Two-dimensional Bin Packing Research Articles

Tree-decomposition based heuristics for the two-dimensional bin packing problem with conflicts

This paper deals with the two-dimensional bin packing problem with conflicts (BPC-2D). Given a finite set of rectangular items, an unlimited number of rectangular bins and a conflict graph, the goal is to find a conflict-free packing of the items minimizing the number of bins used. In this paper, we propose a new framework based on a tree-decomposition for solving this problem. It proceeds by decomposing a BPC-2D instance into subproblems to be solved independently. Applying this decomposition method is not straightforward, since merging partial solutions is hard. Several heuristic strategies are proposed to make an effective use of the decomposition. Computational experiments show the practical effectiveness of our approach.

A Computational Study of Lower Bounds for the Two Dimensional Bin Packing Problem

Abstract We survey lower bounds for the variant of the two-dimensional bin packing problem where items cannot be rotated. We prove that the dominance relation claimed by Carlier et al. [Carlier, J., F. Clautiaux and A. Moukrim, New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation, Computers and Operations Research 34 (2007), pp. 2223–2250] between their lower bounds and those of Boschetti and Mingozzi [Boschetti, M. and A. Mingozzi, The two-dimensional finite bin packing problem part I: New lower bounds for the oriented case, 40R 1 (2003), pp. 27–42] is not valid. We analyze the performance of lower bounds from the literature and we provide the results of a computational experiment.

Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem

In this paper, we develop an extended guided tabu search (EGTS) and a new heuristic packing algorithm for the two-dimensional loading vehicle routing problem (2L-CVRP). The 2L-CVRP is a combination of two well-known NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. We propose a meta-heuristic methodology EGTS which incorporates theories of tabu search and extended guided local search (EGLS). It has been proved that tabu search is a very good approach for the CVRP, and the guiding mechanism of the EGLS can help tabu search to escape effectively from local optimum. Furthermore, we have modified a collection of packing heuristics by adding a new packing heuristic to solve the loading constraints in 2L-CVRP, in order to improve the cost function significantly. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances.

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.

Two-dimensional online bin packing with rotation

In two-dimensional bin packing problems, the input items are rectangles which need to be packed in a non-overlapping manner. The goal is to assign the items into unit squares using an axis-parallel packing. Most previous work on online packing concentrated on items of fixed orientation, which must be assigned such that their bottom side is parallel to the bottom of the bin. In this paper we study the case of rotatable items, which can be rotated by ninety degrees. We give almost tight bounds on the (asymptotic) competitive ratio of bounded space bin packing of rotatable items, and introduce a new unbounded space algorithm. This improves the results of Fujita and Hada.

A New Approximation Method for Set Covering Problems, with Applications to Multidimensional Bin Packing

In this paper we introduce a new general approximation method for set covering problems, based on the combination of randomized rounding of the (near-) optimal solution of the linear programming (LP) relaxation, leading to a partial integer solution and the application of a well-behaved approximation algorithm to complete this solution. If the value of the solution returned by the latter can be bounded in a suitable way, as is the case for the most relevant generalizations of bin packing, the method leads to improved approximation guarantees, along with a proof of tighter integrality gaps for the LP relaxation. For d-dimensional vector packing, we obtain a polynomial-time randomized algorithm with asymptotic approximation guarantee arbitrarily close to $\ln d + 1$. For $d=2$, this value is $1.693\dots$; i.e., we break the natural 2 “barrier” for this case. Moreover, for small values of d this is a notable improvement over the previously known $O(\ln d)$ guarantee by Chekuri and Khanna [SIAM J. Comput., 33 (2004), pp. 837–851]. For two-dimensional bin packing with and without rotations, we obtain polynomial-time randomized algorithms with asymptotic approximation guarantee $1.525\dots$, improving upon previous algorithms with asymptotic performance guarantees arbitrarily close to 2 by Jansen and van Stee [On strip packing with rotations, in Proceedings of the 37th Annual ACM Symposium on the Theory of Computing, 2005, pp. 755–761] for the problem with rotations and $1.691\ldots$ by Caprara [Math. Oper. Res., 33 (2008), pp. 203–215] for the problem without rotations. The previously unknown key property used in our proofs follows from a retrospective analysis of the implications of the landmark bin packing approximation scheme by Fernandez de la Vega and Lueker [Combinatorica, 1 (1981), pp. 349–355]. We prove that their approximation scheme is “subset oblivious,” which leads to numerous applications.

Two-staged guillotine cut, two-dimensional bin packing optimisation with flexible bin size for steel mother plate design

This paper looks into the steel mother plate design problem. A slab, which is an intermediate work in process, is subsequently rolled into a mother plate with the specific dimensions of thickness, length, and width. The mother plate is then cut into customer order plates. As a slab is rolled into a mother plate through a series of horizontal and vertical rolling processes, different-sized mother plates can be generated from a single-slab type. This flexibility allows for the size of a mother plate to be determined according to the order plates assigned to it. Furthermore, when the order plates are cut from a mother plate, a guillotine cut is required to reduce the production cost. The steel mother plate design problem involves the placing of order plates on the mother plates in a guillotine cut pattern and determining the sizes of the mother plates with the objective of minimising the number of slabs; thus it may be considered as a two-staged guillotine cut, two-dimensional bin packing problem with flexible bin size. This paper introduces the problem, presents several mathematical models, and proposes an iterative two-phase heuristic method consisting of several algorithms to solve the problem. Computational results for the benchmark problems show the effectiveness of the proposed method.

Dantzig-Wolfe decomposition and branch-and-price solving in G12

The G12 project is developing a software environment for stating and solving combinatorial problems by mapping a high-level model of the problem to an efficient combination of solving methods. Model annotations are used to control this process. In this paper we explain the mapping to branch-and-price solving. Dantzig-Wolfe decomposition is automatically performed using the additional information given by the model annotations. The models obtained can then be solved using column generation and branch-and-price. G12 supports the selection of specialised subproblem solvers, the aggregation of identical subproblems to reduce symmetries, automatic disaggregation when required by branch-and-bound, the use of specialised subproblem constraint-branching rules, and different master problem solvers including a hybrid solver based on the volume algorithm. We demonstrate the benefits of the G12 framework on three examples: a trucking problem, cutting stock, and two-dimensional bin packing.

적합성 함수를 이용한 2차원 저장소 적재 문제의 휴리스틱 알고리즘

2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다. The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

Sequence based heuristics for two-dimensional bin packing problems

This article addresses several variants of the two-dimensional bin packing problem. In the most basic version of the problem it is intended to pack a given number of rectangular items with given sizes in rectangular bins in such a way that the number of bins used is minimized. Different heuristic approaches (greedy, local search, and variable neighbourhood descent) are proposed for solving four guillotine two-dimensional bin packing problems. The heuristics are based on the definition of a packing sequence for items and in a set of criteria for packing one item in a current partial solution. Several extensions are introduced to deal with issues pointed out by two furniture companies. Extensive computational results on instances from the literature and from the two furniture companies are reported and compared with optimal solutions, solutions from other five (meta)heuristics and, for a small set of instances, with the ones used in the companies.

An agent-based approach to the two-dimensional guillotine bin packing problem

Abstract The two-dimensional guillotine bin packing problem consists of packing, without overlap, small rectangular items into the smallest number of large rectangular bins where items are obtained via guillotine cuts. This problem is solved using a new guillotine bottom left (GBL) constructive heuristic and its agent-based (A–B) implementation. GBL, which is sequential, successively packs items into a bin and creates a new bin every time it can no longer fit any unpacked item into the current one. A–B, which is pseudo-parallel, uses the simplest system of artificial life. This system consists of active agents dynamically interacting in real time to jointly fill the bins while each agent is driven by its own parameters, decision process, and fitness assessment. A–B is particularly fast and yields near-optimal solutions. Its modularity makes it easily adaptable to knapsack related problems.

Models and algorithms for three-stage two-dimensional bin packing

We consider the three-stage two-dimensional bin packing problem (2BP) which occurs in real-world applications such as glass, paper, or steel cutting. We present new integer linear programming formulations: models for a restricted version and the original version of the problem are developed. Both only involve polynomial numbers of variables and constraints and effectively avoid symmetries. Those models are solved using CPLEX. Furthermore, a branch-and-price (B&P) algorithm is presented for a set covering formulation of the unrestricted problem, which corresponds to a Dantzig-Wolfe decomposition of the polynomially-sized model. We consider column generation stabilization in the B&P algorithm using dual-optimal inequalities. Fast column generation is performed by applying a hierarchy of four methods: (a) a fast greedy heuristic, (b) an evolutionary algorithm, (c) solving a restricted form of the pricing problem using CPLEX, and finally (d) solving the complete pricing problem using CPLEX. Computational experiments on standard benchmark instances document the benefits of the new approaches: The restricted version of the integer linear programming model can be used to quickly obtain near-optimal solutions. The unrestricted version is computationally more expensive. Column generation provides a strong lower bound for 3-stage 2BP. The combination of all four pricing algorithms and column generation stabilization in the proposed B&P framework yields the best results in terms of the average objective value, the average run-time, and the number of instances solved to proven optimality.

Bidimensional packing by bilinear programming

We consider geometric problems in which rectangles have to be packed into (identical) squares. These problems turn out to be very hard in practice, and ILP formulations in which variables specify the coordinates in the packing perform very poorly. While most methods developed so far are based on simple geometric considerations, a recent landmark result of Fekete and Schepers suggests to put these geometric aspects aside and use the most advanced tools for the one-dimensional case. In this paper we make additional progress in this direction, especially on the basic question “Does a given set of rectangles fit into a square?”, which turns out to be the bottleneck of all the approaches known. Given a set of rectangles and the associated convex hull of rectangle subsets that fit into a square, we derive a wide class of valid inequalities for this convex hull from a complete description of the two knapsack polytopes associated with the widths and the heights of the rectangles, respectively. In addition, we illustrate how to solve the associated separation problem as a bilinear program, for which we develop a solution method that turns out to be fast in practice, and show that the integer solutions that satisfy all these constraints generally correspond to vertices of the original convex hull for the benchmark instances in the literature. The same tools are used to derive lower bounds for the two-dimensional bin packing problem, corresponding to the determination of an optimal pair of so-called dual feasible functions. These lower bounds in many cases equal those obtained by the customary set covering formulation (for which column generation is very hard), but are computable within a time that is smaller by some orders of magnitude. This allows us to close a few of the benchmark instances in the literature.

New resolution algorithm and pretreatments for the two-dimensional bin-packing problem

The two-dimensional bin-packing (2BP) problem involves packing a given set of rectangles A into a minimum number of larger identical rectangles called bins. In this paper, we introduce the concept of dependent orientation items that have special characteristics, and give the formulation that characterizes these items. Then we propose three pretreatments for the non-oriented version of the problem. These pretreatments allow finding optimal packing of some items subsets of the given instance. They enable increasing the total area of the items and consequently the continuous lower bound. Finally, we propose a new heuristic method based on a best-fit algorithm adapted to the 2BP problem. Numerical experiments show that this method is competitive with the heuristic and metaheuristic algorithms proposed in the literature for the considered problem in respect of both the quality of the solution and the computing time.

Using Decomposition Techniques and Constraint Programming for Solving the Two-Dimensional Bin-Packing Problem

The two-dimensional bin-packing problem is the problem of orthogonally packing a given set of rectangles into a minimum number of two-dimensional rectangular bins. The problem is 𝒩𝒫-hard and very difficult to solve in practice as no good mixed integer programming (MIP) formulation has been found for the packing problem. We propose an algorithm based on the well-known Dantzig-Wolfe decomposition where the master problem deals with the production constraints on the rectangles while the subproblem deals with the packing of rectangles into a single bin. The latter problem is solved as a constraint-satisfaction problem (CSP), which makes it possible to formulate a number of additional constraints that may be difficult to formulate as MIP models. This includes guillotine-cutting requirements, relative positions, fixed positions and irregular bins. The CSP approach uses forward propagation to prune inferior arrangements of rectangles. Unsuccessful attempts to pack rectangles into a bin are brought back to the master model as valid inequalities. Hence, CSP is used not only to solve the pricing problem but also to generate valid inequalities in a branch-and-cut system. Using delayed column-generation, we obtain lower bounds of very good quality in reasonable time. In all instances considered, we obtain similar or better bounds than previously published. Several instances with up to n = 100 rectangles are solved to optimality through the developed branch-and-price-and-cut algorithm.

Two-dimensional bin packing with one-dimensional resource augmentation

The two-dimensional bin packing problem is a generalization of the classical bin packing problem and is defined as follows. Given a collection of rectangles specified by their width and height, pack these into a minimum number of square bins of unit size. Recently, the problem was proved to be APX-hard even in the asymptotic case, i.e. when the optimum solutions require a large number of bins [N. Bansal, J. Correa, C. Kenyon, M. Sviridenko, Bin packing in multiple dimensions: Inapproximability results and approximation schemes, Math. Oper. Res. 31 (1) (2006) 31–49]. On the positive side, there exists a polynomial time algorithm that uses OPT bins whose sides have length ( 1 + ϵ ) , where OPT denotes the number of unit sized bins used by the optimum solution [N. Bansal, J. Correa, C. Kenyon, M. Sviridenko, Bin packing in multiple dimensions: Inapproximability results and approximation schemes, Math. Oper. Res. 31 (1) (2006) 31–49]. A natural question that remains is the approximability of the problem when we are allowed to relax the size of the unit bins in only one dimension. In this paper, we show that there exists an asymptotic polynomial time approximation scheme for packing rectangles into bins of size 1 × ( 1 + ϵ ) .

On Delay-Bounded Multimedia Internet-Call Access Control in a WCDMA Common Channel

We address call access control in the Wideband Code Divisional Multiple Access (WCDMA) downlink common channels. Proposed is a policy consisting of two phases, the first screening calls for admission, and the second scheduling call services according to the most popular preemptive FIFO rule. The scheduling mechanism is first depicted on the infinite vertical time-strip with width representing the aggregate capacity of common channels, by denoting a call service as positioning the corresponding call-rectangle thereon. Enabled by this depiction, we apply the existing results on the two-dimensional (2-D) bin packing problem in predicting the sojourn time of an arriving call. The first phase of call admission control is based on the predictions, from the characteristics of which calls, once admitted, are guaranteed to be serviced within allowable delay. The performance of the proposed access control policy is extensively tested with realistic input data. The results are found convincingly affirmative for its practical applicability

This side up!

We consider two- and three-dimensional bin-packing problems where 90° rotations are allowed. We improve all known asymptotic performance bounds for these problems. In particular, we show how to combine ideas from strip packing and two-dimensional bin packing to give a new algorithm for the three-dimensional strip packing problem where boxes can only be rotated sideways. We propose to call this problem “This side up”. Our algorithm has an asymptotic performance bound of 9/4.

New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

The two-dimensional bin-packing problem (2 BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2 BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2 BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.

The two-dimensional bin packing problem with variable bin sizes and costs

The two-dimensional variable sized bin packing problem (2DVSBPP) is the problem of packing a set of rectangular items into a set of rectangular bins. The bins have different sizes and different costs, and the objective is to minimize the overall cost of bins used for packing the rectangles. We present an integer-linear formulation of the 2DVSBPP and introduce several lower bounds for the problem. By using Dantzig–Wolfe decomposition we are able to obtain lower bounds of very good quality. The LP-relaxation of the decomposed problem is solved through delayed column generation, and an exact algorithm based on branch-and-price is developed. The paper is concluded with a computational study, comparing the tightness of the various lower bounds, as well as the performance of the exact algorithm for instances with up to 100 items.