Bin packing: On optimizing the number of pieces packed

We consider bin-packing variations related to the well-studied problem of maximizing the total number of pieces packed into a fixed set of bins. We show that, when the objective is to minimize the total number of pieces packed subject to the constraint that no unpacked piece will fit, no polynomial-time relative approximation algorithm exists (unless, of course,P=NP). That is, we prove that it isNP-hard to guarantee packing no more than a constant multiple of the optimal number of pieces, for any constant. This appears to be the first bin-packing problem for which this property has been demonstrated. Furthermore, this result also holds for the allied packing variant which seeks to minimize the maximum number of pieces packed in any single bin. We find the situation to be markedly better for the problem of maximizing the minimum number of pieces in any bin. If all bins possess the same capacity, we prove that the familiar SPF rule is an absolute approximation algorithm with additive constant 1, and can therefore be regarded as a best possible heuristic. For the more general and difficult case in which bin capacities may differ, it turns out that SPF fails to qualify as even a relative approximation algorithm. However, we devise an implementation of the well-known FFD heuristic, which we show to be a relative approximation algorithm, yielding a worst-case performance ratio of 1/2 with additive constant 0. Moreover, we prove that (unlessP=NP) no polynomial-time algorithm can guarantee a higher ratio without sacrificing the additive constant.This author's research is supported in part by the National Science Foundation under grants ECS-8403859 and MIP-8603879.     

Resource augmentation in two-dimensional packing with orthogonal rotations

We consider the problem of packing two-dimensional rectangles into the minimum number of unit squares, when 90^° rotations are allowed. Our main contribution is a polynomial-time algorithm for packing rectangles into at most OPT bins whose sides have length (1+ε), for any positive ε. Additionally, we show near-optimal packing results for a number of related packing problems.     

Performance Bound for Bottom-Left Guillotine Packing of Rectangles

In this article we show that bottom-left guillotine placement of rectangles ordered by decreasing width in a fixed-width bin is not more than three times the height of an optimal placement. This bound is also true for bottom-left placement of rectangles without the guillotine constraints. Thus, bottom-left guillotine placement in which rectangles are ordered by decreasing width has the same worst case performance bound as bottom-left placement of rectangles without guillotine constraints.     

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.     

Resource augmentation for online bounded space bin packing

We study online bounded space bin packing in the resource augmentation model of competitive analysis. In this model, the online bounded space packing algorithm has to pack a list L of items in (0,1] into a small number of bins of size b1. Its performance is measured by comparing the produced packing against the optimal offline packing of the list L into bins of size 1.We present a complete solution to this problem: For every bin size b1, we design online bounded space bin packing algorithms whose worst case ratio in this model comes arbitrarily close to a certain bound ρ(b). Moreover, we prove that no online bounded space algorithm can perform better than ρ(b) in the worst case.     

A 5/4 algorithm for two-dimensional packing

This paper proposes a new algorithm for a two-dimensional packing problem first studied by Baker, Coffman, and Rivest (SIAM J. Comput. 9, No. 4(1980), 846–855). In their model, a finite list of rectangles is to be packed into a rectangular bin of finite width but infinite height. The model has applications to certain scheduling and stock-cutting problems. Since the problem of finding an optimal packing is NP-hard, previous work has been directed at finding polynomial approximation algorithms for the problem, i.e., algorithms which come within a constant times the height used by an optimal packing. For the algorithm proposed in this paper, the ratio of the height obtained by the algorithm and the height used by an optimal packing is asymptotically bounded by 5/4. This bound is an improvement over the bound of 4/3 achieved by the best previous algorithm.     

An approximation scheme for the two-stage,two-dimensional knapsack problem

We present an approximation scheme for the two-dimensional version of the knapsack problem which requires packing a maximum-area set of rectangles in a unit square bin, with the further restrictions that packing must be orthogonal without rotations and done in two stages. Achieving a solution which is close to the optimum modulo a small additive constant can be done by taking wide inspiration from an existing asymptotic approximation scheme for two-stage two-dimensional bin packing. On the other hand, getting rid of the additive constant to achieve a canonical approximation scheme appears to be widely nontrivial.     

An asymptotic approximation scheme for the concave cost bin packing problem

We consider a generalized one-dimensional bin packing model in which the cost of a bin is a nondecreasing concave function of the utilization of the bin. We show that for any given positive constant ?, there exists a polynomial-time approximation algorithm with an asymptotic worst-case performance ratio of no more than 1 + ?.     

超尺寸物品装箱问题

本文给出一类新的装箱问题，超尺寸物品装箱问题。就实际解决该问题所普遍彩的两步法，证明了当采用经典目标函数并且拆分次数不超过2时，第二步采用FFDLR的渐进最坏比为3/2。进而针对超尺寸物品装箱问题的算法提出了一个评价效率更高的目标函数。证明了在此目标函数下，当不限制物品的最大尺寸时，第二步采用最优装法两步法的渐近最坏比为2。最后，给出渐近最坏与拆分次数的关系。     

Finding the connected components and a maximum clique of an intersection graph of rectangles in the plane

An intersection graph of rectangles in the (x, y)-plane with sides parallel to the axes is obtained by representing each rectangle by a vertex and connecting two vertices by an edge if and only if the corresponding rectangles intersect. This paper describes algorithms for two problems on intersection graphs of rectangles in the plane. One is an O(n log n) algorithm for finding the connected components of an intersection graph of n rectangles. This algorithm is optimal to within a constant factor. The other is an O(n log n) algorithm for finding a maximum clique of such a graph. It seems interesting that the maximum clique problem is polynomially solvable, because other related problems, such as the maximum stable set problem and the minimum clique cover problem, are known to be NP-complete for intersection graphs of rectangles. Furthermore, we briefly show that the k-colorability problem on intersection graphs of rectangles is NP-complete.     

Exact algorithms for the two-dimensional strip packing problem with and without rotations

We propose exact algorithms for the two-dimensional strip packing problem (2SP) with and without 90° rotations. We first focus on the perfect packing problem (PP), which is a special case of 2SP, wherein all given rectangles are required to be packed without wasted space, and design branch-and-bound algorithms introducing several branching rules and bounding operations. A combination of these rules yields an algorithm that is especially efficient for feasible instances of PP. We then propose several methods of applying the PP algorithms to 2SP. Our algorithms succeed in efficiently solving benchmark instances of PP with up to 500 rectangles and those of 2SP with up to 200 rectangles. They are often faster than existing exact algorithms specially tailored for problems without rotations.     

Resource augmented semi-online bounded space bin packing

We study on-line bounded space bin-packing in the resource augmentation model of competitive analysis. In this model, the on-line bounded space packing algorithm has to pack a list L of items with sizes in (0, 1], into a minimum number of bins of size b, b≥1. A bounded space algorithm has the property that it only has a constant number of active bins available to accept items at any point during processing. The performance of the algorithm is measured by comparing the produced packing with an optimal offline packing of the list L into bins of size 1. The competitive ratio then becomes a function of the on-line bin size b. Csirik and Woeginger studied this problem in [J. Csirik, G.J. Woeginger, Resource augmentation for online bounded space bin packing, Journal of Algorithms 44(2) (2002) 308-320] and proved that no on-line bounded space algorithm can perform better than a certain bound ρ(b) in the worst case. We relax the on-line condition by allowing a complete repacking within the active bins, and show that the same lower bound holds for this problem as well, and repacking may only allow one to obtain the exact best possible competitive ratio of ρ(b) having a constant number of active bins, instead of achieving this bound in the limit. We design a polynomial time on-line algorithm that uses three active bins and achieves the exact best possible competitive ratio ρ(b) for the given problem.     

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.     

Anisotropic Chan–Vese segmentation

In this paper we study a variant to Chan–Vese (CV) segmentation model with rectilinear anisotropy. We show existence of minimizers in the 2-phases case and how they are related to the (anisotropic) Rudin–Osher–Fatemi (ROF) denoising model. Our analysis shows that in the natural case of a piecewise constant on rectangles image ($\mathrm{PCR}$ function in short), there exists a minimizer of the CV functional which is also piecewise constant on rectangles over the same grid that the one defined by the original image. In the multiphase case, we show that minimizers of the CV multiphase functional also share this property in the case that the initial image is a $\mathrm{PCR}$ function. We also investigate a multiphase and anisotropic version of the Truncated ROF algorithm, and we compare the solutions given by this algorithm with minimizers of the multiphase anisotropic CV functional.     

A 54 algorithm for two-dimensional packing

This paper proposes a new algorithm for a two-dimensional packing problem first studied by Baker, Coffman, and Rivest (SIAM J. Comput.9, No. 4(1980), 846–855). In their model, a finite list of rectangles is to be packed into a rectangular bin of finite width but infinite height. The model has applications to certain scheduling and stock-cutting problems. Since the problem of finding an optimal packing is NP-hard, previous work has been directed at finding polynomial approximation algorithms for the problem, i.e., algorithms which come within a constant times the height used by an optimal packing. For the algorithm proposed in this paper, the ratio of the height obtained by the algorithm and the height used by an optimal packing is asymptotically bounded by $\text{5}\text{4}$. This bound is an improvement over the bound of $\text{4}\text{3}$ achieved by the best previous algorithm.     

A 17/10-approximation algorithm fork-bounded space on-line variable-sized bin packing

A version of thek-bounded space on-line bin packing problem, where a fixed collection of bin sizes is allowed, is considered. By packing large items into appropriate bins and closing appropriate bins, we can derive an algorithm with worst-case performance bound 1.7 fork≥3. This research is supported by the Science Foundation under State Education Committee of China. The earlier version was done in Institute of Applied Mathematics, Academia Sinica.     

Dynamic multi-dimensional bin packing

A natural generalization of the classical online bin packing problem is the dynamic bin packing problem introduced by Coffman et al. (1983) [7]. In this formulation, items arrive and depart and the objective is to minimize the maximal number of bins ever used over all times. We study the oriented multi-dimensional dynamic bin packing problem for two dimensions, three dimensions and multiple dimensions. Specifically, we consider dynamic packing of squares and rectangles into unit squares and dynamic packing of three-dimensional cubes and boxes into unit cubes. We also study dynamic d-dimensional hypercube and hyperbox packing. For dynamic d-dimensional box packing we define and analyze the algorithm NFDH for the offline problem and present a dynamic version. This algorithm was studied before for rectangle packing and for square packing and was generalized only for multi-dimensional cubes. We present upper and lower bounds for each of these cases.     

On systolic arrays for updating the Cholesky factorization

We have considered the systolic implementation of several methods for updating the Cholesky factorization. For positive rank-k changes there are simple one-pass arrays that implement algorithms based on elimination and plane rotations. In the case of negative rank-one changes, we do not feel that the standard algorithm [2] has a practical implementation. We have introduced a new algorithm for the case of a negative rank-k change and provided an attractive two-pass systolic implementation.     

Packing into the smallest square: Worst-case analysis of lower bounds

We address the problem of packing a given set of rectangles into the minimum size square. We consider three versions of the problem, arising when the rectangles (i) are squares; (ii) have a fixed orientation; (iii) can be rotated by 90. For each case we study lower bounds, and analyze their worst-case performance ratio. In addition, we evaluate through computational experiments their average performance on instances from the literature.