An algorithm for a cutting stock problem on a strip

A cutting stock problem is formulated as follows: a set of rectangular pieces must be cut from a set of sheets, so as to minimize total waste. In our problem the pieces are requested in large quantities and the set of sheets are long rolls of material. For this class of problems we have developed a fast heuristic based on partial enumeration of all feasible patterns. We then tested the effectiveness on a set of test problems ranging from practical to random instances. Finally, the algorithm has been applied to check the asymptotic behaviour of the solution when a continuous stream of pieces is requested and cutting decisions are to be made while orders are still arriving.     

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.     

Column generation and sequential heuristic procedure for solving an irregular shape cutting stock problem

The research addressing two-dimensional (2D) irregular shape packing has largely focused on the strip packing variant of the problem. However, it can be argued that this is a simplification. The materials from which pieces are required to be cut will ultimately have a fixed length either due to the physical dimensions of the material or through constraints on the cutting machinery. Hence, in order to cut all the pieces, multiple sheets may be required. From this scenario arises the 2D irregular shape cutting stock problem. In this paper, we will present implementations of cutting stock approaches adapted to handle irregular shapes, including two approaches based on column generation (CG) and a sequential heuristic procedure. In many applications, setup costs can be reduced if the same pattern layout is cut from multiple sheets; hence there is a trade-off between material waste and number of patterns. Therefore, we describe the formulation and implementation of an adaptation of the CG method to control the number of different patterns. CG is a common method for the cutting stock problem; however, when the pieces are irregular the sub-problem cannot be solved optimally. Hence we implement CG and solve the sub-problem using the beam search heuristic. Further, we introduce a version of CG for instances where the number of rows is less than the number of columns.     

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.     

A Practical Approach for Determining Rectangular Stock Sizes

In this paper we address the problem of determining what rectangular sizes should be stocked in order to satisfy a bill of materials composed of smaller rectangles. As is common in many manufacturing operations, the stock material will be cut using two-staged guillotine cutting patterns. We first generate a large number of stock sizes ideally suited to the bill of materials. Then we use a facility location algorithm to consolidate the stock sizes down to an acceptable number. Given the solution of what rectangular sizes to stock, a second program is used to map bills of materials into the stock rectangles. The effectiveness of our approach is demonstrated by generating stock sizes for a real-world bill of materials with 392 distinct order sizes and over 7700 order pieces.     

A GRASP algorithm for constrained two-dimensional non-guillotine cutting problems

This paper presents a greedy randomized adaptive search procedure (GRASP) for the constrained two-dimensional non-guillotine cutting problem, the problem of cutting the rectangular pieces from a large rectangle so as to maximize the value of the pieces cut. We investigate several strategies for the constructive and improvement phases and several choices for critical search parameters. We perform extensive computational experiments with well-known instances previously reported, first to select the best alternatives and then to compare the efficiency of our algorithm with other procedures.     

T-shape homogenous block patterns for the two-dimensional cutting problem

This paper presents an algorithm for unconstrained T-shape homogenous block cutting patterns of rectangular pieces. A vertical cut divides the stock sheet into two segments. Each segment consists of sections that have the same length and direction. A section contains a row of homogenous blocks. A homogenous block consists of homogenous strips of the same piece type. Each cut on the block produces just one strip. The directions of two strips cut successively from a block are either parallel or orthogonal. The algorithm uses a dynamic programming recursion to generate optimal blocks, solves knapsack problems to obtain the block layouts on the sections and the section layout on segments of various lengths, and optimally selects two segments to compose the cutting pattern. The computational results indicate that the algorithm is efficient in improving material usage, and the computation time is reasonable.     

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.     

Algorithms for Unconstrained Two-Dimensional Guillotine Cutting

In this paper we consider the unconstrained, two-dimensional, guillotine cutting problem. This is the problem that occurs in the cutting of a number of rectangular pieces from a single large rectangle, so as to maximize the value of the pieces cut, where any cuts that are made are restricted to be guillotine cuts.We consider both the staged version of the problem (where the cutting is performed in a number of distinct stages) and the general (non-staged) version of the problem.A number of algorithms, both heuristic and optimal, based upon dynamic programming are presented. Computational results are given for large problems.     

Multi-job Cutting Stock Problem with Due Dates and Release Dates

The common feature of cutting stock problems is to cut some form of stock materials to produce smaller pieces of materials in quantities matching orders received. Most research on cutting stock problems focuses on either generating cutting patterns to minimize wastage or determining the required number of stock materials to meet orders. In this paper, we examine a variation of cutting stock problems that arises in some industries where meeting orders' due dates is more important than minimizing wastage of materials. We develop two two-dimensional cutting stock models with due date and release date constraints. Since adding due dates and release dates makes the traditional cutting stock problem even more difficult to solve, we develop both LP-based and non-LP-based heuristics to obtain good solutions. The computational results show that the solution procedures are easy to implement and work very well.     

Simple block patterns for the two-dimensional cutting problem

This paper presents an algorithm for the unconstrained two-dimensional cutting problem of rectangular pieces. It proposes the simple block (SB) pattern consisting of simple blocks. The SB pattern is defined recursively. Each cut on the stock plate produces just one simple block. A horizontal cut produces a horizontal block with width equal to that of the leftmost piece in the block. A vertical cut produces a vertical block with length equal to that of the bottommost piece in the block. The algorithm generates the optimal SB pattern recursively, and selects optimally the first piece in each block. It uses upper bound to prune some unpromising branches during the searching process. The computational results indicate that the algorithm is highly efficient in improving material utilization, and the computation time is reasonable.     

A solution procedure for a pattern sequencing problem as part of a one-dimensional cutting stock problem in the steel industry

To cut reinforcing bars for concrete buildings, machines are used which have compartments to store the cut orders until the requirement is met. Number and size of these compartments restrict kind and processing sequence of possible cutting patterns. In this paper we present the so-called “Sequencing algorithm” that tackles the problem of finding a processing sequence for the cutting patterns starting from an integer solution of the cutting stock problem and using an interpretation of relations between orders in patterns as a graph. Computational results are reported.     

Fast heuristic for constrained homogenous T-shape cutting patterns

Homogenous T-shape (HTS) cutting patterns are welcomed when the two-phase process is used to produce rectangular pieces from the stock plate, where the plate is cut into homogenous strips at the first phase, and the strips are divided into pieces at the second phase. A heuristic is presented for generating constrained HTS patterns, where the objective is to maximize the pattern value that is equal to the total value of the included pieces, observing the upper bound constraint on the frequency of each piece type. The heuristic is based on dynamic programming and branch-and-bound techniques. It can yield solutions close to optimal with short computation time. By providing good initial solutions, the heuristic can greatly improve the time efficiency of an existing exact branch-and-bound algorithm.     

Bounds for Two-Dimensional Cutting

This note considers the problem of cutting rectangular pieces from a single large rectangle so as to maximize the value of the pieces cut. A number of bounds that can be used in any tree search procedure for the problem are derived from a zero-one formulation of the problem. Computational results are presented.     

Two-dimensional Cutting Stock with Multiple Stock Sizes

In this paper an algorithm for a cutting stock problem in the wood industry is presented. Cuts are of guillotine type and requirements have to be met exactly, i.e. no over- or under-production is allowed. There are several different board sizes from which panels can be cut and the problem is to find the best mix of boards and respective cutting patterns that satisfies the demand for panels with minimum wastage. The heuristic algorithm uses a pattern-building procedure combined with an enumeration scheme for the mix of boards.     

New integer programming formulations and an exact algorithm for the ordered cutting stock problem

Apart from trim loss minimization, there are many other issues concerning cutting processes that arise in real production systems. One of these is related to the number of stacks that need to be opened near the cutting machines. Many researchers have worked in the last years on cutting stock problems with additional constraints on the number of open stacks. In this paper, we address a related problem: the Ordered Cutting Stock Problem (OCSP). In this case, a stack is opened for every new client's order, and it is closed only when all the items of that order are cut. The OSCP has been introduced recently in the literature. Our aim is to provide further insight into this problem. This paper describes three new integer programming formulations for solving it, and an exact algorithm based on column generation, branch-and-bound and cutting planes. We report on computational experiments on a set of random instances. The results show that good lower bounds can be computed quickly, and that optimal solutions can be found in a reasonable amount of time.     

A branch-and-price algorithm for the two-stage guillotine cutting stock problem

We investigate the two-stage guillotine two-dimensional cutting stock problem. This problem commonly arises in the industry when small rectangular items need to be cut out of large stock sheets. We propose an integer programming formulation that extends the well-known Gilmore and Gomory model by explicitly considering solutions that are obtained by both slitting some stock sheets down their widths and others down their heights. To solve this model, we propose an exact branch-and-price algorithm. To the best of our knowledge, this is the first contribution with regard to obtaining integer optimal solutions to Gilmore and Gomory model. Extensive results, on a set of real-world problems, indicate that the proposed algorithm delivers optimal solutions for instances with up to 809 items and that the hybrid cutting strategy often yields improved solutions. Furthermore, our computational study reveals that the proposed modelling and algorithmic strategy outperforms a recently proposed arc-flow model-based solution strategy.     

Application of optimization for solving a sawing stock problem with a cant sawing pattern

This paper introduces an optimization approach for solving the sawing stock problem in a sawmill in Brazil using the cant sawing pattern, in which lateral and central pieces are cut from the log surface. As this problem has been proved NP-hard and involves some nonlinearities due to the circular geometry of this pattern, we developed a solution method based on two stages. First, we developed an algorithm to generate all sawing patterns, considering the available log diameters and the demanded lumbers. Next, two integer linear programming models were formulated to optimize the number of sawing patterns to be cut, fulfilling the demand in the planning horizon and attending the amount of logs in inventory. One model minimizes the wood loss, while the other maximizes the sales revenue. The optimization approach was evaluated using real data from the sawmill, obtaining significant reductions in the volume cut, in comparison with the current manual planning process, while completely fulfilling the demand.     

New model and heuristic solution approach for one-dimensional cutting stock problem with usable leftovers

In the one-dimensional cutting stock problem with usable leftovers (1DCSPUL), items of the current order are cut from stock bars to minimize material cost. Here, stock bars include both standard ones bought commercially and old leftovers generated in processing previous orders, and cutting patterns often include new leftovers that are usable in processing subsequent orders. Leftovers of the same length are considered to be of the same type. The number of types of leftovers should be limited to simplify the cutting process and reduce the storage area. This paper presents an integer programming model for the 1DCSPUL with limited leftover types and describes a heuristic algorithm based on a column-generation procedure to solve it. Computational results show that the proposed approach is more effective than several published algorithms in reducing trim loss, especially when the number of types of leftovers is limited.