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.     

二维不规则形状计算机自动排米系统CATLS的研究与设计

讨论了二维不规则形状计算机自动排料系统ＣＡＴＬＳ的思想和实现技术，介绍了系统的组成、数据结构及工作过程，并针对所提出的自动排料算法进行了深入的探讨和研究。     

改进的蚂蚁算法在矩形优化排料中的应用

矩形件优化排料问题是一类具有NP完全难度的组合优化问题.将改进的蚂蚁算法应用到矩形件优化排料问题求解中,优化试验结果表明可获得比基本蚂蚁算法更好的效果,为矩形件优化排料这类NP完全问题提供了新的思路和方法.     

基于MVC模式的铁制工艺品排料系统的研究

本文在分析铁制工艺品排料问题需求的基础上，提出一种基于MVC模式的排料系统。对其设计思想和关键技术进行了较深入的研究和阐述。所设计开发的排料系统已经投入试用。     

二维不规则形状自动排料算法的研究与实现

针对二维不规则形状自动排料问题,给出了解决自动排料的求解思路,并对自动排料的主要算法作出了研究和改进,以便更好地提高算法效率及可靠性。     

一种实用的铁制工艺品排料方案的设计与实现

根据铁制工艺品企业的需求,结合前人提出的求解矩形Packing问题的贪心算法和矩形排料的十字线法,本文设计实现了一种实用的铁制工艺品排料方法。实验结果表明,本文所实现的排料方法运行速度较快,具有较强的可用性和实用性。系统已在有关企业投入试用。     

自动排料中关键算法的研究

通过基于演化计算的全局搜索,寻找零件在排料时的最优次序及各自的最佳旋转角度,并采用最低最左原则(BL,Bottom-Left)实现自动排料.文中通过对自动排料中排料件图形的表示及数据结构描述,以及关键算法的实现讨论,将在处理不规则多边形的排料优化问题中判断点是否在多边形内部的通用方法射线法与多边形的单调性相结合,提出一种改进算法,并通过实验获得了较好的结果和性能.     

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。     

优化排料算法的研究与实现

本文首先介绍了优化排料算法的实现过程。为了降低算法复杂度，本算法采用了启发式搜索的方法，进而分析了算法的可行性。在此基础上研制出了二维图形优化排料系统。大量生产实践表明，本系统具有实用、高效等特点。     

切板机计算机辅助排料软件系统的实现

有约束的矩形排料问题是将矩形零件按沿直线切割工艺要求在原料上进行最优布局 ,使得原料的废料最少。针对这种排料问题 ,对零件和原料种类和数量有限和无限的情况 ,采用启发式的方法 ,进行有效的搜索 ,以减少计算时间 ,确保最优的排料结果的算法 ,同时还介绍了在排料实现中一些问题的处理。     

一维下料的改进遗传算法优化

针对一维下料优化问题,在对一维下料方案数学模型分析的基础上,提出了基于改进遗传算法的优化求解方案。主要思想是把零件的一个顺序作为一种下料方案,定义了遗传算法中的关键问题:编码、解码方法、遗传算子和适应度函数的定义。该算法设计了一种新颖的遗传算子,包括顺序交叉算子、线性变异算子、扩展选择算子。根据这一算法开发出了一维下料方案的优化系统。实际应用表明,该算法逼近理论最优值,而且收敛速度快,较好地解决了一维下料问题。     

The generalized assortment and best cutting stock length problems

This paper introduces two new one-dimensional cutting stock models: the generalized assortment problem (GAP) and the best cutting stock length (BSL) problem. These new models provide the potential to reduce waste to values lower than the optimum of current models, under the right management circumstances. In the GAP, management has a standard length and can select one or more of any additional custom stock lengths, and management wishes to minimize cutting stock waste. This model is different from existing models that assume that the selection is from a small fixed set of stock lengths. In the BSL problem, management chooses any number of custom stock lengths, but wishes to find the fewest custom stock lengths in order to have zero waste. Results show waste reductions of 80% with just one custom stock length compared with solutions from standard cutting stock formulations, when item lengths are long relative to the stock length. The models are most effective when the item lengths are nearly as long as the stock length. Solutions from the model have been implemented for a manufacturer. The model is easily generalized to allow multiple existing stock lengths and different costs.     

多尺寸圆木二维下料问题研究

圆木二维下料问题是木材企业中常见问题,针对一些头部与尾部直径相差不大的木材,可以将这些木材看作是圆柱体,下料时将其切成和圆木长度相等的多个长方体毛坯,该问题可转化为二维下料问题。采用顺序价值校正框架和动态规划算法求解该下料问题。顺序生成排样图,每生成一个排样图便调整毛坯的价值,重复该过程直到满足毛坯需求为止。通过迭代生成多个下料方案以便优选。圆木下料的研究对减少木材企业的成本很有意义。     

Exact Solution of Cutting Stock Problems Using Column Generation and Branch-and-Bound

This paper describes an attempt to solve the one-dimensional cutting stock problem exactly, using column generation and branch-and-bound. A new formulation is introduced for the one-dimensional cutting stock problem that uses general integer variables, not restricted to be binary. It is an arc flow formulation with side constraints, whose linear programming relaxation provides a strong lower bound. In this model, a cutting pattern, which corresponds to a path, is decomposed into single arc variables. The decomposition serves the purpose of showing that it is possible to combine the branch-and-bound method with variable generation. Computational times are reported for one-dimensional cutting stock instances with a number of orders up to 30.     

Strips minimization in two-dimensional cutting stock of circular items

Circular items are often produced from stock plates using the cutting and stamping process that consists of two stages. A guillotine machine divides the plate into strips at the cutting stage, and then a press punches out the items from the strips at the stamping stage. The cutting cost at the first stage often increases with the number of strips in the cutting plan. An approach is presented for the two-dimensional cutting stock problem of the strips at the cutting stage. The objective is to minimize the sum of the material and the cutting costs. The approach formulates the problem as an integer linear programming, and uses a column generation method for generating the cutting patterns. The cutting patterns have the feature that each cut on the plate produces just one strip. The computational results indicate that the approach can greatly reduce the number of strips in the cutting plan.     

改进非支配排序进化算法在下料问题中的应用

针对一维下料问题,提出了减少废料、减少下料设置时间和减少可回收余料的三目标优化模型,用改进的非支配排序进化算法求出问题的Pareto最优解集,运用逼近理想解方法从解集中选出一个满意解作为下料方案,各优化目标的权重用CRITIC法算出。仿真实验证明了所提出的方法可以有效解决该类多目标下料问题。     

有限制二维板材启发式下料算法研究

橱柜及板式家具生产都涉及二维板材下料,材料利用率的最大化一直是该类企业追求的目标。本文提出了基于启发式规则的有限制二维板材下料算法。通过在橱柜生产过程中自动下料系统的实施和理论分析,该算法是实用有效的。     

New lower bounds based on column generation and constraint programming for the pattern minimization problem

The pattern minimization problem is a cutting and packing problem that consists in finding a cutting plan with the minimum number of different patterns. This objective may be relevant when changing from one pattern to another involves a cost for setting up the cutting machine. When the minimization of the number of different patterns is done by assuming that no more than the minimum number of rolls can be used, the problem is also referred to as the cutting stock problem with setup costs.     

Evaluation of algorithms for one-dimensional cutting

The paper deals with the problem of evaluating and comparing different one-dimensional stock cutting algorithms regarding trim loss. Different types of problems are identified. An evaluation method is developed which enables a comparison of solutions of all types of problems. A practical example of this methods implementation is presented.Scope and purposeThere are many algorithms and methods for one-dimensional stock cutting with different factors that need to be taken into account. Therefore a general comparison between them is very difficult if not impossible. However, if we assume that trim loss is the most important factor common to different methods, we can overcome this problem by limiting the comparison to trim loss. In different cutting stock problems and in different approaches to them trim loss is defined differently. For the comparison of different solutions to be possible, we need to find a common definition to the trim loss. Such a general definition is introduced by the General One-Dimensional Cutting Stock Problem type (G1D-CSP). In this paper, a problem generator algorithm PGEN for G1D-CSP is presented and the method for evaluation and comparison of different one-dimensional cutting stock algorithms is proposed.     

On the one-dimensional stock cutting problem in the paper tube industry

The one-dimensional cutting stock problem (1D-CSP) is one of the representative combinatorial optimization problems which arises in many industrial applications. Although the primary objective of 1D-CSP is to minimize the total length of used stock rolls, the efficiency of cutting processes has become more important in recent years. The crucial bottleneck of the cutting process often occurs at handling operations in semiautomated manufacturers such as those in the paper tube industry. To reduce interruptions and errors at handling operations in the paper tube industry, we consider a variant of 1D-CSP that minimizes the total length of used stock rolls while constraining (C1) the number of setups of each stock roll type, (C2) the combination of piece lengths occurring in open stacks simultaneously, and (C3) the number of open stacks. For this problem, we propose a generalization of the cutting pattern called the “cutting group,” which is a sequence of cutting patterns that satisfies the given upper bounds of setups of each stock roll type and open stacks. To generate good cutting groups, we decompose the 1D-CSP into a number of auxiliary bin packing problems. We develop a tabu search algorithm based on a shift neighborhood that solves the auxiliary bin packing problems by the first-fit decreasing heuristic algorithm. Experimental results show that our algorithm improves the quality of solutions compared to the existing algorithm used in a paper tube factory.