工业产品造型中布尔运算的运用

布尔运算是造型的基本算法之一,它有较高的通用性和低效率的特点.文章分析了布尔运算低效及呆板的原因,并通过解析造型实例,提出了减少操作修改和提高布尔运算效率的组织策略和运用技巧,这在生产企业的设计与制造中还是有可观的效益.     

冲裁件图形输入系统功能模块的设计

提出了面素拼合辅以线辅入法.其中所设计的每一基本面素均以参软化定义方式存在于用户界面,而以数字模型方式存在于计算机内.在面素的拼合运算(布尔运算)中,提供交、并、差等3种形状算子.参加布尔运算面素可为2个封闭的二维(2D)任意多边形,其交点可有多个(2个以上).同时提供以逐条线段输入图形的方法.从而提高了系统的工作效率及输入复杂图形的能力.     

二维轮廓布尔运算干涉标志算法研究

提出了一个基于干涉标志的计算二维几何轮廓布尔运算的新算法。该算法采用干涉标志计算二维几何轮廓的干涉索引,根据干涉索引值拾取不同的轮廓段分别得到二维几何轮廓的交集、差集和并集。此算法只通过一次计算就可以得到不同的布尔运算结果集,具有较高的算法效率。     

一种用于几何造型的一维布尔运算新算法

几何造型中，往往要进行线段与面、线段与体的分类，分类后用布尔算子合成。本文用自动机理论对合成过程进行了研究，结果给出了一种新的一维布尔运算算法。用有限状态自动机实现的该算法可用于线─—面分类及线─—体分类后的合成。     

用分段法与链表法的二维布尔运算

二维布尔运算是计算机图形学中的基本算法。通过把两个二维几何形体分别按入点与出点位置顺序分段，组合成一个新的图形，形成了—种新的二维布尔运算算法：分段法。笔者介绍了分段法的几何原理和基本步骤，并利用面向对象技术实现复杂数据结构操作的基本方法——链表法实现了常用的二维布尔运算算法，并将两种方法作了比较。     

一种基于单调多边形的三角剖分算法

提出了一种基于单调多边形三角化算法,被三角化的多边形可以含有任意个内孔。先根据边界y（x）方向的局部极值顶点作水平（垂直）分割线,将多边形划分成单连通y（x）单调多边形,然后再将各单调多边形三角化。算法考虑了各种几何奇异情况,因此比较稳定。     

格网划分的双策略跟踪多边形裁剪算法

论文提出了一种高效稳定的多边形裁剪算法,算法支持带内环的平面简单多边形,同时也支持多边形的“并”和“差”等布尔运算.首先,设计了算法所需的数据结构;其次,基于直线扫描转换Bresenham算法原理提出了边网格划分的有效算法,并应用一个简单的方法避免不同网格内边的重复求交;最后,将交点分类为普通交点和顶交点,并针对这两类交点构造了不同的跟踪策略,在跟踪过程中交替、递归地应用这两个策略来确保算法处理特殊情况时的稳定性.与其它同类算法的比较表明,新算法具有更高的效率.     

一种任意复杂程度二维多边形的求交算法

对于由直线段和圆弧段组成的大数据量二维多边形,作者提出一种多边形之间求交点的算法。该算法通过区域划分,把一个大规模的求交点运算分解为多个小规模的求交点运算,计算每个区域内的交点,然后对交点集进行整理,去除重复交点。该算法从实用的角度出发,避开繁琐的数学推理,便于理解,便于编程实现。算法明显地提高了求交点的效率,缩短了运算时间,并在实际应用中取得良好的效果。     

任意二维图形的复杂窗口裁剪算法及其应用

本文提出了一种基于交点全搜索方式的复杂二维图形裁剪算法。该算法通过引入虚边，简化复连通区域为单连通多边形，能够有效地用于多个复连通区域之间的互相裁剪。算法经实际应用证明，具有较高的效率和很好的稳定性。     

点包容性检测

介绍了一个易于实现的点包容性检测算法--倾斜射线法,其特点是射线不会与多边形的顶点或边重合,无须作特殊情况的处理,需计算的区域小,因而计算量小.判断倾斜射线与多边形的交点数,如果交点数为奇数,则点在多边形内,否则,点在多边形外.验证表明,此算法简单有效、稳定可靠,对自相交多边形及带孔多边形等情况同样适用.     

On simplification of large fault trees

This paper describes some fault tree optimization algorithms used in STUK PSA code SPSA that is capable of generating minimal cut sets for fault trees containing 12 000 gates and basic events. The complexity of a minimal cut set search is a function of multiple gates and basic events. In a fault tree containing 1000 multiple elements, these optimizations reduce the number of multiple elements often by 30 to 50%. One novel feature of the algorithm is that no boolean reduction rules are used, since the fault tree itself contains its simplification rules.     

基于三视图的实体重建技术研究

基于三视图的三维实体重建技术是根据已有二维视图中的几何信息和拓扑信息,生成相应的三维实体模型。以AutoCAD绘制的三视图及DXF格式的文件信息为基础,提出并实现了一种由三视图重建三维实体的算法。通过对三视图的规则处理,由计算机自动实现实体重建,对三视图分别进行平移、旋转、拉伸操作,然后作相应的布尔运算即可反求出该视图所对应的三维实体。     

Probabilistic Algorithms for Geometric Elimination

We develop probabilistic algorithms that solve problems of geometric elimination theory using small memory resources. These algorithms are obtained by means of the adaptation of a general transformation due to A. Borodin which converts uniform boolean circuit depth into sequential (Turing machine) space. The boolean circuits themselves are developed using techniques based on the computation of a primitive element of a suitable zero-dimensional algebra and diophantine considerations. Our algorithms improve considerably the space requirements of the elimination algorithms based on rewriting techniques (Gr?bner solving), having simultaneously a time performance of the same kind of them.     

A hybrid optimization model for proficient bidding strategies with no Karush–Kuhn–Tucker optimality conditions

The computational complexity behind the bi‐level optimization problem has led the researchers to adopt Karush–Kuhn–Tucker (KKT) optimality conditions. However, the problem function has more number of complex constraints to be satisfied. Classical optimization algorithms are impotent to handle the function. This paper presents a simplified minimization function, in which both the profit maximization problem and the ISO market clearance problem are considered, but with no KKT optimality conditions. Subsequently, this paper solves the minimization function using a hybrid optimization algorithm. The hybrid optimization algorithm is developed by combining the operations of group search optimizer (GSO) and genetic algorithm (GA). The hybridization enables the dispersion process of GSO to be a new mutated dispersion process for improving the convergence rate. We evaluate the methodology by experimenting on IEEE 14 and IEEE 30 bus systems. The obtained results are compared with the outcomes of bidding strategies that are based on GSO, PSO, and GA. The results demonstrate that the hybrid optimization algorithm solves the minimization function better than PSO, GA, and GSO. Hence, the profit maximization in the proposed methodology is relatively better than that of the conventional algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

AN OPERATION-SEQUENCE-BASED APPROACH IN DESIGNING A FLOW LINE INDEPENDENT-CELL SYSTEM WITH MACHINE CAPACITY INCORPORATED

This paper considers a problem of designing a flow line independent-cell system where each machine can treat multiple production operations and at most two machines of each machine type can be installed in the same cell. The objective is to minimize the total system cost including machine cost and material handling cost subject to each cell capacity. The problem is characterized as NP-hard. Therefore, in order to find a good solution efficiently, this paper proposes two greedy-type heuristic algorithms including a single-combining algorithm and a double-combining algorithm. Both the algorithms are derived by using the process of combining the cells and are tested for their efficiencies with various numerical problems.     

图像腐蚀和膨胀的算法研究

本在图像腐蚀和膨胀的传统算法的基础上，通过分析向量和位移运算的性质，分别对两种易于计算机实现的算法进行了研究一基于向量和位移运算的腐蚀和膨胀算法。实验结果表明该算法易于实现，并且可以极大地提高运算效率，本同时给出了三种算法的理论对比与评价，并验证了腐蚀和膨胀的一个经典的代数性质。     

Robot arc welding task sequencing using genetic algorithms

This paper addresses welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in welding process planning, it has been considered through empirical knowledge, rather than a systematic approach. Thus, an effective task sequencing method for robot arc welding is required. Welding operations can be classified by the number of weldlines and layers. Genetic algorithms are applied to tackle those welding task sequencing problems in productivity and welding quality aspects. A genetic algorithm for the Traveling Salesman Problem (TSP) is utilized to determine welding task sequencing for a multiweldline-singlepass problem. Further, welding task sequencing for multiweldline-multipass welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is presented to solve multi-robot welding task sequencing: mutliweldline with multiple robots. Finally, the genetic algorithms are implemented for the welding task sequencing of three-dimensional weld plate assemblies. Various simulation tests for a welded structure are performed to find the combination of genetic algorithm parameters suitable to weld sequencing problems and to verify the quality of genetic algorithm solutions. Robot operations for weld sequences are simulated graphically using the robot simulation software IGRIP.     

Branch and fathoming algorithms for operation sequencing in process planning

This paper focuses on the operation sequencing problem in process planning, which is the problem of determining the sequence of operations required for producing a part with the objective of minimizing the sum of machine, set-up and tool change costs. In general, operation sequencing in process planning has been a difficult optimization problem, since it has combinatorial characteristics and complex precedence relations. This paper first suggests a precedence-directed graph to describe the precedence relations among operations with a compact form. Then, using the directed graph, the authors develop optimal and heuristic branch and fathoming algorithms that can give optimal and near optimal solutions respectively. Application of the two algorithms is illustrated using an example part. Also, to show the performance of the suggested algorithms, computational experiments were done on randomly generated problems and the results show that the heuristic algorithm gives optimal solutions for all the generated problems with much shorter computation time.     

Use of genetic algorithms to solve production and operations management problems: A review

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.     

Balancing transfer lines using Benders decomposition and ant colony optimisation techniques

In this paper, we investigate a transfer line balancing problem in order to find the line configuration that minimises the non-productive time. The problem is defined at an auto manufacturing company where the cylinder head is manufactured. Technological restrictions among design features and manufacturing operations are taken into consideration. The problem is represented by an integer programming model that assigns design features and cutting tools to machining stations, and specifies the number of machines and production sequence in each station. Three algorithms are developed to efficiently solve the problem under study. The first algorithm uses Benders decomposition approach that decomposes the proposed model into an assignment problem and a sequencing problem. The second algorithm is a hybrid algorithm that mixes Benders decomposition approach with the ant colony optimisation technique. The third algorithm solves the problem using two nested ant colonies. Using 15 different problem dimensions, we compare results of the three algorithms in a computational study. The first algorithm finds optimal solutions of small problem instances only. Second and third algorithms demonstrate optimality gaps less than 4.04 and 3.8%, respectively, when compared to the optimal results given by the first algorithm. Moreover, the second and third algorithms are very promising in solving medium and large-scale problem instances.