一种实用的二维不规则零件排样算法

模拟人排样的排样过程,提出了排样问题的计算和过程模型,实现了直接对多边形进行排样,在实现每重零件的最优的基础上实现排样的近优,实现了快速高效的排样.     

矩形件排样问题的遗传算法求解

本文研究了求解矩形件正交排样优化问题的遗传算法。同时，将矩形件正交排样问题转化为一个排列问题，提出了求一个排列所对应的排样图的下台阶算法（改进的ＢＬ算法）将下台阶算法与遗传算法相结合，用于矩形件排样问题的求解，给出了该算法的实现。用该算法对文献中的两个算例进行了求解，结果表明该算法获得了比ＢＬ算法更好的解，是一种较为行之有效的方法。     

基于改进免疫遗传算法的矩形件排样

文章在基本免疫遗传算法的基础上提出了针对矩形件排样问题的改进算法,探讨了能记忆排样过程先验知识的浓度算子对排样过程的影响,实验证明是有效的。     

基于改进免疫遗传算法的不规则图形排样

在基本遗传算法的基础上提出了针对件排样问题的改进免疫算法;探讨了能记忆排样过程先验知识的浓度算子对排样过程的影响,实验证明是有效的.     

基于遗传模拟退火算法的不规则多边形排样

将遗传授拟退火算法应用于计算机辅助排样领域，设计了一种基于遗传模拟退火技术的启发式排样算法．该算法能够处理不规则多边形的排样问题；同时，给出一种对象的几何表达方式，可以忽略高度不规则形状带来的复杂性影响．该算法通过基于遗传模拟退火算法的全局优化概率搜索，寻找排样件在排样时的最优次序及各自的旋转角度，然后采用基于左下角(BL)策略的启发式排样算法实现自动排样．     

船舶排样零件的编码研究及排样数据库设计

通过分析船舶排样零件形状特征、行业特征,对排样零件进行编码,使其适应于排样过程中快速搜索零件和匹配零件需要;根据排样自动化的实际需要,建立排样管理数据库,成为船舶设计自动化的基础;采用了上述编码机制和数据库设计方案,提高了排样的速度和材料利用率。     

基于两阶段的分段单一矩形优化排样

为有效解决分段单一矩形优化排样问题,给出一个求解分段单一矩形优化排样问题的两阶段方法。第一阶段完成标准子段最佳排样方式求解,并将二维排样问题转化为一维下料问题,第二阶段使用适合于一维下料问题求解的算法完成板材最佳排样方式求解。使用该方法开发了一个单一矩形优化排样系统,该系统既可以解决分段单一矩形排样问题也可以解决其他类型的单一矩形优化排样问题。企业应用实例表明该方法是求解分段单一矩形优化排样问题的一个较为有效的方法。     

矩形件优化排样问题的混合遗传算法求解

利用遗传算法结合剩余矩形排样法求解矩形件正交排样问题。通过遗传算法将矩形件正交排样问题转化为一个排列问题,并引入剩余矩形排样算法来惟一确定每一个排列所对应的排样图(即排样方案),两者结合用于求解矩形件排样问题。最后用此混合遗传算法对文献[1]中的两个算例进行了验证,表明了其有效性。     

矩形件优化排样问题的混合遗传算法求解

利用遗传算法结合剩余矩形排样法求解矩形件正交排样问题。通过遗传算法将矩形件正交排样问题转化为一个排列问题，并引人剩余矩形排样算法来惟一确定每一个排列所对应的排样图（即排样方案），两者结合用于求解矩形件排样问题。最后用此混合遗传算法对文献[1]中的两个算例进行了验证，表明了其有效性。     

计算机辅助排样(CAN)在服装设计裁剪中的应用

文中针对服装企业手工排样裁剪的实际，提出了一个计算机辅助排样方案，并详细论述了排样系统的组成及关键技术问题的实现。     

基于敏捷造船的套料管理数字化系统设计

本文立足于套料的实际业务,以套料流程为中心,重点对零件模块、材料模块、任务模块进行需求分析和功能分析、设计与实现。系统采用客户端连接多服务器系统,将零件对象与关系数据库结合,在套料算法设计中采用面向对象算法设计,在系统的需求分析过程中,采用UML进行程序流程的设计和分析。     

An efficient reliability analysis approach for structure based on probability and probability box models

In this paper, a new reliability analysis method for engineering structures is developed based on probability and probability box (p-box) models. Random variable distributions are used to deal with the uncertain parameters with sufficient information, while the p-box models are employed to deal with the uncertain-but-bounded variables. Due to the existence of the p-box parameters, a limit-state band will result and a complex nesting optimization problem will be involved in this reliability analysis. To reduce the computational burden, an efficient decoupling strategy is developed to solve the nesting optimization problem. Through interval analysis for the probability transformation process, the complex nesting optimization problem can be transformed to a single-layer optimization model. Then, the optimum solution and corresponding reliability index can be obtained by introducing a sequential quadratic programming (SQP) method. Four engineering numerical examples are investigated to demonstrate the effectiveness of the present method.     

An optimizing model to solve the nesting problem of rectangle pieces based on genetic algorithm

In the process of cement equipment manufacturing, the demand of rectangle pieces of steel structure is very large. The traditional manual nesting, which is simply cutting by hand-making according to the arrangement of the number and size, causes the low efficiency and material wasting. To solve the problem above, this paper proposes an optimizing model for nesting problem of rectangle pieces. Firstly, with the aim of the maximum utilization ratio of the sheet, the optimization mathematical model for nesting problem of rectangle pieces is established. The lowest horizontal line searching algorithm is described in detail. Secondly, the mathematical model is solved to get the optimal solution by the combination of genetic algorithm and the lowest horizontal line searching algorithm. In the solution process, this paper presents the methods of gene encoding and decoding, definition of fitness function, the design of genetic operators and the design of algorithm operating parameters. Finally, we use one sheet as an example to illustrate the proposed model and algorithm process. Experimental results have shown that the proposed approach is able to achieve rectangle pieces nesting with the maximum material utilization ratio.     

一种不规则零件排样的新粒子群优化策略

基于图形扫描转换的启发式底左（Heuristic Bottom-Left,HBL）算法,把一种最大速度收缩策略（Maximal Velocity Contractile Strategy,MVCS）的粒子群优化（Particle Swarm Optimization,PSO）算法应用于不规则零件的优化排样,给出了新的排样组合优化算法（MVCS-PSO）的粒子构造方法和零件排样过程,通过实例把该算法与模拟退火遗传算法（Simulated Annealing Genetic Algorithms,SAGA）进行优化排样比较,实验结果表明,具有良好的非线性和动态搜索性能的MVCS-PSO算法是求解排样问题的一种高效算法。     

钣金CAD中一个较优的排料算法

为了解决钣金设计、制造过程中排料工序效率低,周期长的问题,根据最优化理论,在网络算法的基础上设计并实现了一个在钣金ＣＡＤ中较优的排料方案,分析了排样算法的性能,构造了一个实例,说明该算法的界是可以达到的。     

Toward an optimal PRNN-based nonlinear predictor

We present an approach for selecting optimal parameters for the pipelined recurrent neural network (PRNN) in the paradigm of nonlinear and nonstationary signal prediction. We consider the role of nesting, which is inherent to the PRNN architecture. The corresponding number of nested modules needed for a certain prediction task, and their contribution toward the final prediction gain give a thorough insight into the way the PRNN performs, and offers solutions for optimization of its parameters. In particular, nesting allows the forgetting factor in the cost function of the PRNN to exceed unity, hence it becomes an emphasis factor. This compensates for the small contribution of the distant modules to the prediction process, due to nesting, and helps to circumvent the problem of vanishing gradient, experienced in RNNs for prediction. The PRNN is shown to outperform the linear least mean square and recursive least squares predictors, as well as previously proposed PRNN schemes, at no expense of additional computational complexity.     

一种分阶段优化的排料算法

提出一种基于分阶段优化的排料算法.在算法的第一阶段,在排料前先估算本阶段用到的原板材尺寸和数量,将权重因子超过阅值的待排件均匀分布在其上.在排料过程中再根据待排件权重因子和组合因子动态调整空闲区域的排料布局,从而可大幅度降低排料算法复杂度,提高板材利用率.在第二阶段,将剩余待排件合理排放在原板材上,在排料即将结束时,可根据相邻因子确定排料布局以减少废料碎片.生产实践表明,这样研制出的排料系统利用率高、布局合理,能满足工业上的一刀切要求.     

Sequential optimization approach for nesting and cutting sequence in laser cutting

The economy of the laser cutting process depends on two productivity issues: (i) nesting, a classic problem of finding the most efficient layout for cutting parts with minimum material waste; (ii) cutting sequence, which targets the optimal sequence of edges of the parts to be cut for minimum cycle time. This paper presents a two stage sequential optimization procedure for nesting and cutting sequence for the objectives of maximizing material utilization and minimization of ideal (non-cut) travel distance of laser cut tool. However, the focus of this paper is the development of solution technique for optimal cutting sequence to any given layout. Simulated annealing algorithm (SAA) is considered to evolve the optimal cutting sequence. The proposed SAA is illustrated with the optimal material utilization layout obtained using sheet cutting suite software, a professional rectangular nesting software package. The robust test carried out with five typical problems shows that the SAA proposed for cutting sequence is capable of providing near optimal solutions. The performance comparison with two literature problems reveals that the proposed SAA is able to give improved result than GA and ACO algorithms.     

OpenMP指导语句全局嵌套类型的静态分析及应用

由于指导语句动态嵌套与绑定规则的存在,OpenMP程序中线程的一些上下文只能在运行时刻才能完全确定.然而,通过编译时刻的静态分析可以部分确定指导语句的嵌套类型,这些信息可以用于指导后续的编译与优化.由于函数调用的存在,嵌套与绑定常常会跨越过程边界,除了通常的局部和全局分析之外,还需要过程间分析的支持.通过在通常的过程间分析的基础上附加信息,可以使得嵌套类型信息在过程调用图中进行传播.将这些全局信息与过程内的局部信息结合起来,就可以在编译时刻确定语句的嵌套类型.结果表明,编译时刻的嵌套类型分析可以有效地确定通常的科学与工程计算程序中指导语句的嵌套类型,基于嵌套类型的翻译与优化可以同时减少运行时开销和目标代码长度.     

板料优化排样问题

在材料加工领域,板料优化排样是实现薄板和厚板材料充分利用的一个常见问题。该问题是典型的NP完全问题,其求解过程复杂,求解耗时大,难以获得精确解。这不利于该问题的工程应用,为此,目前学术界提出了多种用于解决该问题的近似算法,求取在工程应用中可接受且耗时合理的优化排样方案。该文在对板料排样问题进行阐述的基础上,对近年来国内在板料优化排样问题方面所开展的研究进行了分析,对板料排样问题的发展前景进行了展望。