Heuristic approaches to large-scale periodic packing of irregular shapes on a rectangular sheet

The nesting problem is a two-dimensional cutting and packing problem where the small pieces to cut have irregular shapes. A particular case of the nesting problem occurs when congruent copies of one single shape have to fill, as much as possible, a limited sheet. Traditional approaches to the nesting problem have difficulty to tackle with high number of pieces to place. Additionally, if the orientation of the given shape is not a constraint, the general nesting approaches are not particularly successful. This problem arises in practice in several industrial contexts such as footwear, metalware and furniture. A possible approach is the periodic placement of the shapes, in a lattice way. In this paper, we propose three heuristic approaches to solve this particular case of nesting problems. Experimental results are compared with published results in literature and additional results obtained from new instances are also provided.     

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.     

Construction heuristics for two-dimensional irregular shape bin packing with guillotine constraints

The paper examines a new problem in the irregular packing literature that has many applications in industry: two-dimensional irregular (convex) bin packing with guillotine constraints. Due to the cutting process of certain materials, cuts are restricted to extend from one edge of the stock-sheet to another, called guillotine cutting. This constraint is common place in glass cutting and is an important constraint in two-dimensional cutting and packing problems. In the literature, various exact and approximate algorithms exist for finding the two dimensional cutting patterns that satisfy the guillotine cutting constraint. However, to the best of our knowledge, all of the algorithms are designed for solving rectangular cutting where cuts are orthogonal with the edges of the stock-sheet. In order to satisfy the guillotine cutting constraint using these approaches, when the pieces are non-rectangular, practitioners implement a two stage approach. First, pieces are enclosed within rectangle shapes and then the rectangles are packed. Clearly, imposing this condition is likely to lead to additional waste. This paper aims to generate guillotine-cutting layouts of irregular shapes using a number of strategies. The investigation compares three two-stage approaches: one approximates pieces by rectangles, the other two approximate pairs of pieces by rectangles using a cluster heuristic or phi-functions for optimal clustering. All three approaches use a competitive algorithm for rectangle bin packing with guillotine constraints. Further, we design and implement a one-stage approach using an adaptive forest search algorithm. Experimental results show the one-stage strategy produces good solutions in less time over the two-stage approach.     

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.     

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.     

Mixed integer quadratically-constrained programming model to solve the irregular strip packing problem with continuous rotations

The irregular strip packing problem consists of cutting a set of convex and non-convex two-dimensional polygonal pieces from a board with a fixed height and infinite length. Owing to the importance of this problem, a large number of mathematical models and solution methods have been proposed. However, only few papers consider that the pieces can be rotated at any angle in order to reduce the board length used. Furthermore, the solution methods proposed in the literature are mostly heuristic. This paper proposes a novel mixed integer quadratically-constrained programming model for the irregular strip packing problem considering continuous rotations for the pieces. In the model, the pieces are allocated on the board using a reference point and its allocation is given by the translation and rotation of the pieces. To reduce the number of symmetric solutions for the model, sets of symmetry-breaking constraints are proposed. Computational experiments were performed on the model with and without symmetry-breaking constraints, showing that symmetry elimination improves the quality of solutions found by the solution methods. Tests were performed with instances from the literature. For two instances, it was possible to compare the solutions with a previous model from the literature and show that the proposed model is able to obtain numerically accurate solutions in competitive computational times.     

Complete and robust no-fit polygon generation for the irregular stock cutting problem

The no-fit polygon is a construct that can be used between pairs of shapes for fast and efficient handling of geometry within irregular two-dimensional stock cutting problems. Previously, the no-fit polygon (NFP) has not been widely applied because of the perception that it is difficult to implement and because of the lack of generic approaches that can cope with all problem cases without specific case-by-case handling. This paper introduces a robust orbital method for the creation of no-fit polygons which does not suffer from the typical problem cases found in the other approaches from the literature. Furthermore, the algorithm only involves two simple geometric stages so it is easily understood and implemented. We demonstrate how the approach handles known degenerate cases such as holes, interlocking concavities and jigsaw type pieces and we give generation times for 32 irregular packing benchmark problems from the literature, including real world datasets, to allow further comparison with existing and future approaches.     

A hybrid approach for optimization of one-dimensional cutting

The article examines a hybrid approach for optimizing one-dimensional stock cutting. The proposed approach combines two methods: the pattern-oriented LP-based method, and the item-oriented sequential heuristic procedure. The purpose of such a combined method is its ability to cut order lengths in exactly required number of pieces and to cumulate consecutive residual lengths in one piece which could be used later. The sample problem is presented and solved.     

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 beam search implementation for the irregular shape packing problem

This paper investigates the irregular shape packing problem. We represent the problem as an ordered list of pieces to be packed where the order is decoded by a placement heuristic. A placement heuristic from the literature is presented and modified with a more powerful nofit polygon generator and new evaluation criteria. We implement a beam search algorithm to search over the packing order. Using this approach many parallel partial solutions can be generated and compared. Computational results for benchmark problems show that the algorithm generates highly competitive solutions in significantly less time than the best results currently in the literature.     

Three-stage heuristic algorithm for three-dimensional irregular packing problem

This paper studies a new practical problem which can be decomposed into three three-dimensional packing problems: three-dimensional irregular packing with variable-size cartons problem, three-dimensional variable-size bin packing problem, and the single container loading problem. Since the three sub-problems are NP-hard, searching a good solution becomes more difficult. In this paper, mathematical models of each sub-problem are developed and three-stage heuristic algorithms are proposed to solve this new problem. Experiments are conducted with random instances generated by real-life case. Computational results indicate that the proposed algorithm is efficient and can yield satisfactory results.     

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.     

Reducing the number of cuts in generating three-staged cutting patterns

Three-staged guillotine patterns are widely used in the manufacturing industry to cut stock plates into rectangular items. The cutting cost often increases with the number of cuts required. This paper focuses on the rectangular two-dimensional cutting stock problem, where three-staged guillotine patterns are used, and the objective is to minimize the sum of plate and cutting costs. The column generation framework is used to solve the problem. It uses a pattern-generation procedure to obtain the patterns. The cutting cost is considered in both the pattern-generation procedure and the objective of the linear programming formulation. The computational results indicate that the approach can reduce the number of cuts, without increasing the plate cost.     

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.     

Secondary log breakdown optimization with dynamic programming

Log breakdown can be viewed as a two-stage process with logs sawn into slabs of wood known as flitches during the primary stage and flitches further processed during the secondary stage to produce edged (cut lengthwise) and trimmed (cut widthwise) pieces. This paper addresses the secondary problem and describes some procedures for determining the optimal cutting of flitches into graded dimensional boards. The problem is formulated as a set packing problem with the objective being to maximise total value. Extensions to the basic formulation include constraints which restrict the number of saws and which disallow waste between adjacent edged pieces. The problem is solved using dynamic programming techniques and the algorithms incorporated into a sawing simulation system. Comparisons with existing edging and trimming procedures show that substantial reductions in solution time (to as little as 1/25th of the time required for an enumerative search) can be achieved.     

MIP models for two-dimensional non-guillotine cutting problems with usable leftovers

In this study we deal with the two-dimensional non-guillotine cutting problem of how to cut a set of larger rectangular objects to a set of smaller rectangular items in exactly a demanded number of pieces. We are concerned with the special case of the problem in which the non-used material of the cutting patterns (objects leftovers) may be used in the future, for example if it is large enough to fulfill future item demands. Therefore, the problem is seen as a two-dimensional non-guillotine cutting/packing problem with usable leftovers, also known in the literature as a two-dimensional residual bin-packing problem. We use multilevel mathematical programming models to represent the problem appropriately, which basically consists of cutting the ordered items using a set of objects of minimum cost, among all possible solutions of minimum cost, choosing one that maximizes the value of the usable leftovers, and, among them, selecting one that minimizes the number of usable leftovers. Because of special characteristics of these multilevel models, they can be reformulated as one-level mixed integer programming (MIP) models. Illustrative numerical examples are presented and analysed.     

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.     

Random search in the one-dimensional cutting stock problem

The paper reports on a randomized approach to the one-dimensional cutting stock problem. In a sequential heuristic the next pattern to be processed is chosen according to the best outcome of 200 trials of random bin packing. The quality of the solutions is discussed and the problem of multiple occurrence of equivalent solutions with different patterns is explained by the cross over technique known from genetic algorithms. For one data instance with 27 demand lengths, reported by Haessler (1975), four different optimal solutions are given.     

Bidirectional best-fit heuristic for orthogonal rectangular strip packing

In a non-guillotinable rectangular strip packing problem (RF-SPP), the best orthogonal placement of given rectangular pieces on a strip of stock sheet having fixed width and infinite height are searched. The aim is to minimize the height of the strip while including all the pieces in appropriate orientations. In this study, a novel bidirectional best-fit heuristic (BBF) is introduced for solving RF-SPPs. The proposed heuristic as a new feature considers the gaps in both horizontal and vertical directions during the placement process. The performance of BBF is compared to some previous approaches, including one of the best heuristics from the literature. BBF achieves better results than the existing heuristics and delivers a better or matching performance as compared to the most of the previously proposed meta-heuristics for solving RF-SPPs.