复杂约束下的导弹作战任务规划模型和算法研究*

导弹作战任务规划是一个涉及时间、资源、质量和其他关系约束的复杂问题.首先通过定叉约束满足效用对基本约束满足模型进行了扩展,建立了导弹任务规划的约束满足优化模型.在此基础上,研究了任务规划模型求解的时间和效用传播算法,提出了基于综合效用的优化求解框架.该模型和求解框架易于解决具有多种约束因素的复杂问题,具有较好的通用性.通过定义软、硬约束效用,使得实际任务规划问题求解具有更好的灵活性.     

Disaster management in case of CBRNe events: an innovative methodology to improve the safety knowledge of advisors and first responders

Nowadays, Chemical-Biological-Radiological-Nuclear-explosive (CBRNe) risks are one of the world's main safety concerns. The radiological disasters of Fukushima and Chernobyl, the chemical events of Seveso or the release of Sarin in the Tokyo Subway, the biological emergencies such as the H1N1 flu or the recent Ebola outbreak, and recent news about the availability of non-conventional weapons acquired by fundamentalist organisations represent evidence of potential future threats. CBRNe risks are a real and global threat. The University of Rome, Tor Vergata, in collaboration with the most important Italian and international bodies working in the field of CBRNe safety and security, and supported by the North Atlantic Treaty Organisation and the Organisation for the Prohibition of Chemical Weapons, organises International Masters Courses on the Protection against CBRNe events. Within this framework, a Table-Top Exercise was planned, in collaboration with the Italian Ministry of Interior and Ministry of Defence. The scenario, the logistic organisation, on-going adjustments during the exercise and the outcomes are presented here and analysed.     

基于势场启发蚁群算法的远程水中兵器航路规划

针对航行误差较大的远程水中兵器航路规划问题,采用栅格化方法建立海洋环境模型,为使兵器在航行过程中能有效规避障碍并导向目标,提出一种人工势场力为启发因子的改进蚁群算法,利用该方法搜索远程水中兵器从起始点至目标点的最佳路径,算法解决了经典蚁群算法容易陷入局部最优及收敛速度慢的问题。仿真结果表明该规划算法虽有少量的路径损失,但可以有效避免由于误差引起的航行安全问题,是一种有效的远程水中兵器航路规划方法。     

Column generation for stochastic green telecommunication network planning with switchable base stations

We present the green telecommunication network planning problem with switchable base stations, where the location and configuration of the base stations are optimized, while taking into account uncertainty and variability of demand. The problem is formulated as a two‐stage stochastic program under demand uncertainty with integers in both stages. Since solving the presented problem is computationally challenging, we develop the corresponding Dantzig‐Wolfe reformulation and propose a solution approach based on column generation. Comprehensive computational results are provided for instances of varying characteristics. The results show that the joint location and dynamic switching of base stations leads to significant savings in terms of energy cost. Up to 30% reduction in power consumption cost is achieved while still serving all users. In certain cases, allowing dynamic configurations leads to more installed base stations and higher user coverage, while having lower total energy consumption. The Dantzig‐Wolfe reformulation provides solutions with a tight LP‐gap eliminating the need for a full branch‐and‐price scheme. Furthermore, the proposed column generation solution approach is computationally efficient and outperforms CPLEX on the majority of the tested instances. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 351–366, 2016     

基于改进快速扩展随机树的机械臂路径规划

针对机械臂路径规划问题,提出一种基于改进RRT算法的路径规划方法。改进RRT结合了目标偏置策略和贪婪生长策略的优点,在随机采样时,以一定概率使采样点偏置为目标节点,降低随机采样的盲目性,在目标节点方向上采用贪婪式扩展策略,增加随机树局部方向上的生长速度。RRT法规划路径结果并非最优,提出改进GPP法删除多余路径节点,优化机械臂运动路径。通过与Biased-RRT和Greedy-RRT数值仿真结果对比,证明了改进RRT在计算时间、迭代次数、扩展节点数上均优于以上方法。在机械臂两种典型工作环境中的仿真结果表明,使用该方法可以较好解决排爆机械臂避障路径规划问题。     

A bidirectional heuristic for maximizing the net present value of large‐scale projects subject to limited resources

We consider the scheduling of large‐scale projects to maximize the project net present value given temporal and resource constraints. The net present value objective emphasizes the financial aspects of project management. Temporal constraints between the start times of activities make it possible to handle practical problem assumptions. Scarce resources are an expression of rising cost. Since optimization techniques are not expedient to solve such problems and most heuristic methods known from literature cannot deal with general temporal constraints, we propose a new bidirectional priority‐rule based method. Scheduling activities with positive cash flows as early and activities with negative cash flows as late as possible results in a method which is completed by unscheduling techniques to cope with scarce resources. In a computational experiment, we compare the well‐known serial generation scheme where all activities are scheduled as early as possible with the proposed bidirectional approach. On the basis of a comprehensive data set known from literature containing instances with up to 1002 activities, the efficiency of the new approach is demonstrated. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

基于分形理论的目标航路预测

电磁干扰条件下解决断续航路预测问题对防空兵有效抗击空中目标至关重要。针对包络灰预测方法预测精度较低和Verhulst灰色预测模型计算过程复杂的情况,提出了预测航路的分形方法。在分形理论的基础上,研究了其用于目标断续航路预测的基本思路,建立了基于分形的目标航路预测模型,并对模型进行了求解。最后,利用建立的分形模型对雷达丢失目标后的目标航路进行了预测,通过实例体现了分形方法在用于航路预测时的准确性、灵活性和易实现等特点。结果表明,用该法对断续目标航路的预测具有一定的借鉴意义。     

一种基于知识的作战计划系统设计

作战计划系统是复杂的问题求解系统,在军事领域的各个方面发挥着重要的作用。介绍了当前一些主要的作战计划辅助生成系统及其特点,分析比较和归纳整理了当前军用计划系统所采用的一些常见规划技术和优缺点,重点分析了作战计划系统中的智能规划技术。在此基础上,提出了一种基于知识的作战计划辅助生成系统的开发设想,构建了系统知识库,系统采用层级任务网络规划技术,给出了这种规划技术的理论框架和算法过程,并且把这种技术与多主体规划技术相结合,辅助实现军事作战任务的逐层分解与规划优化,进而生成完整的行动序列,较好地解决了作战计划辅助生成问题。     

基于Multi-Agent的武器装备虚拟维修训练系统

提出了一种基于Multi-Agent的虚拟维修训练系统(VMTS)结构框架,整个系统分别由主控Agent、仿真Agent、和接口Agent3个具有交互作用的Agent组成,从而将虚拟维修训练系统的开发转化为一个多Agent系统的设计与开发.基于多Agent的框架结构可实现受训者的智能模型及虚拟训练场景中虚拟物体的行为模型,从而可以提高VMTS的健壮性和可重用性.基于Agent的概念模型实现了Agent之间的交互和协作,并介绍了主控Agent和仿真Agent的具体实现方法.     

Models for production planning under power interruptions

We present a robust optimization model for production planning under the assumption that electricity supply is subject to uncertain interruptions caused by participation in interruptible load contracts (ILCs). The objective is to minimize the cost of electricity used for production while providing a robust production plan which ensures demand satisfaction under all possible interruption scenarios. The combinatorial size of the set of interruption scenarios makes this a challenging problem. Furthermore, we assume that no probabilistic information is known about the supply uncertainty: we only use the information given in the ILC to identify an uncertainty set that captures the possible scenarios. We construct a general robust framework to handle this uncertainty and present a heuristic to compute a good feasible solution of the robust model. We provide computational experiments on a real‐world example and compare the performance of an exact solver applied to the robust model with that of the heuristic procedure. Finally, we include the operational impact of interruptions such as “recovery modes” in the definition of the uncertainty set. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013