An investigation into two bin packing problems with ordering and orientation implications

This paper considers variants of the one-dimensional bin packing (and stock cutting) problem in which both the ordering and orientation of items in a container influences the validity and quality of a solution. Two new real-world problems of this type are introduced, the first that involves the creation of wooden trapezoidal-shaped trusses for use in the roofing industry, the second that requires the cutting and scoring of rectangular pieces of cardboard in the construction of boxes. To tackle these problems, two variants of a local search-based approximation algorithm are proposed, the first that attempts to determine item ordering and orientation via simple heuristics, the second that employs more accurate but costly branch-and-bound procedures. We investigate the inevitable trade-off between speed and accuracy that occurs with these variants and highlight the circumstances under which each scheme is advantageous.     

Applying self-adaptive evolutionary algorithms to two-dimensional packing problems using a four corners’ heuristic

This paper proposes a four corners’ heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners’ (FC) heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature; with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted.     

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.     

PackLib: An integrated library of multi-dimensional packing problems

We present PackLib², the first fully integrated benchmark library for multi-dimensional packing instances. PackLib² combines a systematic collection of all benchmark instances from previous literature with a well-organized set of new and challenging large instances. The XML format allows linking basic benchmark data with other important properties, like bibliographic information, origin, best known solutions, runtimes, etc. Transforming instances into a variety of existing input formats is also quite easy, as the XML format lends itself to easy conversion; for this purpose, a number of parsers are provided. Thus, PackLib² aims at becoming a one-stop location for the packing and cutting community: in addition to fair and easy comparison of algorithmic work and ongoing measurement of scientific progress, it poses numerous challenges for future research.     

A hybrid genetic algorithm-heuristic for a two-dimensional orthogonal packing problem

In this paper we address a two-dimensional (2D) orthogonal packing problem, where a fixed set of small rectangles has to be placed on a larger stock rectangle in such a way that the amount of trim loss is minimized. The algorithm we propose hybridizes a placement procedure with a genetic algorithm based on random keys. The approach is tested on a set of instances taken from the literature and compared with other approaches. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm.     

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.     

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.     

Optimising the cutting of wood fibre plates in the hardboard industry

Hardboard companies transform eucalyptus trunks into rectangular wood fibre plates basically by means of processes of disintegration and reconstitution of wood fibres. Such plates called hardboards are then cut into ordered items (smaller rectangles) to satisfy customer demands. In this paper, we present approaches to generate cutting patterns that minimize the cost or waste of material, considering different particular constraints associated with longitudinal and transversal saws, head cuts, book rotation and item unloading stations of the cutting machine. The methods are based on dynamic programming recursive formulas combined with greedy constructive heuristics and the primal simplex algorithm. To illustrate the application of these approaches, a case study was carried out in a Brazilian hardboard company. The results show that the approaches are able to produce better solutions than the ones currently used by the company.     

A skyline heuristic for the 2D rectangular packing and strip packing problems

In this paper, we propose a greedy heuristic for the 2D rectangular packing problem (2DRP) that represents packings using a skyline; the use of this heuristic in a simple tabu search approach outperforms the best existing approach for the 2DRP on benchmark test cases. We then make use of this 2DRP approach as a subroutine in an “iterative doubling” binary search on the height of the packing to solve the 2D rectangular strip packing problem (2DSP). This approach outperforms all existing approaches on standard benchmark test cases for the 2DSP.     

The two-dimensional finite bin packing problem. Part I: New lower bounds for the oriented case

The Two-Dimensional Finite Bin Packing Problem (2BP) consists of determining the minimum number of large identical rectangles, bins, that are required for allocating without overlapping a given set of rectangular items. The items are allocated into a bin with their edges always parallel or orthogonal to the bin edges. The problem is strongly NP-hard and finds many practical applications. In this paper we describe new lower bounds for the 2BP where the items have a fixed orientation and we show that the new lower bounds dominate two lower bounds proposed in the literature. These lower bounds are extended in Part II (see Boschetti and Mingozzi 2002) for a more general version of the 2BP where some items can be rotated by . Moreover, in Part II a new heuristic algorithm for solving both versions of the 2BP is presented and computational results on test problems from the literature are given in order to evaluate the effectiveness of the proposed lower bounds.     

A block-based layer building approach for the 2D guillotine strip packing problem

We examine the 2D strip packing problems with guillotine-cut constraint, where the objective is to pack all rectangles into a strip with fixed width and minimize the total height of the strip. We combine three most successful ideas for the orthogonal rectangular packing problems into a single coherent algorithm: (1) packing a block of rectangles instead of a single rectangle in each step; (2) dividing the strip into layers and pack layer by layer; and (3) unrolling and repacking the top portion of the solutions where usually wasted space occurs. Computational experiments on benchmark test sets suggest that our approach rivals existing approaches.     

A goal-driven approach to the 2D bin packing and variable-sized bin packing problems

In this paper, we examine the two-dimensional variable-sized bin packing problem (2DVSBPP), where the task is to pack all given rectangles into bins of various sizes such that the total area of the used bins is minimized. We partition the search space of the 2DVSBPP into sets and impose an order on the sets, and then use a goal-driven approach to take advantage of the special structure of this partitioned solution space. Since the 2DVSBPP is a generalization of the two-dimensional bin packing problem (2DBPP), our approach can be adapted to the 2DBPP with minimal changes. Computational experiments on the standard benchmark data for both the 2DVSBPP and 2DBPP shows that our approach is more effective than existing approaches in literature.     

An infinite family of subcubic graphs with unbounded packing chromatic number

Recently, Balogh et al. (2018) answered in negative the question that was posed in several earlier papers whether the packing chromatic number is bounded in the class of graphs with maximum degree 3. In this note, we present an explicit infinite family of subcubic graphs with unbounded packing chromatic number.     

Exact algorithm for generating two-segment cutting patterns of punched strips

Metal plates are often divided into items in two stages. First a guillotine shear cuts the plate into strips at the shearing stage, and then a stamping press punches out the items from the strips at the punching stage. This paper presents an algorithm for generating optimal two-segment cutting patterns of strips at the shearing stage. An orthogonal cut divides the plate into two segments, each of which contains strips of the same direction and length. The algorithm uses dynamic programming techniques to determine the optimal strip layouts on segments of various lengths, and selects two segments to appear in the optimal pattern. The segments are considered in increasing order of their lengths, so that dominant properties can be used to shorten the computation time. The computational results indicate that the algorithm is efficient in both material utilization and computation time.     

A column generation heuristic for the two-dimensional two-staged guillotine cutting stock problem with multiple stock size

We consider a two-dimensional cutting stock problem where stock of different sizes is available, and a set of rectangular items has to be obtained through two-staged guillotine cuts. We propose a heuristic algorithm, based on column generation, which requires as its subproblem the solution of a two-dimensional knapsack problem with two-staged guillotines cuts. A further contribution of the paper consists in the definition of a mixed integer linear programming model for the solution of this knapsack problem, as well as a heuristic procedure based on dynamic programming. Computational experiments show the effectiveness of the proposed approach, which obtains very small optimality gaps and outperforms the heuristic algorithm proposed by Cintra et al. [3].     

Space defragmentation for packing problems

One of main difficulties of multi-dimensional packing problems is the fragmentation of free space into several unusable small parts after a few items are packed. This study proposes a defragmentation technique to combine the fragmented space into a continuous usable space, which potentially allows the packing of additional items. We illustrate the effectiveness of this technique using the two- and three-dimensional bin packing problem, where the aim is to load all given items (represented by rectangular boxes) into the minimum number of identical bins. Experimental results based on well-known 2D and 3D bin packing data sets show that our defragmentation technique alone is able to produce solutions approaching the quality of considerably more complex meta-heuristic approaches for the problem. In conjunction with a bin shuffling strategy for incremental improvement, our resultant algorithm outperforms all leading meta-heuristic approaches based on the commonly used benchmark data by a significant margin.     

Hardness of approximation for orthogonal rectangle packing and covering problems

Bansal and Sviridenko [N. Bansal, M. Sviridenko, New approximability and inapproximability results for 2-dimensional bin packing, in: Proceedings of the 15th Annual ACM–SIAM Symposium on Discrete Algorithms, SODA, 2004, pp. 189–196] proved that there is no asymptotic PTAS for 2-dimensional Orthogonal Bin Packing (without rotations), unless P=NP. We show that similar approximation hardness results hold for several 2- and 3-dimensional rectangle packing and covering problems even if rotations by ninety degrees are allowed. Moreover, for some of these problems we provide explicit lower bounds on asymptotic approximation ratio of any polynomial time approximation algorithm. Our hardness results apply to the most studied case of 2-dimensional problems with unit square bins, and for 3-dimensional strip packing and covering problems with a strip of unit square base.     

An integer programming model for two- and three-stage two-dimensional cutting stock problems

In this paper, an integer programming model for two-dimensional cutting stock problems is proposed. In the problems addressed, it is intended to cut a set of small rectangular items of given sizes from a set of larger rectangular plates in such a way that the total number of used plates is minimized.