One-dimensional Bin Packing Problem Research Articles

Average-weight-controlled bin-oriented heuristics for the one-dimensional bin-packing problem

Bin-oriented heuristics for one-dimensional bin-packing problem construct solutions by packing one bin at a time. Several such heuristics consider two or more subsets for each bin and pack the one with the largest total weight. These heuristics sometimes generate poor solutions, due to a tendency to use many small items early in the process. To address this problem, we propose a method of controlling the average weight of items packed by bin-oriented heuristics. Constructive heuristics and an improvement heuristic based on this approach are introduced. Additionally, reduction methods for bin-oriented heuristics are presented. The results of an extensive computational study show that: (1) controlling average weight significantly improves solutions and reduces computation time of bin-oriented heuristics; (2) reduction methods improve solutions and processing times of some bin-oriented heuristics; and (3) the new improvement heuristic outperforms all other known complex heuristics, in terms of both average solution quality and computation time.

Packing of one-dimensional bins with contiguous selection of identical items: An exact method of optimal solution

Consideration was given to the one-dimensional bin packing problem under the conditions for heterogeneity of the items put into bins and contiguity of choosing identical items for the next bin. The branch-and-bound method using the "next fit" principle and the "linear programming" method were proposed to solve it. The problem and its solution may be used to construct an improved lower bound in the problem of two-dimensional packing.

A heuristic for solving large bin packing problems in two and three dimensions

The more-dimensional bin packing problem (BPP) considered here requires packing a set of rectangular-shaped items into a minimum number of identical rectangular-shaped bins. All items may be rotated and the guillotine cut constraint has to be respected. A straightforward heuristic is presented that is based on a method for the container loading problem following a wall-building approach and on a method for the one-dimensional BPP. 1,800 new benchmark instances are introduced for the two-dimensional and three-dimensional BPP. The instances include more than 1,500 items on average. Applied to these very large instances, the heuristic generates solutions of acceptable quality in short computation times. Moreover, the influence of different instance parameters on the solution quality is investigated by an extended computational study.

Last two fit augmentation to the well-known construction heuristics for one-dimensional bin-packing problem: an empirical study

The bin-packing problem is a combinatorial optimization problem that has been widely applied in many manufacturing and logistics industries. It requires packing a set of different-sized items into a minimum number of identical bins. This paper introduces the last two fit (L2F) augmentation to the well-known next fit decreasing, first fit decreasing, and best fit decreasing algorithms for the one-dimensional bin-packing problem. L2F checks whether an additional item can be packed into the bin when an item is packed; if it cannot be done, replacement of the item with a pair of two unpacked items whose combined size is larger than the item is pursued. The focus of this paper is on improving the well-known construction heuristics with L2F and comparing their performance to the original heuristics. Experiments with benchmark problem sets show the effectiveness of the L2F augmented algorithms.

Relaxations and exact solution of the variable sized bin packing problem

We address a generalization of the classical one-dimensional bin packing problem with unequal bin sizes and costs. We investigate lower bounds for this problem as well as exact algorithms. The main contribution of this paper is to show that embedding a tight network flow-based lower bound, dominance rules, as well as an effective knapsack-based heuristic in a branch-and-bound algorithm yields very good performance. In addition, we show that the particular case with all weight items larger than a third the largest bin capacity can be restated and solved in polynomial-time as a maximum-weight matching problem in a nonbipartite graph. We report the results of extensive computational experiments that provide evidence that large randomly generated instances are optimally solved within moderate CPU times.

A new destructive bounding scheme for the bin packing problem

In this paper, we present a new lower bounding scheme for the one-dimensional bin packing problem based on a destructive approach and we prove its effectiveness to solve hard instances. Performance comparison to other available lower bounds shows the effectiveness of our proposed lower bounds.

Complementary column generation and bounding approaches for set partitioning formulations

We present a complementary column generation feature that produces tight upper bounds, thereby enhancing heuristic and exact column generation approaches for (minimization) set partitioning formulations that possess dense column structures. We also introduce a duality-based lower bound that prompts a useful termination criterion, which can be utilized to mitigate the tailing-off effect induced by column generation approaches. Computations are presented for the one-dimensional bin packing problem and a joint vehicle assembly-routing problem.

Evolutionary based heuristic for bin packing problem

In this paper, we investigate the use of evolutionary based heuristic to the one-dimensional bin packing problem (BPP). Unlike other evolutionary heuristics used with optimization problems, a non-specialized and non-hybridized algorithm is proposed and analyzed for solving BPP. The algorithm uses a modified permutation with separators encoding scheme, unique concept of separators’ movements during mutation, and separators removal as a technique of problem size reduction. The set of experiments confirmed that the proposed approach is comparable to much more complicated algorithms.

Average Case Analysis of Bounded Space Bin Packing Algorithms

We consider the one-dimensional bin packing problem and analyze the average case performance of bounded space algorithms. The analysis covers a wide variety of bin packing algorithms including Next-K Fit, K-Bounded Best Fit and Harmonic algorithms, as well as of others. We assume discrete item sizes, an assumption which is true in most real-world applications of bin packing. We present a Markov chains method which is general enough to calculate results for any i.i.d. discrete item size distribution. The paper presents many results heretofore unknown or conjectured from simulation. Some of the results are surprising as they differ considerably from results for continuous distributions.

A one-dimensional bin packing problem with shelf divisions

Given bins of size B, non-negative values d and Δ , and a list L of items, each item e ∈ L with size s e and class c e , we define a shelf as a subset of items packed inside a bin with total item sizes at most Δ such that all items in this shelf have the same class. Two subsequent shelves must be separated by a shelf division of size d. The size of a shelf is the total size of its items plus the size of the shelf division. The class constrained shelf bin packing problem (CCSBP) is to pack the items of L into the minimum number of bins, such that the items are divided into shelves and the total size of the shelves in a bin is at most B. We present hybrid algorithms based on the First Fit (Decreasing) and Best Fit (Decreasing) algorithms, and an APTAS for the problem CCSBP when the number of different classes is bounded by a constant C.

Solving the one-dimensional bin packing problem with a weight annealing heuristic

In this paper, we develop a new procedure that uses the concept of weight annealing to solve the one-dimensional bin packing problem. Our procedure is straightforward and easy to follow. We apply it to 1587 instances taken from benchmark problem sets and compare our results to those found in the literature. We find that our procedure produces very high-quality solutions very quickly and generates several new optimal solutions.

Infimum-convolution description of concentration properties of product probability measures, with applications

This paper is devoted to the concentration properties of product probability measures μ = μ 1 ⊗ ⋯ ⊗ μ n , expressed in term of dimension-free functional inequalities of the form ( ∫ e α Q α f d μ ) 1 α ( ∫ e − ( 1 − α ) f d μ ) 1 ( 1 − α ) ⩽ 1 , where α is a parameter, 0 < α < 1 , and Q α f is an appropriate infimum-convolution operator. This point of view has been introduced by Maurey [B. Maurey, Some deviation inequalities, Geom. Funct. Anal. 1 (1991) 188–197]. It has its origins in concentration inequalities by Talagrand where the enlargement of sets is done in accordance with the cost function of the operator Q α f (see [M. Talagrand, Concentration of measure and isoperimetric inequalities in product spaces, Publ. Math. Inst. Hautes Études Sci. 81 (1995) 73–205, M. Talagrand, New concentration inequalities in product spaces, Invent. Math. 126 (1996) 505–563, M. Talagrand, A new look at independence, Ann. Probab. 24 (1996) 1–34]). A main application of the functional inequalities obtained here is optimal deviations inequalities for suprema of sums of independent random variables. As example, we also derive classical deviations bounds for the one-dimensional bin packing problem.

Two heuristics for the one-dimensional bin-packing problem

In this paper we have proposed two heuristics for the one-dimensional bin-packing problem. One is a hybrid steady-state grouping genetic algorithm, whereas the other is an improved version of Perturbation MBS’ heuristic (Fleszar and Hindi in Comput Oper Res 29:821–839, 2002). A combined approach using these two heuristics gives results which are comparable with the best methods known so far.

Fast lifting procedures for the bin packing problem

In this paper, we investigate fast lower bounds for the deterministic one-dimensional bin packing problem. We present two variants of a general lifting procedure which aims at systematically tightening a given lower bound. We describe several enhancements which improve the efficiency of the proposed procedure. Extensive numerical experiments show that the proposed lifting procedures consistently improve lower bounds from the literature.

A one-dimensional bin packing problem with shelf divisions

Given bins of size B, non-negative values d and @D, and a list L of items, each item [email protected]?L with size se and class ce, we define a shelf as a subset of items packed inside a bin with total item sizes at most @D such that all items in this shelf have the same class. Two subsequent shelves must be separated by a shelf division of size d. The size of a shelf is the total size of its items plus the size of the shelf division. The class constrained shelf bin packing problem (CCSBP) is to pack the items of L into the minimum number of bins, such that the items are divided into shelves and the total size of the shelves in a bin is at most B. We present hybrid algorithms based on the First Fit (Decreasing) and Best Fit (Decreasing) algorithms, and an APTAS for the problem CCSBP when the number of different classes is bounded by a constant C.

Average-Case Performance Analysis of a 2D Strip Packing Algorithm?NFDH

The two-dimensional strip packing problem is a generalization of the classic one-dimensional bin packing problem. It has many important applications such as costume clipping, material cutting, real-world planning, packing, scheduling etc. Average-case performance analysis of approximation algorithms attracts a lot of attention because it plays a crucial role in selecting an appropriate algorithm for a given application. While approximation algorithms for two-dimensional packing are frequently presented, the results of their average-case performance analyses have seldom been reported due to intractability. In this paper, we analyze the average-case performance of Next Fit Decreasing Height (NFDH) algorithm, one of the first strip packing algorithms proposed by Coffman, Jr. in 1980. We prove that the expected height of packing with NFDH algorithm, when the heights and widths of the rectangle items are independent and both obey (0, 1] uniform distribution, is about n/3, where n is the number of rectangle items. We also validate the theoretical result with experiments.

Polytopes, permutation shapes and bin packing

We define a partition of the unit hypercube into polytopes. These polytopes correspond to arrangements of a sequence of objects into bins in the next fit solution to the one-dimensional bin packing problem. The probability that an arrangement appears is given by the volume of the corresponding polytope in the partition. We show that this volume can be calculated as the solution to a problem of permutation enumeration and apply this technique to analyze the behavior of the next fit algorithm.

An approximation algorithm with absolute worst-case performance ratio 2 for two-dimensional vector packing

The two-dimensional vector packing problem is the generalization of the classical one-dimensional bin packing problem to two dimensions. While an asymptotic polynomial time approximation scheme has been designed for one-dimensional bin packing, the existence of an asymptotic polynomial time approximation scheme for two dimensions would imply P= NP. The existence of an approximation algorithm for the two-dimensional vector packing problem with an asymptotic performance guarantee 2 was an open problem so far. In this paper we present an O(n log n) time algorithm for two-dimensional vector packing with absolute performance guarantee 2.

New heuristics for one-dimensional bin-packing

Several new heuristics for solving the one-dimensional bin packing problem are presented. Some of these are based on the minimal bin slack (MBS) heuristic of Gupta and Ho. A different algorithm is one based on the variable neighbourhood search metaheuristic. The most effective algorithm turned out to be one based on running one of the former to provide an initial solution for the latter. When tested on 1370 benchmark test problem instances from two sources, this last hybrid algorithm proved capable of achieving the optimal solution for 1329, and could find for 4 instances solutions better than the best known. This is remarkable performance when set against other methods, both heuristic and optimum seeking. Scope and purpose Packing items into boxes or bins is a task that occurs frequently in distribution and production. A large variety of different packing problems can be distinguished, depending on the size and shape of the items, as well as on the form and capacity of the bins (H. Dyckhoff and U. Finke, Cutting and Packing in Production and Distribution: a Typology and Bibliography, Springer, Berlin, 1992). Similar problems occur in minimising material wastage while cutting pieces into particular smaller ones and in the scheduling of identical processors in order to minimise total completion time. This work addresses the basic packing problem, known as the one-dimensional bin packing problem, where it is required to pack a number of items into the smallest possible number of bins of pre-specified equal capacity. Even though this problem is simple to state, it is NP hard, i.e., it is unlikely that there exists an algorithm that could solve every instance of it in polynomial time. Solution of more general realistic packing problems is probably contingent upon the availability of effective and computationally efficient solution procedures for the basic problem. In this work we present several heuristics capable of doing that. Extensive computational testing attests to the power of these heuristics, as well as to their computational efficiency.

Performance analysis and improvement for some linear on-line bin-packing algorithms

The study on one-dimensional bin packing problem may bring about many important applications such as multiprocessor scheduling, resource allocating, real-world planning and packing. Harmonic algorithms (including H K, RH, etc.) for bin packing have been famous for their linear-time and on-line properties for a long time. This paper profoundly analyzes the average-case performance of harmonic algorithms for pieces of i.i.d. sizes, provides the average-case performance ratio of H K under (0,d] (d ≤ 1) uniform distribution and the average-case performance ratio of RH under (0,1] uniform distribution. We also finished the discussion of the worst-case performance ratio of RH. Moreover, we propose a new improved version of RH that obtains better worst- and average-case performance ratios.