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.     

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.     

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.     

2DCPackGen: A problem generator for two-dimensional rectangular cutting and packing problems

Cutting and packing problems have been extensively studied in the literature in recent decades, mainly due to their numerous real-world applications while at the same time exhibiting intrinsic computational complexity. However, a major limitation has been the lack of problem generators that can be widely and commonly used by all researchers in their computational experiments. In this paper, a problem generator for every type of two-dimensional rectangular cutting and packing problems is proposed. The problems are defined according to the recent typology for cutting and packing problems proposed by Wäscher, Haußner, and Schumann (2007) and the relevant problem parameters are identified. The proposed problem generator can significantly contribute to the quality of the computational experiments run with cutting and packing problems and therefore will help improve the quality of the papers published in this field.     

Cutting and packing problems for irregular objects with continuous rotations: mathematical modelling and non-linear optimization

We further improve our methodology for solving irregular packing and cutting problems. We deal with an accurate representation of objects bounded by circular arcs and line segments and allow their continuous rotations and translations within rectangular and circular containers. We formulate a basic irregular placement problem which covers a wide spectrum of packing and cutting problems. We provide an exact non-linear programming (NLP) model of the problem, employing ready-to-use phi-functions. We develop an efficient solution algorithm to search for local optimal solutions for the problem in a reasonable time. The algorithm reduces our problem to a sequence of NLP subproblems and employs optimization procedures to generate starting feasible points and feasible subregions. Our algorithm allows us to considerably reduce the number of inequalities in NLP subproblems. To show the benefits of our methodology we give computational results for a number of new challenger and the best known benchmark instances.     

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 modelling framework for solving restricted planar location problems using phi-objects

This paper presents a general modelling framework for restricted facility location problems with arbitrarily shaped forbidden regions or barriers, where regions are modelled using phi-objects. Phi-objects are an efficient tool in mathematical modelling of 2D and 3D geometric optimization problems, and are widely used in cutting and packing problems and covering problems. The paper shows that the proposed modelling framework can be applied to both median and centre facility location problems, either with barriers or forbidden regions. The resulting models are either mixed-integer linear or non-linear programming formulations, depending on the shape of the restricted region and the considered distance measure. Using the new framework, all instances from the existing literature for this class of problems are solved to optimality. The paper also introduces and optimally solves a realistic multi-facility problem instance derived from an archipelago vulnerable to earthquakes. This problem instance is significantly more complex than any other instance described in the literature.     

A tutorial in irregular shape packing problems

Cutting and packing problems have been a core area of research for many decades. Irregular shape packing is one of the most recent variants to be widely researched and its history extends over 40 years. The evolution of solution approaches to this problem can be attributed to increased computer power and advances in geometric techniques as well as more sophisticated and insightful algorithm design. In this paper we will focus on the latter. Our aim is not to give a chronological account or an exhaustive review, but to draw on the literature to describe and evaluate the core approaches. Irregular packing is combinatorial and as a result solution methods are heuristic, save a few notable exceptions. We will explore different ways of representing the problem and mechanisms for moving between solutions. We will also propose where we see the future challenges for researchers in this area.     

A Method for Solving Container Packing for a Single Size of Box

There is an extensive literature on heuristic algorithms for two-dimensional cutting problems and three-dimensional packing, but there seems to be very little on the three-dimensional single-box type packing problem. This paper gives a structure for dealing with that problem as a heuristic. It also presents a set of upper bounds on the optimal fit. Finally, the paper compares a particular application of the algorithmic structure with the George and Robinson multiple-box type heuristic.     

Jostling for position: local improvement for irregular cutting patterns

This paper introduces a new improvement heuristic for irregular cutting and packing problems. The method is based on a small number of repetitions of any leftmost placement policy and is particularly effective in situations where computation time is strictly limited but exceeds that required for a single pass approach. Both the algorithm and the geometry required for implementation are described in full and the results of computational experiments on a variety of data are presented. These results show that the algorithm is an effective technique for producing good packings.     

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.     

Staged and constrained two-dimensional guillotine cutting problems: An AND/OR-graph approach

An AND/OR-graph approach has been proposed by Morabito et al. to solve non-staged and unconstrained two-dimensional guillotine cutting problems. Basically, this approach consists of representing cutting patterns as complete paths in an AND/OR graph (where nodes and arcs correspond to rectangles and cuts, respectively) and choosing a search strategy to traverse or enumerate the nodes of the graph. In this present paper, the AND/OR-graph approach is extended to solve staged and constrained problems. Computational experiments with examples from the literature are performed and indicate that this approach generates good and fast solutions using a microcomputer. In addition to the fact that the AND/OR-graph approach can handle important constraints, it can be easily extended to solve cutting and packing problems with multiple dimensions.     

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.     

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 set of geometric programming test problems and their solutions

This paper attempts to provide a set of standard test examples for researchers working in the area of geometric programming and general nonlinear, continuous, nonconvex programming algorithms. The examples consist partly of applications of nonlinear programming that have appeared in the literature and partly of original geometric programming applications. Solutions to all the problems are provided as well as the starting points from which these solutions were computed. Other computationally important aspects such as tolerances and degree of accuracy with which these problems were solved, are also included.This work was supported in part by Canada Council Grant #S74-0148 and National Research Council of Canada Grant #084-6319-32.     

Packing Steiner trees: a cutting plane algorithm and computational results

In this paper we describe a cutting plane algorithm for the Steiner tree packing problem. We use our algorithm to solve some switchbox routing problems of VLSI-design and report on our computational experience. This includes a brief discussion of separation algorithms, a new LP-based primal heuristic and implementation details. The paper is based on the polyhedral theory for the Steiner tree packing polyhedron developed in our companion paper (this issue) and meant to turn this theory into an algorithmic tool for the solution of practical problems.     

A new upper bound for unconstrained two-dimensional cutting and packing

A new upper bound for the unconstrained two-dimensional cutting or packing problem is proposed in this paper. The proposed upper bound can be calculated for any size of plate by solving just two knapsack problems at the beginning of the algorithm. In this research, the proposed upper bound was applied to the well known exact cutting algorithm, although it can be used for both cutting and packing applications. The experimental results demonstrate that the new upper bound is very efficient, and reduces the search time required to find an optimal solution.     

Tools of mathematical modeling of arbitrary object packing problems

The article reviews the concept of and further develops phi-functions (Φ-functions) as an efficient tool for mathematical modeling of two-dimensional geometric optimization problems, such as cutting and packing problems and covering problems. The properties of the phi-function technique and its relationship with Minkowski sums and the nofit polygon are discussed. We also describe the advantages of phi-functions over these approaches. A clear definition of the set of objects for which phi-functions may be derived is given and some exceptions are illustrated. A step by step procedure for deriving phi-functions illustrated with examples is provided including the case of continuous rotation.     

Approximate and exact algorithms for the double-constrained two-dimensional guillotine cutting stock problem

In this paper, we propose approximate and exact algorithms for the double constrained two-dimensional guillotine cutting stock problem (DCTDC). The approximate algorithm is a two-stage procedure. The first stage attempts to produce a starting feasible solution to DCTDC by solving a single constrained two dimensional cutting problem, CDTC. If the solution to CTDC is not feasible to DCTDC, the second stage is used to eliminate non-feasibility. The exact algorithm is a branch-and-bound that uses efficient lower and upper bounding schemes. It starts with a lower bound reached by the approximate two-stage algorithm. At each internal node of the branching tree, a tailored upper bound is obtained by solving (relaxed) knapsack problems. To speed up the branch and bound, we implement, in addition to ordered data structures of lists, symmetry, duplicate, and non-feasibility detection strategies which fathom some unnecessary branches. We evaluate the performance of the algorithm on different problem instances which can become benchmark problems for the cutting and packing literature.