New Approximability Results for Two-Dimensional Bin Packing

We study the two-dimensional bin packing problem: Given a list of n rectangles the objective is to find a feasible, i.e. axis-parallel and non-overlapping, packing of all rectangles into the minimum number of unit sized squares, also called bins. Our problem consists of two versions; in the first version it is not allowed to rotate the rectangles while in the other it is allowed to rotate the rectangles by 90∘, i.e. to exchange the widths and the heights. Two-dimensional bin packing is a generalization of its one-dimensional counterpart and is therefore strongly NP-hard. Furthermore Bansal et al. showed that even an APTAS is ruled out for this problem, unless P=NP. This lower bound of asymptotic approximability was improved by Chlebik and Chlebikova to values 1+1/3792 and 1+1/2196 for the version with and without rotations, respectively. On the positive side there is an asymptotic 1.69.. approximation by Caprara without rotations and an asymptotic 1.52... approximation by Bansal et al.for both versions. We give a new asymptotic upper bound for both versions of our problem: For any fixed ε and any instance that fits optimally into OPT bins, our algorithm computes a packing into (3/2+ε)⋅OPT+69 bins in the version without rotations and (3/2+ε)⋅OPT+39 bins in the version with rotations. The algorithm has polynomial running time in the input length. In our new technique we consider an optimal packing of the rectangles into the bins. We cut a small vertical or horizontal strip out of each bin and move the intersecting rectangles into additional bins. This enables us to either round the widths of all wide rectangles, or the heights of all long rectangles in this bin. After this step we round the other unrounded side of these rectangles and we achieve a solution with a simple structure and only few types of rectangles. Our algorithm initially rounds the instance and computes a solution that nearly matches the modified optimal solution

A 2-approximation for 2D bin packing

We study the two|-dimensional geometrical bin packing problem (2DBP): given a list of rectangles, provide a packing of all these into the smallest possible number of $1\times1$ bins without rotating the rectangles. We present a $2$|-approximate algorithm, which improves over the previous best known ratio of $3$, matches the best results for the rotational case and also matches the known lower bound of approximability. Our approach makes strong use of a recently-discovered PTAS for a related knapsack problem and a new algorithm that can pack instances into \OPT+2 bins for any constant \OPT

Bin packing and covering with longest items at the bottom: online version

We consider the NP hard problems of online bin packing and online bin covering while requiring that larger (or longer, in the one-dimensional case) items be placed at the bottom of the bins, below smaller (or shorter) items. Bin sizes can be uniform or variable. If variable, the bin sizes are drawn from a finite collection. In uniform sized online bin packing, we prove an upper bound of two on the approximation ratio for special cases of the problem and provide computational results for the general case using a variation of the first fit heuristic. In uniform sized online bin covering, we prove a non-approximability result and present a modified first fit heuristic. In online variable-sized bin covering, we show that the approximation ratio guaranteed by our heuristic is a function of bin lengths

On the generalized bin packing problem

The generalized bin packing problem (GBPP) is a novel packing problem arising in many transportation and logistic settings, characterized by multiple items and bins attributes and the presence of both compulsory and non-compulsory items. In this paper, we study the computational complexity and the approximability of the GBPP. We prove that the GBPP cannot be approximated by any constant, unless P = NP. We also study the particular case of a single bin type and show that when an unlimited number of bins is available, the GBPP can be reduced to the bin packing with rejection (BPR) problem, which is approximable. We also prove that the GBPP satisfies Bellman’s optimality principle and, exploiting this result, we develop a dynamic programming solution approach. Finally, we study the behavior of standard and widespread heuristics such as the first fit, best fit, first fit decreasing, and best fit decreasing.We show that while they successfully approximate previous versions of bin packing problems, they fail to approximate the GBPP