On packing squares into a rectangle

We prove that every set of squares with total area 1 can be packed into a rectangle of area at most 2867/2048=1.399… . This improves on the previous best bound of 1.53. Also, our proof yields a linear time algorithm for finding such a packing.     

On packing of rectangles in a rectangle

It is known that ${\sum }_{i=1}^{\infty }1∕\left(i(i+1)\right)=1$. In 1968, Meir and Moser (1968) asked for finding the smallest $?$ such that all the rectangles of sizes $1∕i\times 1∕(i+1)$, $i\in \{1,2,\dots \}$, can be packed into a square or a rectangle of area $1+?$. First we show that in Paulhus (1997), the key lemma, as a statement, in the proof of the smallest published upper bound of the minimum area is false, then we prove a different new upper bound. We show that $?\le 1.26?10^{?9}$ if the rectangles are packed into a square and $?\le 6.878?10^{?10}$ if the rectangles are packed into a rectangle.     

4-Block heuristic for the rectangle packing problem

In this paper the rectangle packing problem (RPP) is considered. The RPP consists in finding a packing pattern of small rectangles within a larger rectangle such that the area utilization is maximized. We develop new heuristics for the RPP which are based on the G4-heuristic for the pallet loading problem. In addition to the general RPP we take also into account further restrictions which are of practical interest.     

Expected wasted space of optimal simple rectangle packing

A simple packing of a collection of rectangles contained in [0,1]² is a disjoint subcollection such that each vertical line meets at most one rectangle of the packing. The wasted space of the packing is the surface of the area of the part of [0,1]² not covered by the packing. We prove that for a certain number L, for all N2, the wasted space W_N in an optimal simple packing of N independent uniformly distributed rectangles satisfiesWork partially supported by an N.S.F. grant.Mathematics Subject Classification (2000): 60D05     

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.     

Optimal cutting directions and rectangle orientation algorithm

The first stage in hierarchical approaches to Floorplan Design defines topological relations between components that intend to optimize a given objective in a circuit board. These relations determine a placement that is subsequently optimized in order to minimize a cost measurement (that will probably be one between chip area or perimeter). The board optimization gives rise to multiple subproblems that need to be answered in order to obtain a good solution. Among the most relevant ones we find the problem of defining the optimal orientation of cells and the definition of the optimal cutting sequence that minimize the placement board area. We will present a generalization of an algorithm due to Stockmeyer so that it obtains a solution that not only defines the optimal cell orientation but also the slicing cuts sequence that will lead to this optimal orientation and overall area minimization.     

Exact and approximation algorithms for a soft rectangle packing problem

We consider the problem of finding an arrangement of rectangles with given areas that minimizes the total length of all inner and outer border lines. We present a polynomial time approximation algorithm and derive an upper bound estimation on its approximation ratio. Furthermore, we give a formulation of the problem as mixed-integer nonlinear program and show that it can be approximatively reformulated as linear mixed-integer program. On a test set of problem instances, we compare our approximation algorithm with another one from the literature. Using a standard numerical mixed-integer linear solver, we show that adding the solutions from the approximation algorithm as advanced starter helps to reduce the overall solution time for proven global optimality, or gives better primal and dual bounds if a certain time-limit is reached before.     

Optimal multicast route packing

This paper considers the hop-constrained multicast route packing problem with a bandwidth reservation to build QoS-guaranteed multicast routing trees with a minimum installation cost. Given a set of multicast sessions, each of which has a hop limit constraint and a bandwidth requirement, the problem is to determine the set of multicast routing trees in an arc-capacitated network with the objective of minimizing the cost. For the problem, we propose a branch-and-cut-and-price algorithm, which can be viewed as a branch-and-bound method incorporating both the strong cutting plane algorithm and the column generation method. We implemented and tested the proposed algorithm on randomly generated problem instances with sizes up to 30 nodes, 570 arcs, and 10 multicast sessions. The test results show that the algorithm can obtain the optimal solution to practically sized problem instances within a reasonable time limit in most cases.     

An effective quasi-human based heuristic for solving the rectangle packing problem

In this paper, we introduce an effective deterministic heuristic, Less Flexibility First, for solving the classical NP-complete rectangle packing problem. Many effective heuristics implemented for this problem are CPU-intensive and non-deterministic in nature. Others, including the polynomial approximation methodology [J. Assoc. Comput. Mach. 32 (1) (1985) 130] are too laborious for practical problem sizes. The technique we propose is inspired and developed by enhancing some rule-of-thumb guidelines resulting from the generation-long work experience of human professionals in ancient days. Although the Less Flexibility First heuristic is a deterministic algorithm, the results are very encouraging. This algorithm can consistently produce packing densities of around 99% on most randomly generated large examples. As compared with the recent results of a well known simulated annealing based Rectangle Packing (RP) algorithm [IEEE Trans. Computer-aided Design Integrated Circuits Systems 17 (1) (1998) 60], the results are much better both in less dead space² (4% vs 6.7%) and much less CPU time (9.57 vs 331.78 seconds). Experimenting our heuristics on a public rectangle packing data set covering instances of 16–97 rectangles, the average unpack ratio is quite satisfactory (0.92% for bounding boxes limited to be optimum and 2.68% for the completed packing), while most cases spend only a few minutes in CPU time.     

A reduction approach for solving the rectangle packing area minimization problem

In the rectangle packing area minimization problem (RPAMP) we are given a set of rectangles with known dimensions. We have to determine an arrangement of all rectangles, without overlapping, inside an enveloping rectangle of minimum area. The paper presents a generic approach for solving the RPAMP that is based on two algorithms, one for the 2D Knapsack Problem (KP), and the other for the 2D Strip Packing Problem (SPP). In this way, solving an instance of the RPAMP is reduced to solving multiple SPP and KP instances. A fast constructive heuristic is used as SPP algorithm while the KP algorithm is instantiated by a tree search method and a genetic algorithm alternatively. All these SPP and KP methods have been published previously. Finally, the best variants of the resulting RPAMP heuristics are combined within one procedure. The guillotine cutting condition is always observed as an additional constraint. The approach was tested on 15 well-known RPAMP instances (above all MCNC and GSRC instances) and new best solutions were obtained for 10 instances. The computational effort remains acceptable. Moreover, 24 new benchmark instances are introduced and promising results are reported.     

Local search algorithms for the rectangle packing problem with general spatial costs

We propose local search algorithms for the rectangle packing problem to minimize a general spatial cost associated with the locations of rectangles. The problem is to pack given rectangles without overlap in the plane so that the maximum cost of the rectangles is minimized. Each rectangle has a set of modes, where each mode specifies the width and height of the rectangle and its spatial cost function. The spatial costs are general piecewise linear functions which can be non-convex and discontinuous. Various types of packing problems and scheduling problems can be formulated in this form. To represent a solution of this problem, a pair of permutations of n rectangles is used to determine their horizontal and vertical partial orders, respectively. We show that, under the constraint specified by such a pair of permutations, optimal locations of the rectangles can be efficiently determined by using dynamic programming. The search for finding good pairs of permutations is conducted by local search and metaheuristic algorithms. We report computational results on various implementations using different neighborhoods, and compare their performance. We also compare our algorithms with other existing heuristic algorithms for the rectangle packing problem and scheduling problem. These computational results exhibit good prospects of the proposed algorithms. Key words.rectangle packing – sequence pair – general spatial cost – dynamic programming – metaheuristicsMathematics Subject Classification (1991):20E28, 20G40, 20C20     

Improved local search algorithms for the rectangle packing problem with general spatial costs

The rectangle packing problem with general spatial costs is to pack given rectangles without overlap in the plane so that the maximum cost of the rectangles is minimized. This problem is very general, and various types of packing problems and scheduling problems can be formulated in this form. For this problem, we have previously presented local search algorithms using a pair of permutations of rectangles to represent a solution. In this paper, we propose speed-up techniques to evaluate solutions in various neighborhoods. Computational results for the rectangle packing problem and a real-world scheduling problem exhibit good prospects of the proposed techniques.     

Parallel greedy algorithms for packing unequal circles into a strip or a rectangle

Given a finite set of circles of different sizes we study the strip packing problem (SPP) as well as the Knapsack Problem (KP). The SPP asks for a placement of all circles within a rectangular strip of fixed width so that the variable length of the strip is minimized. The KP requires packing of a subset of the circles in a given rectangle so that the wasted area is minimized. To solve these problems some greedy algorithms were developed which enhance the algorithms proposed by Huang et al. (J Oper Res Soc 56:539–548, 2005). Furthermore, the new greedy algorithms were parallelized using a master slave approach. The resulting parallel methods were tested using the instances introduced by Stoyan and Yaskov (Eur J Oper Res 156:590–600, 2004). Additionally, two sets of 128 instances each for the SPP and for the KP were generated and results for these new instances are also reported.     

An effective recursive partitioning approach for the packing of identical rectangles in a rectangle

In this work, we deal with the problem of packing (orthogonally and without overlapping) identical rectangles in a rectangle. This problem appears in different logistics settings, such as the loading of boxes on pallets, the arrangements of pallets in trucks and the stowing of cargo in ships. We present a recursive partitioning approach combining improved versions of a recursive five-block heuristic and an L-approach for packing rectangles into larger rectangles and L-shaped pieces. The combined approach is able to rapidly find the optimal solutions of all instances of the pallet loading problem sets Cover I and II (more than 50?000 instances). It is also effective for solving the instances of problem set Cover III (almost 100?000 instances) and practical examples of a woodpulp stowage problem, if compared to other methods from the literature. Some theoretical results are also discussed and, based on them, efficient computer implementations are introduced. The computer implementation and the data sets are available for benchmarking purposes.     

Using tree search bounds to enhance a genetic algorithm approach to two rectangle packing problems

A popular approach when using a genetic algorithm in the solution of constrained problems is to exploit problem specific information by representing individuals as ordered lists. A construction heuristic is then often used as a decoder to produce a solution from each ordering. In such a situation further information is often available in the form of bounds on the partial solutions. This paper uses two two-dimensional packing problems to illustrate how this information can be incorporated into the genetic operators to improve the overall performance of the search. Our objective is to use the packing problems as a vehicle for investigating a series of modifications of the genetic algorithm approach based on information from bounds on partial solutions.     

Optimal spray patterns for counterflow cooling towers with structured packing

In counterflow cooling towers, non-uniform patterns of flow are the consequence of the application of water from circular spray nozzles set in a square manifold. A detailed computational model for the performance of a tower accounting for this non-uniform water flow in cellular packing has been developed. The radial spray pattern of individual nozzles producing the best possible thermal performance was determined by optimization. It was concluded that for a particular nozzle manifold and packing arrangement, a performance limitation exists due to the inherently non-uniform pattern of water flow.     

Rectangling a rectangle

We show that the following are equivalent: (i) A rectangle of eccentricityv can be tiled using rectangles of eccentricityu. (ii) There is a rational function with rational coefficients,Q(z), such thatv =Q(u) andQ maps each of the half-planes {z | Re(z) < 0} and {z | Re(z) > 0 into itself, (iii) There is an odd rational function with rational coefficients,Q(z), such thatv = Q(u) and all roots ofv = Q(z) have a positive real part. All rectangles in this article have sides parallel to the coordinate axes and all tilings are finite. We letR(x, y) denote a rectangle with basex and heighty. In 1903 Dehn [1 ] proved his famous result thatR(x, y) can be tiled by squares if and only ify/x is a rational number. Dehn actually proved the following result. (See [4] for a generalization to tilings using triangles.) The first and third authors were partially supported by NSF.     

Computing rectangle enclosures

The rectangle enclosure problem is the problem of determining the subset of n iso-oriented planar rectangles that enclose a query rectangle Q. In this paper, we use a three layered data structure which is a combination of Range and Priority search trees and answers both the static and dynamic cases of the problem. Both the cases use O(n> log² n) space. For the static case, the query time is O(log² n log log n + K). The dynamic case is supported in O(log³ n + K) query time using O(log³ n) amortized time per update. K denotes the size of the answer. For the d-dimensional space the results are analogous. The query time is O(log^2d-2 n log log n + K) for the static case and O(log^2d-1 n + K) for the dynamic case. The space used is O(n> log^2d-2 n) and the amortized time for an update is O(log^2d-1 n). The existing bounds given for a class of problems which includes the present one, are O(log^2d n + K) query time, O(log^2d n) time for an insertion and O(log^2d-1 n) time for a deletion.     

Parabolic rectangle packings

The contacts graph (or nerve) of a packing is a combinatorial graph which describes the combinatorics of the packing. Let G be the 1-skeleton of a triangulation of an open disk and let P be a rectangle packing with contact graph G. In this paper a topological criterion for deciding whether G is an α-EL parabolic graph is given. Our result shows the internal relation between the topological property of the packing P and the combinatorial property of the contacts graph G of P.     

The rectangle intersection problem revisited

We take another look at the problem of intersecting rectangles with parallel sides. For this we derive a one-pass, time optimal algorithm which is easy to program, generalizes tod dimensions well, and illustrates a basic duality in its data structures and approach.The work of the first author was supported by the DAAD (Deutscher Akademischer Austauschdienst) and carried out while visiting McMaster University as a postdoctoral fellow. The work of the second author was supported by a Natural Sciences and Engineering Research Council of Canada Grant No. A-7700.