Development of some cutting stock algorithms

A multivariant scheme of the south-eastern angle method for a two dimensional mass cutting stock problem is discussed in the paper. The use of a flexible linear programming package LP-LGIT for the solution of such problems with various column generators is described. The paralleling of a computational scheme of the method of the generating structures is suggested.     

Branch-and-Price Algorithms for the One-Dimensional Cutting Stock Problem

We compare two branch-and-price approaches for the cutting stock problem. Each algorithm is based on a different integer programming formulation of the column generation master problem. One formulation results in a master problem with 0–1 integer variables while the other has general integer variables. Both algorithms employ column generation for solving LP relaxations at each node of a branch-and-bound tree to obtain optimal integer solutions. These different formulations yield the same column generation subproblem, but require different branch-and-bound approaches. Computational results for both real and randomly generated test problems are presented.     

Solving binary cutting stock problems by column generation and branch-and-bound

We present an algorithm for the binary cutting stock problem that employs both column generation and branch-and-bound to obtain optimal integer solutions. We formulate a branching rule that can be incorporated into the subproblem to allow column generation at any node in the branch-and-bound tree. Implementation details and computational experience are discussed.This research was supported by NSF and AFOSR grant DDM-9115768     

A Semidefinite Programming Based Polyhedral Cut and Price Approach for the Maxcut Problem

We investigate solution of the maximum cut problem using a polyhedral cut and price approach. The dual of the well-known SDP relaxation of maxcut is formulated as a semi-infinite linear programming problem, which is solved within an interior point cutting plane algorithm in a dual setting; this constitutes the pricing (column generation) phase of the algorithm. Cutting planes based on the polyhedral theory of the maxcut problem are then added to the primal problem in order to improve the SDP relaxation; this is the cutting phase of the algorithm. We provide computational results, and compare these results with a standard SDP cutting plane scheme. Research supported in part by NSF grant numbers CCR–9901822 and DMS–0317323. Work done as part of the first authors Ph.D. dissertation at RPI.     

Cutting planes and column generation techniques with the projective algorithm

The problem studied is that of solving linear programs defined recursively by column generation techniques or cutting plane techniques using, respectively, the primal projective method or the dual projective method.This research has been supported in part by FCAR of Quebec, Grant Nos. CE-130 and EQ-3078, by NSERC of Canada, Grant No. A4152, and by the Fonds National Suisse de la Recherche Scientifique, Grant No. 1.467.0.86.     

Computational study of large-scale <Emphasis Type="Italic">p</Emphasis>-Median problems

Given a directed graph G(V,A), the p-Median problem consists of determining p nodes (the median nodes) minimizing the total distance from the other nodes of the graph. We present a Branch-and-Cut-and-Price algorithm yielding provably good solutions for instances with |V|≤3795. Key ingredients of the algorithm are a delayed column-and-row generation technique, exploiting the special structure of the formulation, to solve the LP-relaxation, and cutting planes to strengthen the formulation and limit the size of the enumeration tree.     

On the convergence of a class of outer approximation algorithms for convex programs

This paper presents a new class of outer approximation methods for solving general convex programs. The methods solve at each iteration a subproblem whose constraints contain the feasible set of the original problem. Moreover, the methods employ quadratic objective functions in the subproblems by adding a simple quadratic term to the objective function of the original problem, while other outer approximation methods usually use the original objective function itself throughout the iterations. By this modification, convergence of the methods can be proved under mild conditions. Furthermore, it is shown that generalized versions of the cut construction schemes in Kelley-Cheney-Goldstein's cutting plane method and Veinott's supporting hyperplane method can be incorporated with the present methods and a cut generated at each iteration need not be retained in the succeeding iterations.     

Heuristic for the rectangular two-dimensional single stock size cutting stock problem with two-staged patterns

Two-staged patterns are often used in manufacturing industries to divide stock plates into rectangular items. A heuristic algorithm is presented to solve the rectangular two-dimensional single stock size cutting stock problem with two-staged patterns. It uses the column-generation method to solve the residual problems repeatedly, until the demands of all items are satisfied. Each pattern is generated using a procedure for the constrained single large object placement problem to guarantee the convergence of the algorithm. The computational results of benchmark and practical instances indicate the following: (1) the algorithm can solve most instances to optimality, with the gap to optimality being at most one plate for those solutions whose optimality is not proven and (2) for the instances tested, the algorithm is more efficient (on average) in reducing the number of plates used than a published algorithm and a commercial stock cutting software package.     

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.     

实用下料优化问题模型建立及解法

“下料问题(cuttingstockproblem)”是把相同形状的一些原材料分割加工成若干个不同规格大小的零件的问题,此类问题在工程技术和工业生产中有着重要和广泛的应用.本文首先以材料最省为原则建立模型,采用分层基因算法模型求解出模型的解,若此结果不符合时间限制条件,则通过以客户时间需求为第一目标的分组抽样模型处理后,再借助分层基因算法给出该模型的最优解.     

带交易费用的投资组合模型的割平面解法

本文讨论了带交易费用的投资组合模型，因对这一类带二次约束的线性优化问题没有特殊的处理方法，我们利用割平面法使这一非线性优化间题可通过解一系列线性规划问题来求解．     

Two exact algorithms for the capacitated p-median problem

The p-median problem has been widely studied in combinatorial optimisation, but its generalisation to the capacitated case has not. We propose a branch and price algorithm, comparing it with a standard MIP solver and a branch and bound algorithm based on Lagrangean relaxation. We present computational experience, using test instances drawn from the literature and new instances with higher ratio between the number of medians p and the number of nodes N. The branch and price algorithm shows very good performances and computational time robustness in solving problems for any ratio.Received: December 2002, Revised: August 2003AMS classification: 90C10, 90C27     

带转向延误的非对称用户平衡模型与算法

为准确刻画交通网络和出行行为的复杂特征,考虑路口的转向延误及路段之间相互作用的非对称性因素,用非线性互补理论建立了带转向延误的非对称用户平衡模型,分析了用户平衡解的存在性.结合列生成算法采用有效路径集来避免枚举路网中所有路径的优点和FBLSA算法求解非线性互补问题的全局收敛性特点,提出了修正FBLSA算法.最后针对一个中等规模的交通网络进行数值实验,结果显示该算法对处理非对称网络是十分有效的.     

Selective Gram–Schmidt orthonormalization for conic cutting surface algorithms

It is not straightforward to find a new feasible solution when several conic constraints are added to a conic optimization problem. Examples of conic constraints include semidefinite constraints and second order cone constraints. In this paper, a method to slightly modify the constraints is proposed. Because of this modification, a simple procedure to generate strictly feasible points in both the primal and dual spaces can be defined. A second benefit of the modification is an improvement in the complexity analysis of conic cutting surface algorithms. Complexity results for conic cutting surface algorithms proved to date have depended on a condition number of the added constraints. The proposed modification of the constraints leads to a stronger result, with the convergence of the resulting algorithm not dependent on the condition number. Research supported in part by NSF grant number DMS-0317323. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.     

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 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.     

A polyhedral approach to a production planning problem

Production planning in manufacturing industries is concerned with the determination of the production quantities (lot sizes) of some items over a time horizon, in order to satisfy the demand with minimum cost, subject to some production constraints. In general, production planning problems become harder when different types of constraints are present, such as capacity constraints, minimum lot sizes, changeover times, among others. Models incorporating some of these constraints yield, in general, NP-hard problems. We consider a single-machine, multi-item lot-sizing problem, with those difficult characteristics. There is a natural mixed integer programming formulation for this problem. However, the bounds given by linear relaxation are in general weak, so solving this problem by LP based branch and bound is inefficient. In order to improve the LP bounds, we strengthen the formulation by adding cutting planes. Several families of valid inequalities for the set of feasible solutions are derived, and the corresponding separation problems are addressed. The result is a branch and cut algorithm, which is able to solve some real life instances with 5 items and up to 36 periods. This revised version was published online in June 2006 with corrections to the Cover Date.     

Solving a continuous local access network design problem with a stabilized central column generation approach

In this paper, we focus on a variant of the multi-source Weber problem. In the multi-source Weber problem, the location of a fixed number of concentrators, and the allocation of terminals to them, must be chosen to minimize the total cost of links between terminals and concentrators. In our variant, we have a third hierarchical level, two categories of link costs, and the number of concentrators is unknown. To solve this difficult problem, we propose several heuristics, and use a new stabilized column generation approach, based on a central cutting plane method, to provide lower bounds.     

Cutting Sphere Algorithm

We present a generalization of the conventional cutting plane algorithm for the solution of nonconvex optimization problems with nonsmooth inequality constraints. The cuts are effected using spheres rather than hyperplanes.     

On the generalization of ECP and OA methods to nonsmooth convex MINLP problems

In this article, generalization of some mixed-integer nonlinear programming algorithms to cover convex nonsmooth problems is studied. In the extended cutting plane method, gradients are replaced by the subgradients of the convex function and the resulting algorithm shall be proved to converge to a global optimum. It is shown through a counterexample that this type of generalization is insufficient with certain versions of the outer approximation algorithm. However, with some modifications to the outer approximation method a special type of nonsmooth functions for which the subdifferential at any point is a convex combination of a finite number of subgradients at the point can be considered. Numerical results with extended cutting plane method are also reported.