不确定使命环境下C2组织结构动态适应性优化方法研究
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摘要
信息化战争的高度复杂性与动态不确定性,决定了战争中组织整体对抗优势的获取与维持必须建立在敏捷的指挥控制(Command and Control, C2)组织之上。敏捷的C2组织要求组织具有良好的适应性,如何根据使命环境中动态到达的不确定事件对C2组织能力的影响,适时的对当前组织结构进行适应性优化调整,是C2组织研究的关键问题之一。现有的组织结构适应性优化方法普遍选取整个使命执行期间作为优化时域,并且大多是采用组织重构的方法进行适应性优化,效果并不理想,关于C2组织结构的适应性优化问题还需要进一步深入研究。
论文的研究集中在不确定使命环境下C2组织能力的测度分析,C2组织结构的适应性优化模型以及动态适应性优化方法等方面,做了以下几个方面的工作:
1、提出了C2组织能力测度。完善了关于C2组织的组织元素和组织结构的定义和描述。特别的,为了度量使命的决策能力需求以及使命环境中不确定因素对组织决策能力的影响,考虑了任务和决策者的决策能力属性。定义了C2组织为有效完成使命所提供的决策能力和资源能力,并提出了相应的测度参数,分别给出了它们的定义和描述。分析了C2组织所面向的使命环境中各种不确定因素的类型、来源及其对组织的影响,以及C2组织结构对组织能力的优化作用,建立了组织结构变量与组织能力之间的优化关系。
2、提出了C2组织结构的分层动态适应性优化方法。基于不确定使命环境下C2组织的能力分析,以组织能力测度最大化为目标构建了C2组织结构适应性优化(C2 Organization Structure Adaptive Optimization, COSAO)模型。针对COSAO模型中结构变量与能力测度具有复杂关联性的特点,提出了基于组织结构分层的适应性优化策略,降低了求解C2组织结构适应性优化模型时的复杂性。针对COSAO模型具有的动态不确定性特点,通过引入滚动时域的思想,提出了基于滚动时域的动态适应性优化策略,采用时域分解原理,通过灵活触发的多次较短时域的优化,降低计算的复杂度并适应所面向环境的不确定性变化。综合两种适应性优化策略,提出了C2组织结构的分层动态适应性优化方法SLDAO,降低了求解COSAO模型时面临的动态不确定性和复杂关联性。
3、提出了基于滚动时域的C2组织决策层结构动态适应性优化方法。建立了C2组织决策能力测度的数学模型,从完成决策工作的量和质两个角度反映了使命决策能力需求的满足程度。讨论了影响决策能力的决策执行能力损耗事件和任务决策负载强度变化事件,定义了不确定事件的参数。在组织决策能力分析的基础上构建了决策层结构适应性优化模型。针对决策层结构适应性优化模型具有的动态不确定性特点,提出了基于滚动时域的决策层结构动态适应性优化方法DLSDAO-RHP,设计了预测窗口、滚动窗口、优化子问题以及滚动机制等滚动时域策略要素。该方法能够根据使命环境的不确定性程度来调整优化时域的长度,将原问题分解为较短时域内多个优化子问题,降低了问题求解时所面临的不确定性。针对优化时域内的优化子问题,提出了嵌套改进模拟退火求解算法NISA。案例分析和对比实验表明,DLSDAO-RHP方法能够通过多次较短时域内的子问题优化提升不确定使命环境下组织的决策能力。
4、提出了基于关键事件的C2组织资源层结构动态适应性优化方法。建立了C2组织资源能力测度的数学模型,从完成任务的量和质两个角度反映了使命资源能力需求的满足程度。讨论了影响资源能力的平台损耗事件、任务新增或取消事件以及任务处理时间变化事件,定义了不确定事件的参数。在组织资源能力分析的基础上构建了组织资源层结构适应性优化模型。针对组织资源层结构适应性优化模型具有的动态不确定性特点,提出了基于关键事件的C2组织资源层结构两阶段动态适应性优化方法TAOBKE。该方法通过不确定事件对组织资源能力的影响及时触发资源层结构适应性优化。案例分析和对比实验表明,在不确定使命环境下TAOBKE方法能够有效的提升组织的资源能力。
5、设计了分析C2组织结构动态适应性优化的综合案例。以一次多军兵种联合作战的登陆战役为例,综合讨论决策能力影响事件和资源能力影响事件对C2组织的影响,对本文所提出的C2组织结构分层动态适应性优化方法SLDAO进行了验证。案例分析说明了不确定使命环境下组织结构动态适应性调整的必要性和SLDAO方法的良好性能,通过对C2组织结构的有效分层,有效减少了组织重构,保证了C2组织结构的稳定性;随着不确定事件的增多,SLDAO方法能够控制组织结构调整代价的增长,因此面对不确定事件频繁发生的情况,SLDAO方法具有明显的优势。
The highly complexity and dynamic uncertainty of information warfare requires agile Command and Control (C2) organization to acquire and maintain the overall confrontation superiority. The agile C2 organization is characterized by well adaptability. How to timely carry out the adaptive optimization on the current organization structure, according to the effect of dynamic arriving uncertainty events on the C2 organization capabilities under mission environment, comes into being one of the primary problems in the research of C2 organization. The existing methods of organization structure adaptive optimization generally select the whole mission execution period as optimization horizon, and mostly adopt organization reconfiguration method, the effect of which is not ideal.The problem of C2 organization structure adaptive optimization still needs further research.
Focusing on the capability measurement of C2 organization under uncertainty mission environment, adaptive optimization model of C2 organization structure and dynamic adaptive optimization method, this paper makes the following contributions:
1. C2 organization capabilities measurement is proposed. The definition and description of C2 organization elements and organization structure are improved, and particularly, in order to measure decision capabilities requirement of mission and effect of uncertainty factors on the decision capabilities, the decision capability attributes of task and decision-makers are considered. The decision capabilities and resource capabilities provided by C2 organization to complete mission effectively are defined, the corresponding measurement parameters of which are proposed respectively, and then the definition and description of measurement parameters are given. The category, source of uncertainty factors and theirs influence on organization under uncertainty mission environment, as well as optimizing function of organization structure to organization capabilities are analyzed, optimal relation between organization structure variables and organization capabilities is established.
2. Layered dynamic adaptive optimization method of C2 organization structure is proposed. The C2 organization structure adaptive optimization (COSAO) model to maximize organization capability is built based on the analysis of C2 organization capabilities under uncertainty mission environment. In view of complex association between structure variables and capabilities measurement, adaptive optimization strategy based on layered organization structure is proposed, which reduces the complexity of COSAO model solution. In view of dynamic uncertainty of COSAO model, through introducing rolling horizon theory, the dynamic adaptive optimization strategy based on rolling horizon is presented. Using horizon decomposition principle and optimization of multiple shorter horizons that triggered flexibly, this strategy is computationally efficient while adapting to the uncertainty change of environment. Combined with two adaptive optimization stategies, the layered dynamic adaptive optimization method of C2 organization structure (SLDAO) is proposed, which reduces dynamic uncertainty and complex association during COSAO model solution.
3. Dynamic adaptive optimization of organization decision-layer structure based on rolling horizon procedure (RHP) is proposed. The mathematical model of measurement parameters for C2 organization decision capabilities is established, which reflects the satisfaction degree of mission decision capability requirements from the perspective of quantity and quality of completed task. The events of decision implementation capability loss and intensity change of task decision-load which influence on the decision capability are discussed. The uncertainty events parameters are defined. Based on the analysis of organization decision capability, the adaptive optimization model of decision layer structure is built, according to the dynamic uncertainty of which, the adaptive optimization method of decision-layer structure based on RHP (DLSDAO-RHP) is presented, RHP strategy elements including the prediction window, rolling window, optimization sub-problems and rolling mechanism are designed. This method can adjust the length of optimization horizon according to uncertainty degree under mission environment, and reduce the solution uncertainty by decomposing the initial problem into multiple optimization sub-problems in shorter horizon. According to optimization sub-problems in optimization horizon, the nested improved simulated annealing algorithm (NISA) is proposed. Case analysis and comparison experiments indicate that DLSDAO-RHP can improve the organizational decision capability under uncertainty mission environment through multiple sub-problems optimization in shorter horizons.
4. Dynamic adaptive optimization method of C2 organization resource-layer structure based on key events is proposed. The mathematical model of measurement parameters for C2 organization resource capabilities is established, which reflects the satisfaction degree of mission resource capability requirements from the perspective of quantity and quality of completed task. The events of platforms loss, task addition and cancellation, and task-processing temporal variation are discussed. The uncertainty events parameters are defined. Based on the analysis of organization resource capability, the adaptive optimization model of resource-layer structure is built, according to the dynamic uncertainty of which, two-stage dynamic adaptive optimization method based on key events (TAOBKE) of resource-layer structure is presented. This method can trigger adaptive optimization of resource-layer structure in real-time through the effect of uncertainty events on organization resource capability. Case analysis and comparison experiments indicate that TAOBKE can improve the organization resource capability effectively under uncertainty mission environment.
5. The comprehensive case to analyze dynamic adaptive optimization of C2 organization structure is designed. Taking a landing compaign in multi-arms combined operation for example, this paper synthetically discusses the effect of influencing events of decision capability and resource capability on C2 organization, and proves the presented SLDAO method. The case analysis illustrates the necessity of dynamic adaptive optimization and adjustment of organization structure under uncertainty mission environment as well as the well performance of SLDAO method. Through layered C2 organizaton structure, the SLDAO method reduces organization reconfiguration effectively, and ensures the stability of C2 organization structure. Along with the increasing of uncertainty events, SLDAO method can control the increase of structure adjustment cost, therefore which has the obvious advantages faced with the frequent occurrence of uncertainty events.
论文的研究集中在不确定使命环境下C2组织能力的测度分析,C2组织结构的适应性优化模型以及动态适应性优化方法等方面,做了以下几个方面的工作:
1、提出了C2组织能力测度。完善了关于C2组织的组织元素和组织结构的定义和描述。特别的,为了度量使命的决策能力需求以及使命环境中不确定因素对组织决策能力的影响,考虑了任务和决策者的决策能力属性。定义了C2组织为有效完成使命所提供的决策能力和资源能力,并提出了相应的测度参数,分别给出了它们的定义和描述。分析了C2组织所面向的使命环境中各种不确定因素的类型、来源及其对组织的影响,以及C2组织结构对组织能力的优化作用,建立了组织结构变量与组织能力之间的优化关系。
2、提出了C2组织结构的分层动态适应性优化方法。基于不确定使命环境下C2组织的能力分析,以组织能力测度最大化为目标构建了C2组织结构适应性优化(C2 Organization Structure Adaptive Optimization, COSAO)模型。针对COSAO模型中结构变量与能力测度具有复杂关联性的特点,提出了基于组织结构分层的适应性优化策略,降低了求解C2组织结构适应性优化模型时的复杂性。针对COSAO模型具有的动态不确定性特点,通过引入滚动时域的思想,提出了基于滚动时域的动态适应性优化策略,采用时域分解原理,通过灵活触发的多次较短时域的优化,降低计算的复杂度并适应所面向环境的不确定性变化。综合两种适应性优化策略,提出了C2组织结构的分层动态适应性优化方法SLDAO,降低了求解COSAO模型时面临的动态不确定性和复杂关联性。
3、提出了基于滚动时域的C2组织决策层结构动态适应性优化方法。建立了C2组织决策能力测度的数学模型,从完成决策工作的量和质两个角度反映了使命决策能力需求的满足程度。讨论了影响决策能力的决策执行能力损耗事件和任务决策负载强度变化事件,定义了不确定事件的参数。在组织决策能力分析的基础上构建了决策层结构适应性优化模型。针对决策层结构适应性优化模型具有的动态不确定性特点,提出了基于滚动时域的决策层结构动态适应性优化方法DLSDAO-RHP,设计了预测窗口、滚动窗口、优化子问题以及滚动机制等滚动时域策略要素。该方法能够根据使命环境的不确定性程度来调整优化时域的长度,将原问题分解为较短时域内多个优化子问题,降低了问题求解时所面临的不确定性。针对优化时域内的优化子问题,提出了嵌套改进模拟退火求解算法NISA。案例分析和对比实验表明,DLSDAO-RHP方法能够通过多次较短时域内的子问题优化提升不确定使命环境下组织的决策能力。
4、提出了基于关键事件的C2组织资源层结构动态适应性优化方法。建立了C2组织资源能力测度的数学模型,从完成任务的量和质两个角度反映了使命资源能力需求的满足程度。讨论了影响资源能力的平台损耗事件、任务新增或取消事件以及任务处理时间变化事件,定义了不确定事件的参数。在组织资源能力分析的基础上构建了组织资源层结构适应性优化模型。针对组织资源层结构适应性优化模型具有的动态不确定性特点,提出了基于关键事件的C2组织资源层结构两阶段动态适应性优化方法TAOBKE。该方法通过不确定事件对组织资源能力的影响及时触发资源层结构适应性优化。案例分析和对比实验表明,在不确定使命环境下TAOBKE方法能够有效的提升组织的资源能力。
5、设计了分析C2组织结构动态适应性优化的综合案例。以一次多军兵种联合作战的登陆战役为例,综合讨论决策能力影响事件和资源能力影响事件对C2组织的影响,对本文所提出的C2组织结构分层动态适应性优化方法SLDAO进行了验证。案例分析说明了不确定使命环境下组织结构动态适应性调整的必要性和SLDAO方法的良好性能,通过对C2组织结构的有效分层,有效减少了组织重构,保证了C2组织结构的稳定性;随着不确定事件的增多,SLDAO方法能够控制组织结构调整代价的增长,因此面对不确定事件频繁发生的情况,SLDAO方法具有明显的优势。
The highly complexity and dynamic uncertainty of information warfare requires agile Command and Control (C2) organization to acquire and maintain the overall confrontation superiority. The agile C2 organization is characterized by well adaptability. How to timely carry out the adaptive optimization on the current organization structure, according to the effect of dynamic arriving uncertainty events on the C2 organization capabilities under mission environment, comes into being one of the primary problems in the research of C2 organization. The existing methods of organization structure adaptive optimization generally select the whole mission execution period as optimization horizon, and mostly adopt organization reconfiguration method, the effect of which is not ideal.The problem of C2 organization structure adaptive optimization still needs further research.
Focusing on the capability measurement of C2 organization under uncertainty mission environment, adaptive optimization model of C2 organization structure and dynamic adaptive optimization method, this paper makes the following contributions:
1. C2 organization capabilities measurement is proposed. The definition and description of C2 organization elements and organization structure are improved, and particularly, in order to measure decision capabilities requirement of mission and effect of uncertainty factors on the decision capabilities, the decision capability attributes of task and decision-makers are considered. The decision capabilities and resource capabilities provided by C2 organization to complete mission effectively are defined, the corresponding measurement parameters of which are proposed respectively, and then the definition and description of measurement parameters are given. The category, source of uncertainty factors and theirs influence on organization under uncertainty mission environment, as well as optimizing function of organization structure to organization capabilities are analyzed, optimal relation between organization structure variables and organization capabilities is established.
2. Layered dynamic adaptive optimization method of C2 organization structure is proposed. The C2 organization structure adaptive optimization (COSAO) model to maximize organization capability is built based on the analysis of C2 organization capabilities under uncertainty mission environment. In view of complex association between structure variables and capabilities measurement, adaptive optimization strategy based on layered organization structure is proposed, which reduces the complexity of COSAO model solution. In view of dynamic uncertainty of COSAO model, through introducing rolling horizon theory, the dynamic adaptive optimization strategy based on rolling horizon is presented. Using horizon decomposition principle and optimization of multiple shorter horizons that triggered flexibly, this strategy is computationally efficient while adapting to the uncertainty change of environment. Combined with two adaptive optimization stategies, the layered dynamic adaptive optimization method of C2 organization structure (SLDAO) is proposed, which reduces dynamic uncertainty and complex association during COSAO model solution.
3. Dynamic adaptive optimization of organization decision-layer structure based on rolling horizon procedure (RHP) is proposed. The mathematical model of measurement parameters for C2 organization decision capabilities is established, which reflects the satisfaction degree of mission decision capability requirements from the perspective of quantity and quality of completed task. The events of decision implementation capability loss and intensity change of task decision-load which influence on the decision capability are discussed. The uncertainty events parameters are defined. Based on the analysis of organization decision capability, the adaptive optimization model of decision layer structure is built, according to the dynamic uncertainty of which, the adaptive optimization method of decision-layer structure based on RHP (DLSDAO-RHP) is presented, RHP strategy elements including the prediction window, rolling window, optimization sub-problems and rolling mechanism are designed. This method can adjust the length of optimization horizon according to uncertainty degree under mission environment, and reduce the solution uncertainty by decomposing the initial problem into multiple optimization sub-problems in shorter horizon. According to optimization sub-problems in optimization horizon, the nested improved simulated annealing algorithm (NISA) is proposed. Case analysis and comparison experiments indicate that DLSDAO-RHP can improve the organizational decision capability under uncertainty mission environment through multiple sub-problems optimization in shorter horizons.
4. Dynamic adaptive optimization method of C2 organization resource-layer structure based on key events is proposed. The mathematical model of measurement parameters for C2 organization resource capabilities is established, which reflects the satisfaction degree of mission resource capability requirements from the perspective of quantity and quality of completed task. The events of platforms loss, task addition and cancellation, and task-processing temporal variation are discussed. The uncertainty events parameters are defined. Based on the analysis of organization resource capability, the adaptive optimization model of resource-layer structure is built, according to the dynamic uncertainty of which, two-stage dynamic adaptive optimization method based on key events (TAOBKE) of resource-layer structure is presented. This method can trigger adaptive optimization of resource-layer structure in real-time through the effect of uncertainty events on organization resource capability. Case analysis and comparison experiments indicate that TAOBKE can improve the organization resource capability effectively under uncertainty mission environment.
5. The comprehensive case to analyze dynamic adaptive optimization of C2 organization structure is designed. Taking a landing compaign in multi-arms combined operation for example, this paper synthetically discusses the effect of influencing events of decision capability and resource capability on C2 organization, and proves the presented SLDAO method. The case analysis illustrates the necessity of dynamic adaptive optimization and adjustment of organization structure under uncertainty mission environment as well as the well performance of SLDAO method. Through layered C2 organizaton structure, the SLDAO method reduces organization reconfiguration effectively, and ensures the stability of C2 organization structure. Along with the increasing of uncertainty events, SLDAO method can control the increase of structure adjustment cost, therefore which has the obvious advantages faced with the frequent occurrence of uncertainty events.
引文
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