堆底熔穿事故的场外后果及其对策

在压水堆核电站应急计划和应急响应中，堆底熔穿事故占有重要位置。本文用法国核电厂事故源项Ｓ３（对应于堆底熔穿事故）计算了在典型气象条件下的场外放射学的后果，根据一般防护决策原则和我国颁布的干预水平，提出了保护公众的应急防护措施的建议。     

遗传算法和Dijkstra算法在动态权值系统中的比较

针对遗传算法和Dijkstra算法在求解动态权值系统中最短路径时的性能问题,采用比较法,将两种算法应用在同一个实际游戏模型中,对其算法的稳定性、智能性、时间复杂度进行对比测试。游戏模型模拟了各种条件下的动态权值系统。为了使遗传算法更加可靠,通过优化其变异过程使得收敛速度更快,可靠性更高。实验数据表明,遗传算法在每张地图上的得分数以及算法所用时间普遍高于Dijkstra算法,从而得出遗传算法在求解动态权值系统中最短路径问题时稳定性和预期效果明显好于Dijkstra算法,但其时间复杂度较高的结论。     

基于最短路径树的优化生存时间路由算法

为提高无线传感网的生存时间,提出基于最短路径树的优化生存时间路由算法(LORA_SPT).该算法引入节点分类概念,构造基于链路能耗因子、自身节点剩余能量因子、邻居节点剩余能量因子和类型权重因子等多个因子的权值函数.针对不同类型的节点采用不同的权重因子,最后利用dijkstra算法完成最短路径树,所有节点沿着最短路径树将...     

自适应回声对消的初期迭代统计学模型及改进算法

为减少滤波器长度,提出自适应滤波算法初期迭代统计学模型及改进的回声消除算法。提出的统计学模型分析了自适应算法迭代初期滤波器各系数的均值和方差。基于该模型提出的改进算法,先检测回声路径峰值,进而确定回声路径延时,然后以延时为中心,用一个短的滤波器辨识原回声路径活跃系数部分。用实际回声路径进行仿真,理论和实验结果均表明,新算法在迭代的前75～100步已可准确检测回声路径峰值并确定延时;而减少滤波器长度,可大幅提高自适应算法收敛速度并降低算法计算复杂度。     

认知定位系统的自适应精度研究

为了能够实现定位精度的自适应,本文分析了认知无线电定位系统(cognitive positioning systems,CPS)的原理。该系统利用频谱及环境感知等模块中的传感器单元来获取所需的定位参数,并将其与到达时间(time of arrival,TOA)定位估计算法相结合,以克拉美罗下界(Cramer-Rao lower bound,CRLB)为准则分析最大似然(maximum likelihood,ML)定位精度自适应算法。本文还对单径和多径环境条件下定位精度自适应性能进行了比较。仿真结果表明,该算法能够实现定位精度的自适应,并能通过改善系统可用的频带带宽、分散频谱的数目、信号的调制方式等因素来提高定位精度。     

PTN网络的二次断纤链路规划算法

在PTN（PacketTransportNetwork）网络规划建设中,需要对光纤链路留出备份带宽,以保证部分光纤断开时,受影响业务有足够的容量进行路由重组。这也是提高网络生存性的有效方法之一。文中首先遍历网络双链路的失效状态,然后断开网络中任意两条链路,通过逐次增加链路容量来保证失效业务能够重组路由;最后提出二次断纤链路容量规划算法并进行试验仿真。结果表明,该算法在节约网络带宽、降低建造成本以及故障容错方面有着良好性能,能够很好应用于传送网络的链路规划中。     

基于神经网络的电子地图最优路径研究

现阶段电子地图的应用已经深入到各行各业,最优路径的查找及规划作为GIS的一个关键问题,成为研究的一个难点,本文提出一种基于Hopfield神经网络的方法来解决地图最优路径问题。以物流配送中的车辆路径最优化为例,先根据Hopfield神经网络模型研究基于Hopfield的最优路径算法,然后探讨和验证该算法的可行性、实用性,最后通过算例分析对该方法进行验证。     

考虑多样化需求的诊疗设备维护和患者调度联合优化研究

针对诊疗设备维护和住院患者候诊难以有效协调的问题,建立了联合优化模型。首先,假设设备具有离散的劣化状态,将设备的劣化过程建模为连续时间马尔可夫链;其次,考虑到患者对诊疗设备的不同功能频率需求,以及不同劣化状态对患者治疗时间、费用的影响,以患者就诊顺序、检查策略、修复策略为决策变量建立了设备维护和患者调度模型;最后,采用改进后的非支配排序遗传算法对多目标问题进行了求解。实验结果验证了设备维护与患者调度联合优化模型的有效性。改进后的算法提高了整体和局部的搜索能力,且具有鲁棒性。     

Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.