the binary constraints of the task subject's ability attributes and structural characteristics, a quantitative adjustment model of subtask sets is established. By improving the elitist retention strategy and introducing task decomposition granularity and the dynamic adjustment strategy of crossover mutation probability, the improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) algorithm is proposed. Finally, the simulation results verify that INSGA-Ⅱ has the advantages of diversity, convergence in solution set and timeliness performance compared with the traditional optimization algorithms. The results show that the method proposed in this paper enable decision makers to adjust and control the task decomposition results according to the actual situation of the task subject, and overcome the problem that the traditional methods rely on subjective experience and ignore the constraints of the ability attributes and structural characteristics of the task subject.

Key words: combat task decomposition, task subject, binary constraints, extraction-verification-adjustment (EVA) method, multi-objective optimization, task descomposition

中图分类号:

E917

刘乾, 鲁云军, 陈克斌, 韩梦瑶, 郭亮. 任务主体二元约束下作战任务分解EVA方法[J]. 系统工程与电子技术, 2022, 44(7): 2201-2210.

Qian LIU, Yunjun LU, Kebin CHEN, Mengyao HAN, Liang GUO. Combat task decomposition EVA method based on binary constraints of task subject[J]. Systems Engineering and Electronics, 2022, 44(7): 2201-2210.

图/表 14

图1

图2

图3

表1

表2

表3

表4

表5

表6

表7

表8

图4

表9

图5

参考文献 31

1	曹裕华, 冯书兴, 徐雪峰. 作战任务分解的概念表示方法研究[J]. 计算机仿真, 2007, 24 (8): 1- 4. doi: 10.3969/j.issn.1006-9348.2007.08.001
	CAO Y H , FENG S X , XU X F . Research on conception representation of operational mission break[J]. Computer Simulation, 2007, 24 (8): 1- 4. doi: 10.3969/j.issn.1006-9348.2007.08.001
2	GAELE S , BRUNO M , DOMINIQUE F . Goal decomposition tree: an agent model to generate a validated agent behavior[J]. Lecture Notes in Computer Science, 2006, 39 (4): 124- 140.
3	张维明, 刘忠, 阳东升, 等. 体系工程理论与方法[M]. 北京: 科学出版社, 2010.
	ZHANG W M , LIU Z , YANG D S , et al. System engineering theory and method[M]. Beijing: Science Press, 2010.
4	董涛, 刘付显, 李响. 内聚度和粒度在作战任务分解评估中的应用[J]. 电光与控制, 2012, 19 (12): 14- 17.
	DONG T , LIU F X , LI X . Cohesion and granularity applied in task decomposition evaluation for operations[J]. Electronics Optics & Control, 2012, 19 (12): 14- 17.
5	PANG Z L , WANG G Y , YANG J . A multi-granularity decomposition mechanism of complex tasks based on density peaks[J]. Big Data Mining and Analytics, 2018, 1 (2): 245- 256.
6	LIU A J , PFUND M , FOWLER J . Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition[J]. Journal of Systems Engineering and Electronics, 2016, 27 (2): 422- 433. doi: 10.1109/JSEE.2016.00043
7	ZOU Z G , CHEN W F , WANG S S , et al. Role-based approaches for operational tasks modeling and flexible decomposition[J]. Journal of Systems Engineering and Electronics, 2016, 26 (6): 1191- 1206.
8	KARNIEL A , REICH Y . Formalizing a workflow-net implementation of design structure matrix based process planning for new product development[J]. IEEE Trans.on Systems, Man and Cybernetics-Part A: Systems and Humans, 2011, 47 (3): 476- 491.
9	YANG Q , YAO T , LU T , et al. An overlapping-based design structure matrix for measuring interaction strength and clustering analysis in product development project[J]. IEEE Trans.on Engineering Managemen, 2014, 6 (1): 159- 170.
10	SON S , KIM J H , LEE J W , et al. Improving supply chain management process using design structure matrix based cross-functional analysis[J]. Systems Engineering, 2019, 22 (4): 313- 329. doi: 10.1002/sys.21484
11	RAMEZANIA R , SEDAGHAT Y . A decomposition-based reliability and makespan optimization technique for hardware task graphs[J]. Reliability Engineering and System Safety, 2018, 180, 13- 24. doi: 10.1016/j.ress.2018.07.007
12	LIU W J, JI J, YANG Y M. Capability-based design task decomposition in heavy military vehicle collaborative development process[C]//Proc. of the 28th International Academy for Production Engineering Design Conference, 2018: 13-18.
13	VILLOTA W , GIRONZA M , ORDONEZ A . On the feasibility of using hierarchical task networks and network functions virtualization for managing software-defined networks[J]. IEEE Access, 2018, 6, 38026- 38040. doi: 10.1109/ACCESS.2018.2852649
14	WISNIEWSKIA R , KARATKEVICH A . Decomposition of distributed edge systems based on the Petri nets and linear algebra technique[J]. Journal of Systems Architecture, 2019, 96, 20- 31. doi: 10.1016/j.sysarc.2019.01.015
15	CHITTILAPPILLY A I , CHEN L , AMER-UAHIA S . A survey of general-purpose crowdsourcing techniques[J]. IEEE Trans.on Knowledge and Data Engineering, 2016, 28 (9): 2246- 2266. doi: 10.1109/TKDE.2016.2555805
16	WATANABE C , HIRAMATSU K , KASHINO K . Knowledge discovery from layered neural networks based on non-negative task matrix decomposition[J]. IEICE Trans.on Information and Systems, 2020, E103D (2): 390- 397.
17	RYAN K L , LEE E W , LEE S G . Business-OWL (BOWL)-a hierarchical task network ontology for dynamic business process decomposition and formulation[J]. IEEE Trans.on Services Computing, 2012, 5 (2): 246- 259. doi: 10.1109/TSC.2011.48
18	QI C , WANG D . Dynamic aircraft carrier flight deck task planning based on HTN[J]. IFAC-Papers Online, 2016, 49 (12): 1608- 1613. doi: 10.1016/j.ifacol.2016.07.810
19	邵天浩, 张宏军, 程恺, 等. 层次任务网络中的重新规划研究综述[J]. 系统工程与电子技术, 2020, 42 (12): 2833- 2846. doi: 10.3969/j.issn.1001-506X.2020.12.21
	SHAO T H , ZHANG H J , CHENG K , et al. Review of replanning in hierarchical task network[J]. Systems Engineering and Electronics, 2020, 42 (12): 2833- 2846. doi: 10.3969/j.issn.1001-506X.2020.12.21
20	SHAO T H , ZHANG H J , CHENG K , et al. The hierarchical task network planning method based on Monte Carlo tree search[J]. Knowledge-Based Systems, 2021, 225, 107067. doi: 10.1016/j.knosys.2021.107067
21	余加振. 基于OOR框架的作战任务分析方法研究[D]. 长沙: 国防科学技术大学, 2010.
	YU J Z. Reseach on approach of operational task analysis based on OOR framework[D]. Changsha: National University of Defense Technology, 2010.
22	XU X , JU R S , LIU X C , et al. Extending HTN to planning and execution control for small combat unit simulation[J]. International Journal of Modeling, Simulation, and Scientific Computing, 2017, 8 (2): 1750032. doi: 10.1142/S1793962317500325
23	LEE S M. Hierarchical planning knowledge for refining partial-order plans[D]. Bethlehem: Lehigh University, 2012.
24	U.S. Joint Chiefs of Staff. Universal joint task Manual[R]. Virginia Arlington: Chairman of Joint Chiefs of Staff, 2011: B-A-1.
25	ASHYAP N , KUMARI A C , CHHIKARA R . Multiobjective optimization using NSGA-Ⅱ for service composition in IoT[J]. Procedia Computer Science, 2020, 167, 1928- 1933. doi: 10.1016/j.procs.2020.03.214
26	XU X Q , YANG K W , DOU Y J , et al. High-end equipment development task decomposition and scheme selection method[J]. Journal of Systems Engineering and Electronics, 2021, 32 (1): 118- 135. doi: 10.23919/JSEE.2021.000012
27	ZHANG L , GE H J , MA Y , et al. Multi-objective optimization design of a notch filter based on improved NSGA-Ⅱ for conducted emissions[J]. IEEE Access, 2020, 8, 83213- 83223. doi: 10.1109/ACCESS.2020.2991576
28	SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.
29	CHEN K B , LU Y J , LIU Q , et al. A method to validate operational capability index model of heterogeneous combat networks based on characteristic topology analysis[J]. IEEE Access, 2020, 8, 59760- 59773. doi: 10.1109/ACCESS.2020.2983082
30	夏博远, 杨克巍, 杨志伟, 等. 基于杀伤网评估的装备组合多目标优化[J]. 系统工程与电子技术, 2021, 43 (2): 399- 409.
	XIA B Y , YANG K W , YANG Z W , et al. Multi-objective optimization of equipment portfolio based on kill-web evaluation[J]. Systems Engineering and Electronics, 2021, 43 (2): 399- 409.
31	LI J, CHEN J, XIN B, et al. Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-Ⅱ and adaptive MOEA/D: a comparison study[C]//Proc. of the IEEE Congress on Evolutionary Computation, 2015: 3132-3139.

染色体编码	子任务编号	染色体编码	子任务编号
T₁	1	T₅	3
T₂	1	T₆	3
T₃	2	T₇	2
T₄	1	T₈	1

任务T	T₁	T₂	T₃	T₄	T₅	T₆	T₇	T₈	T₉	T₁₀	T₁₁	T₁₂	T₁₃
T₁	0	0.233 1	0	0.423 8	0	0	0	0	0	0	0	0	0
T₂	0.233 1	0	0.287 7	0	0	0	0	0	0	0	0	0	0
T₃	0	0.287 7	0	0	0.611 2	0.643 6	0	0	0	0	0	0	0
T₄	0.423 8	0	0	0	0	0	0	0	0	0.701 7	0.685 4	0	0
T₅	0	0	0.611 2	0	0	0	0.621 6	0	0	0	0	0	0
T₆	0	0	0.643 6	0	0	0	0.725 9	0	0	0	0	0	0
T₇	0	0	0	0	0.621 6	0.725 9	0	0.340 9	0	0	0	0	0
T₈	0	0	0	0	0	0	0.340 9	0	0.823 3	0	0	0	0
T₉	0	0	0	0	0	0	0	0.823 3	0	0	0	0	0
T₁₀	0	0	0	0.701 7	0	0	0	0	0	0	0	0.428 5	0
T₁₁	0	0	0	0.685 4	0	0	0	0	0	0	0	0.470 2	0
T₁₂	0	0	0	0	0	0	0	0	0	0.428 5	0.470 2	0	0.852 8
T₁₃	0	0	0	0	0	0	0	0	0	0	0	0.852 8	0

任务T	T₁₄	T₁₅	T₁₆	T₁₇	T₁₈	T₁₉
T₁₄	0	0.110 3	0.049 3	0.955 3	0.048 7	0
T₁₅	0.110 3	0	0.593 0	0.237 0	1	0.548 6
T₁₆	0.049 3	0.593 0	0	0.160 8	0.593 9	0.875 7
T₁₇	0.955 3	0.237 0	0.160 8	0	0.160 2	0.101 4
T₁₈	0.048 7	1	0.593 9	0.160 2	0	0.601 4
T₁₉	0	0.548 6	0.875 7	0.101 4	0.601 4	0

能力C	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉	C₁₀
T₁	0	0	0	0	0	0	0	10	0	0
T₂	0	0	0	10	13	10	6	0	0	0
T₃	0	0	0	11	15	13	0	0	0	0
T₄ T₁₀	0	0	0	20	14	12	0	0	0	0
T₅	0	1	0	0	0	0	0	5	0	0
T₆ T₇	0	0	0	8	6	10	0	4	0	10
T₈ T₉	0	0	0	0	0	8	0	0	10	6
T₁₁	0	0	0	0	0	0	0	5	0	0
T₁₂ T₁₃	0	0	0	0	0	10	0	0	0	6
T₁₄ T₁₇	10	0	0	0	0	0	0	0	0	0
T₁₅ T₁₈	0	6	0	0	0	0	0	0	0	0
T₁₆ T₁₉	0	0	20	0	0	0	0	0	0	0

量化调整结果	SD_inner	SD_inter
{1, 2, 2}	0.769 8	0.205 3