ID 原文 译文
11474 但经典PERT不能对具有随机后续工序的情况进行建模,而且存在时间参数估计主观性强,估计误差较大等不足。 But classic PERT cannot of subsequent process with stochastic modeling, parameter estimation of time and is subjective, error estimation, etc. ‭
11475 本文提出在经典PERT模型中加入跳转虚工序的方法增强其对随机工序的建模能力,并提出了相应的分析评估方法。 In this paper, add jump in classical PERT model of virtual process method to improve their ability of modeling of random process, and proposed the corresponding evaluation methods.
11476 另外,提出了基于德尔菲评估法与专家权重相结合并综合相似工序的历史数据的方法克服经典方法在时间参数确定方面的不足。 ‭In addition, the assessment is proposed based on Delphi method combined with expert weight and comprehensive historical data of similar process method to overcome the time parameters determine the deficiency of classic methods.
11477 最后,利用某装备的使用保障过程PERT建模分析实例验证了该方法的有效性。 ‭Finally, the use of the use of certain equipment safeguard process as PERT model analysis to verify the effectiveness of the proposed method.
11478 备件是装备保障的重要物质基础,合理规划备件的配置方案是提高装备保障效能的关键。 Spare parts is an important material foundation of equipment support, reasonable planning and configuration scheme of spare parts is the key to increase the efficiency of the equipment support.
11479 针对多级保障系统备件配置优化的高维、非线性问题,构建了以备件保障度最大、保障费用最小为目标函数,以其他准则为约束条件的优化配置模型。 Spare parts for multilevel security system configuration optimization of high-dimensional, nonlinear problems, building on the spare parts to ensure maximum, security costs, and objective function is minimum to the optimal allocation model of other criteria as constraint conditions.
11480 面向优化模型求解的难题,在传统粒子群算法的基础上,提出了一种改进的粒子群求解算法。 Oriented optimization model to solve the problem, on the basis of the traditional particle swarm optimization (pso) algorithm.
11481 给出了该算法的设计思路和优化流程,采用基于准则的方法以及改进惯性权重等措施,以两个目标作为引导,在备件配置方案生成时可以避免长时间的无效搜索,提高了粒子群优化算法的求解效率。 An improved particle swarm algorithm, the algorithm design and optimization process is given, based on the principles of methods, and improve the inertia weight, measures such as guide, with two goals as the spare parts configuration scheme generation can avoid long invalid search, improved particle swarm optimization algorithm's efficiency.
11482 最后通过算例证明该方法的可行性和有效性。 And finally USES case to prove the feasibility and effectiveness of the method.
11483 当系统某次评价不达标,选取怎样的方案使系统整改后达标便是评价后续决策问题。 When an evaluation system is not up to standard, the selection standard is how to plan the system improvement after evaluation of subsequent decision problem.