冲压件的智能排样

本文提出针对冲压件的一种基于知识的智能优化排样技术。把数学方法、子目标法、启发式搜索法及符号推理方法有机集成，协同求解。首次将制件下料技术的作业性作为排样的一种约束条件，探讨了智能优化排样的一些关键技术方法。     

冲裁件优化排样方法的研究与实现

论述了冲裁件排样材料利用率计算的数学模型和计算机的排样的方法,并给出了采用加密点逐步移动判定法对冲裁件进行单排顺排、单排对排的计算机最优排样算法.以达到节约材料,提高效益的目的.以智能优化算法为基础,给出了单排对排的优化排样算法及实现方法,实验结果证明本文提出的方法是有效的.     

冲裁件智能排样系统的研究

提出Hopfield人工神经网络与遗传算法相结合的算法,进行了智能排样系统的研究。该系统通过读入冲裁零件排样图形,自动计算出排样结果,并输出零件的优化排样图形。实例分析结果表明。该系统不但具有良好界面和人机交互功能,而且排样自动化程度高、提高了材料利用率。     

面向CIMS的板类零件下料工艺系统

介绍了一个以优化排样为核心，为企业板类零件下料生产提供有效整体解决方案的集成工艺设计系统。提出了基于企业局域网、面向GIMS的总体集成设计方案，开发了以优化排样为核心的下料工艺设计流程，设计并实现面向多种下料工艺的优化排样算法。最后以广西某集团为对象介绍了上述思路的具体实施方案及实施效果。     

皮革智能排样系统的开发

随着计算机技术的飞速发展，计算机智能技术也逐渐的被用到皮革生产的排样系统中。介绍了皮革智能排样系统的核心功能模块及其实现。提出了一种新的算法，来处理在不规则和有瑕疵的母板中对不规则形状的样片进行排样的问题。涉及了图形离散化算法、最优匹配算法和碰撞检测算法，并且给出了最终的排样效果图。     

基于粗糙集的矩形件优化填充排样方法研究

为了使排样方案更能满足设计者设计意图,符合企业设计规范,研究了一种基于粗糙集的矩形件优化填充排样方法。在实际的排样工作中,综合考虑工件成本、加工时间、施工效率与设计偏好度这些因素的情况下,提出了一种基于粗糙集理论的矩形件综合价值计算方法,将排样多边形区域划分为多个矩形子段,建立矩形件优化填充排样数学模型,采用基于最优子段排样的动态规划算法求解最大价值,以获取最优布置方案。最后,将其成功应用于铝模板智能排摸设计系统开发中,结合实例验证了该方法的有效性。     

排样智能的现状分析及实施策略

分析了排样研究的现状,提出了智能排样的概念,并对智能排样应解决的关键问题作了一些探讨。     

改进的遗传算法在异形件优化排样中的应用

对于二维不规则图形零件在排样区域上的最优排列，也就是对二维不规则图形的计算机自动排样算法进行优化的问题。采用矩形包络算法、遗传算法和模拟退火算法相结合，寻找排样件在排样时的最优次序及各自的旋转角度。再用基于“最低水平线”策略的启发式排样算法实现二维不规则图形零件自动排样。从而得到满意的优化排样结果。     

改进多边形算法在冲裁优化排样的应用

主要研究冲裁工件排样优化算法及实现问题。多边形算法适于单一形状冲裁工件排样方式的优化，但经实际测试该算法具有通用性差，求解精度不高等缺点。在原算法基础上提出改进的多边形算法，可高效、准确的得出工件排样的最优解，并在AutoCAD上，依据改进算法用ObjectARX开发出优化排样系统。     

蚂蚁算法在矩形件优化排样中的应用

蚂蚁算法是解决优化问题的一种相对轻新的启发式算法，大规模的矩形件优化排样问题是个NP难题。文中尝试用蚂蚁算法求解矩形件优化排样问题，根据提出的求解算法，开发出了基于蚂蚁算法的计算机辅助优化排样系统，并将蚂蚁算法的求解结果和遗传算法进行了对比，试验结果证明了用蚂蚁算法求解矩形件优化排样问题的可行性和有效性。     

一种系统维修性优化分配的方法

针对现有维修性分配方法的不足 ,从系统的经济性分析入手 ,提出了一种以系统相关费用为最小的优化分配方法 ,证明了最优解的存在性 ,并给出了优化分配表达式。最后举例说明了该方法的应用     

A new fuzzy optimal allocation of detuned passive filters based on a Nonhomogeneous Cuckoo Search Algorithm considering resonance constraint

Passive filters are used as one of the effective solutions to mitigate harmonics and improve power quality in electrical networks. In this paper, a new fuzzy approach is proposed for the allocation of detuned passive filters based on a Nonhomogeneous Cuckoo Search Algorithm (NoCuSa). In this method, a resonance index is inserted in the problem formulation to avoid being in a resonance condition after the allocation of passive filters. In this regard, the candidate locations for the installation of passive filters are first selected based on a sensitivity analysis. Then, the values and tuning orders of the passive filters are optimized by using the proposed algorithm for single- and multi-load levels while applying fixed and switched passive filters. In the simulations, different scenarios for optimal allocation of passive filters are investigated and compared with optimal allocation of capacitors. Finally, the fuzzy problem model is implemented on an IEEE 69-bus network by NoCuSa and compared with different optimization algorithms. The results demonstrate an improvement in the final annual net benefit by applying NoCuSa in comparison with other algorithms. In addition, another comparison made between the proposed method and those implemented on IEEE 69-bus system shows the efficiency of the proposed method.     

A study of optimal allocation of computing resources in cloud manufacturing systems

As a new advanced service-oriented networked manufacturing model, cloud manufacturing (CMfg) has been proposed recently. The optimal allocation of computing resources (OACR) is a core part for implementing CMfg. High heterogeneity, high dynamism, and virtualization make the OACR problem more complex than the traditional scheduling problems in grid system or cloud computing system. In this paper, a new comprehensive model for OACR is proposed in the CMfg system. In this model, all main computation, communication, and reliability constraints in the special circumstances are considered. To solve the OACR problem, a new improved niche immune algorithm was presented. Associated with the niche strategy, new heuristics are designed flexibly based on the characteristics of the problem and pheromone is added for adaptive searching. Experiments demonstrate the effectiveness of the designed heuristic information and show NIA’s high performances for addressing the OACR problem compared with other intelligent algorithms.     

粒子群及局部搜索算法在串并联系统结构优化中的应用

将粒子群算法和局部搜索算法相结合,用于解决串并联系统的冗余分配问题。介绍串并联系统结构,确定该冗余分配问题以系统最小费用为优化目标,以系统可用度不能低于某一确定值为约束条件。应用概率生成函数(UGF)方法计算系统可用度,将粒子群算法和局部搜索算法相结合进行优化求解,并给出迭代过程。通过实例对优化迭代过程进行具体说明。     

Product Grouping and Resource Allocation in Multiline Manufacturing Systems

This paper studies the product grouping problem and the resource allocation problem in the design of manufacturing systems where multiple production lines are used to manufacture a range of products. Each production line in the product grouping problem is dedicated to manufacturing a group of products. A mathematical model is developed to determine the number of product groups and the composition of each product group in the manufacturing system. For the resource allocation problem, a mathematical model is developed to determine the optimal resource allocation scheme for each production line and the optimal inventory level for each product. A genetic-based algorithm is proposed to solve the product grouping problem and the resource allocation problem simultaneously, and its results are compared to those of the conventional heuristic approaches. The proposed genetic approach is a simple but effective means of solving these problems.     

基于复杂网络理论的多项目实施资源优化配置研究

针对多项目实施过程中的资源优化配置问题,首先建立了基于复杂网络理论的多项目实施资源配置复杂网络模型,并结合Pajek分析软件对模型参数进行分析研究;其次提出了资源受限下的多项目实施资源配置算法;最后以多项目实施中资源配置为例,结合资源优化配置算法,以资源配置效益最大化为目标函数,对资源受限下的多项目实施资源优化配置问题进行研究,验证了该方法的可行性和有效性.     

Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.     

Multi-objective stochastic programming to solve manpower allocation problem

Effective manpower allocation is among the most vital and complicated decisions for most companies on account of imprecise nature of input information of the problem. This paper presents a novel combination of the chance-constrained programming and the global criterion model for manpower allocation problem that is called chance-constrained global criterion. The proposed model is a deterministic equivalent for the multi-objective stochastic problem of manpower allocation. To illustrate the model, a tri-objective stochastic manpower allocation case problem for determining optimal number of manpower in a job-shop manufacturing system is formulated and solved, and then the competitive advantages of the model are discussed. To have a better judgment on the validity and performance efficiency of the model, 20 different problems are generated and solved. The results show that increasing the size of problem do not have much effect on the number of iterations required for finding the optimal solution, and this model decreases complicacy in modeling the problem.     

自动化立体仓库堆垛机拣选作业调度研究

将堆垛机拣选作业调度归纳为旅行商问题，并从遗传算法角度，研究了求解堆垛机拣选作业调度最优路径的具体方法。经过仿真试验验证，这一方法完全可行。