An integrated approach to the one-dimensional cutting stock problem in coronary stent manufacturing

This paper presents a two-stage approach for pattern generation and cutting plan determination of the one-dimensional cutting stock problem. Calculation of the total number of patterns that will be cut and generation of the cutting patterns are performed in the first stage. On the other hand, the second stage determines the cutting plan. The proposed approach makes use of two separate integer linear programming models. One of these models is employed by the first stage to generate the cutting patterns through a heuristic procedure with the objective of minimizing trim loss. The cutting patterns obtained from Stage 1 are then fed into the second stage. In this stage, another integer linear programming model is solved to form a cutting plan. The objective of this model is to minimize a generalized total cost function consisting of material inputs, number of setups, labor hours and overdue time; subject to demand requirements, material availability, regular and overtime availability, and due date constraints. The study also demonstrates an implementation of the proposed approach in a coronary stent manufacturer. The case study focuses on the cutting phase of the manufacturing process followed by manual cleaning and quality control activities. The experiments show that the proposed approach is suitable to the conditions and requirements of the company.     

Accelerating column generation for variable sized bin-packing problems

In this paper, we study different strategies to stabilize and accelerate the column generation method, when it is applied specifically to the variable sized bin-packing problem, or to its cutting stock counterpart, the multiple length cutting stock problem. Many of the algorithms for these problems discussed in the literature rely on column generation, processes that are known to converge slowly due to primal degeneracy and the excessive oscillations of the dual variables. In the sequel, we introduce new dual-optimal inequalities, and explore the principle of model aggregation as an alternative way of controlling the progress of the dual variables. Two algorithms based on aggregation are proposed. The first one relies on a row aggregated LP, while the second one solves iteratively sequences of doubly aggregated models. Working with these approximations, in the various stages of an iterative solution process, has proven to be an effective way of achieving faster convergence.     

A Center Cutting Plane Algorithm for a Likelihood Estimate Problem

We describe a method for solving the maximum likelihood estimate problem of a mixing distribution, based on an interior cutting plane algorithm with cuts through analytic centers. From increasingly refined discretized statistical problem models we construct a sequence of inner non-linear problems and solve them approximately applying a primal-dual algorithm to the dual formulation. Refining the statistical problem is equivalent to adding cuts to the inner problems.     

A fractional algorithm for optimal cutting of lumber into dimension parts

A fractional algorithm is described which optimizes the cutting of boards or lumber into dimension parts. The model is an extension of previously developed models and is purposely designed for cutting scenarios where the customer order for the dimension parts can be satisfied within a given range, i.e., flexible rather than exact demand. An illustrative example is presented simply to describe the model and compare results between the standard procedure and the modified procedure proposed in this paper.     

An algorithm for the two-dimensional assortment problem

In this paper we consider the two-dimensional assortment problem. This is the problem of choosing from a set of stock rectangles a subset which can be used for cutting into a number of smaller rectangular pieces. Constraints are imposed upon the number of such pieces which result from the cutting.A heuristic algorithm for the guillotine cutting version of the problem is developed based on a greedy procedure for generating two-dimensional cutting patterns, a linear program for choosing the cutting patterns to use and an interchange procedure to decide the best subset of stock rectangles to cut.Computational results are presented for a number of test problems which indicate that the algorithm developed produces good quality results both for assortment problems and for two-dimensional cutting problems.     

凸多边形的最优切割策略

本文研究的是在一个平面区域内切割出一个预定的凸多边形的最优策略问题 .首先应用动态规划建立模型 ,然后 ,证明了优化变换的两个准则 ,最后 ,我们对极先切割边进行了讨论 ,得出了简明的最优切割策略     

关于“截断切割”的最优设计

针对 1 997年全国大学生数学建模竟赛 B题 ,对于换刀费用 e=0的情况 ,本文设计了一种异常简捷的切割厚度排序法来寻找最优切割方案 ,同时在数学上给出了严格的证明 .对于换刀费用 e≠ 0的情况 ,以 e=0时得到的最优切割方案为基础 ,先通过简单的调整原则寻找出限定不同换刀次数时各自的最优切割方案 ,再通过费用比较便可简捷地得到随 e值的大小而变化的最优切割方案 .本文构造的模型在求解时无须用计算机编程 ,只用手算即可简捷地得到答案     

A comparative numerical analysis for the guillotine two-dimensional cutting problem

In this work, the behavior of four algorithms in the resolution of the two-dimensional constrained guillotine cutting problem is analyzed. This problem is concerned about the way a set of pieces should be cut from a plate of greater dimensions, considering guillotine cutting and a constrained number of times a piece can be cut from the plate. In this study three combinatorial and two heuristic methods are considered. In the combinatorial methods from the set of pieces, a minimum loss layout is constructively generated based on Wang's algorithm. In addition, an evolutionary and an annealing type approach are considered. All of these models have been implemented on a high performance Silicon Graphics machine. Performance of each algorithm is analyzed both in terms of percentage waste and running time. In order to do that, a set of 1000 instances are classified according to their combinatorial degree and subsequently evaluated. This revised version was published online in June 2006 with corrections to the Cover Date.     

Primal cutting plane algorithms revisited

Dual fractional cutting plane algorithms, in which cutting planes are used to iteratively tighten a linear relaxation of an integer program, are well-known and form the basis of the highly successful branch-and-cut method. It is rather less well-known that various primal cutting plane algorithms were developed in the 1960s, for example by Young. In a primal algorithm, the main role of the cutting planes is to enable a feasible solution to the original problem to be improved. Research on these algorithms has been almost non-existent.  In this paper we argue for a re-examination of these primal methods. We describe a new primal algorithm for pure 0-1 problems based on strong valid inequalities and give some encouraging computational results. Possible extensions to the case of general mixed-integer programs are also discussed.     

Global Minimization of Increasing Positively Homogeneous Functions over the Unit Simplex

In this paper we study a method for global optimization of increasing positively homogeneous functions over the unit simplex, which is a version of the cutting angle method. Some properties of the auxiliary subproblem are studied and a special algorithm for its solution is proposed. A cutting angle method based on this algorithm allows one to find an approximate solution of some problems of global optimization with 50 variables. Results of numerical experiments are discussed.     

Cutting Plane/Tabu Search Algorithms for Low Rank Concave Quadratic Programming Problems

In this paper, we will propose an efficient heuristic algorithm for solving concave quadratic programming problems whose rank of the objective function is relatively small. This algorithm is a combination of Tuy's cutting plane to eliminate the feasible region and a kind of tabu-search method to find a good vertex. We first generate a set of V of vertices and select one of these vertices as a starting point at each step, and apply tabu-search and Tuy's cutting plane algorithm where the list of tabu consists of those vertices eliminated by cutting planes and those newly generated vertices by cutting planes. When all vertices of the set V are eliminated, the algorithm is terminated. This algorithm need not converge to a global minimum, but it can work very well when the rank is relatively small (up to seven). The incumbent solutions are in fact globally optimal for all tested problems. We also propose an alternative algorithm by incorporating Rosen's hyperrectangle cut. This algorithm is more efficient than the combination of Tuy's cutting plane and tabu-search.     

有交货时间限制的大规模实用下料问题

研究的是有交货时间限制的单一原材料下料问题(规模较大).对于一维下料问题,本文得到一个有各自交货时间的模型.针对该模型提出一种新的算法:DP贪婪算法.计算结果是总用料800根即可完成需求任务,材料利用率为99.6%.对于二维下料问题,在一维的基础上建立了二维的求解模型,运用我们自己设计的降维思想结合一维的DP贪婪算法,给出解决该模型的算法.计算结果是总用料451块即可完成需求任务,材料利用率位99.2%.算法设计时考虑了普遍的情况,所以算法在解决大多数实际下料问题,特别是大规模下料问题时是切实有效的.     

A tabu search algorithm for a two-dimensional non-guillotine cutting problem

In this paper we study a two-dimensional non-guillotine cutting problem, the problem of cutting rectangular pieces from a large stock rectangle so as to maximize the total value of the pieces cut. The problem has many industrial applications whenever small pieces have to be cut from or packed into a large stock sheet. We propose a tabu search algorithm. Several moves based on reducing and inserting blocks of pieces have been defined. Intensification and diversification procedures, based on long-term memory, have been included. The computational results on large sets of test instances show that the algorithm is very efficient for a wide range of packing and cutting problems.     

Embedding optimisation algorithms with Mosel

Algebraic modelling languages allow models to be implemented in such a way that they can easily be understood and modified. They are therefore a working environment commonly used by practitioners in Operations Research. Having once developed models, they need to be integrated inside the company information system. This step often involves embedding a model into a programming language environment: many existing algebraic modelling languages make possible to run parameterised models and subsequently retrieve their results, but without any facility for interacting with the model during the model generation or solution process.In this paper we show how to use the Mosel environment to implement complex algorithms directly in the modelling language.The Office cleaning problem is solved by a branch-and-cut algorithm, implemented entirely in the modelling language (including the definition of the callback function for the solver). Secondly, a cutting stock problem is solved by column generation, also implemented in the modelling language.AMS classification: 90Cxx, 65K05, 68N15     

下料问题的优化模型及其快速算法

结合生产实际中具体的下料问题,本文建立了该类问题的优化模型,并提出下料方式的遴选三准则,即高利用率优先准则,长度优先准则和时间优先准则.运用本文的算法对一维下料的利用率高达99.6%,机器时间4秒.对二维的利用率为98.9%,机器时间约7秒.     

截断切割问题的参数规划模型

对一个截断切割问题,本文给出了一个参数网络规划模型,总结了[3]中的解法,并给出了一个实例.     

Alternative configurations for cutting machines in a tube cutting mill

In a steel tube mill where an endless stream of steel tube is supplied from a manufacturing facility, trim waste is never made regardless of cutting patterns used and the standard cutting stock problem seems meaningless. Therefore, the continuous stock cutting problem with setup is introduced to minimize the sum of cutting time and pattern changing time to meet the given demand. We propose a new configuration of cutting machines to achieve higher production efficiency, namely the open-ended configuration as opposed to the traditional closed-ended configuration, thereby two variants of the problem are defined. We propose linear formulations for both problems using binary expansion of the number of pieces of different types in a pattern. Furthermore, we define the time for pattern change as a linear function of the number of knives used in the pattern to be more realistic. Computational studies suggest that the open-ended cutting machine may improve the production time by up to 44% and that our linear formulations are more efficient than the existing ones.     

自动化车床最优刀具检测模型研究

针对自动化车床工序最优检测和刀具更换问题进行了探讨.将定期检测和将刀具更换作用于同一工序流程,在只考虑刀具故障条件下,通过概率论和更新过程理论建立了以单位时间内期望费用为目标函数的数学模型,以检测间隔和刀具更换间隔为策略,确定最优的策略使得目标函数达到最小,并求出了经长期运行单位时间内期望费用的明显表达式.最后还对结果进行了讨论.     

Generalized fractional programming and cutting plane algorithms

In this paper, we introduce a variant of a cutting plane algorithm and show that this algorithm reduces to the well-known Dinkelbach-type procedure of Crouzeix, Ferland, and Schaible if the optimization problem is a generalized fractional program. By this observation, an easy geometrical interpretation of one of the most important algorithms in generalized fractional programming is obtained. Moreover, it is shown that the convergence of the Dinkelbach-type procedure is a direct consequence of the properties of this cutting plane method. Finally, a class of generalized fractional programs is considered where the standard positivity assumption on the denominators of the ratios of the objective function has to be imposed explicitly. It is also shown that, when using a Dinkelbach-type approach for this class of programs, the constraints ensuring the positivity on the denominators can be dropped.The authors like to thank the anonymous referees and Frank Plastria for their constructive remarks on an earlier version of this paper.This research was carried out at Erasmus University, Rotterdam, The Netherlands and was supported by JNICT, Lisboa, Portugal, under Contract BD/707/90-RM.