Heuristic placement routines for two-dimensional bin packing problem.

Problem statement: Cutting and packing (C and P) problems are optimization problems that are concerned in finding a good arrangement of multiple small items into one or more larger objects. Bin packing problem is a type of C AND P problems. Bin packing problem is an important industrial problem where the general objective is to reduce the production costs by maximizing the utilization of the larger objects and minimizing the material used. Approach: In this study, we considered both oriented and non-oriented cases of Two-Dimensional Bin Packing Problem (2DBPP) where a given set of small rectangles (items), was packed without overlaps into a minimum number of identical large rectangles (bins). We proposed heuristic placement routines called the Improved Lowest Gap Fill, LGFi and LGFiOF for solving non-oriented and oriented cases of 2DBPP respectively. Extensive computational experiments using benchmark data sets collected from the literature were conducted to assess the effectiveness of the proposed routines. Results: The computational results were compared with some well known heuristic placement routines. The results showed that the LGFi and LGFiOF are competitive when compared with other heuristic placement routines. Conclusion: Both LGFi and LGFiOF produced better packing quality compared to other heuristic placement routines

A note on the lower bound for online strip packing

This note presents a lower bound of $3/2+\sqrt{33}/6 \approx 2.457$ on the competitive ratio for online strip packing. The instance construction we use to obtain the lower bound was first coined by Brown, Baker and Katseff (1980). Recently this instance construction is used to improve the lower bound in computer aided proofs. We derive the best possible lower bound that can be obtained with this instance construction

Approximating Geometric Knapsack via L-packings

We study the two-dimensional geometric knapsack problem (2DK) in which we are given a set of n axis-aligned rectangular items, each one with an associated profit, and an axis-aligned square knapsack. The goal is to find a (non-overlapping) packing of a maximum profit subset of items inside the knapsack (without rotating items). The best-known polynomial-time approximation factor for this problem (even just in the cardinality case) is (2 + \epsilon) [Jansen and Zhang, SODA 2004]. In this paper, we break the 2 approximation barrier, achieving a polynomial-time (17/9 + \epsilon) < 1.89 approximation, which improves to (558/325 + \epsilon) < 1.72 in the cardinality case. Essentially all prior work on 2DK approximation packs items inside a constant number of rectangular containers, where items inside each container are packed using a simple greedy strategy. We deviate for the first time from this setting: we show that there exists a large profit solution where items are packed inside a constant number of containers plus one L-shaped region at the boundary of the knapsack which contains items that are high and narrow and items that are wide and thin. As a second major and the main algorithmic contribution of this paper, we present a PTAS for this case. We believe that this will turn out to be useful in future work in geometric packing problems. We also consider the variant of the problem with rotations (2DKR), where items can be rotated by 90 degrees. Also, in this case, the best-known polynomial-time approximation factor (even for the cardinality case) is (2 + \epsilon) [Jansen and Zhang, SODA 2004]. Exploiting part of the machinery developed for 2DK plus a few additional ideas, we obtain a polynomial-time (3/2 + \epsilon)-approximation for 2DKR, which improves to (4/3 + \epsilon) in the cardinality case.Comment: 64pages, full version of FOCS 2017 pape

Parameterized Approximation Schemes for Independent Set of Rectangles and Geometric Knapsack

The area of parameterized approximation seeks to combine approximation and parameterized algorithms to obtain, e.g., (1+epsilon)-approximations in f(k,epsilon)n^O(1) time where k is some parameter of the input. The goal is to overcome lower bounds from either of the areas. We obtain the following results on parameterized approximability: - In the maximum independent set of rectangles problem (MISR) we are given a collection of n axis parallel rectangles in the plane. Our goal is to select a maximum-cardinality subset of pairwise non-overlapping rectangles. This problem is NP-hard and also W[1]-hard [Marx, ESA\u2705]. The best-known polynomial-time approximation factor is O(log log n) [Chalermsook and Chuzhoy, SODA\u2709] and it admits a QPTAS [Adamaszek and Wiese, FOCS\u2713; Chuzhoy and Ene, FOCS\u2716]. Here we present a parameterized approximation scheme (PAS) for MISR, i.e. an algorithm that, for any given constant epsilon>0 and integer k>0, in time f(k,epsilon)n^g(epsilon), either outputs a solution of size at least k/(1+epsilon), or declares that the optimum solution has size less than k. - In the (2-dimensional) geometric knapsack problem (2DK) we are given an axis-aligned square knapsack and a collection of axis-aligned rectangles in the plane (items). Our goal is to translate a maximum cardinality subset of items into the knapsack so that the selected items do not overlap. In the version of 2DK with rotations (2DKR), we are allowed to rotate items by 90 degrees. Both variants are NP-hard, and the best-known polynomial-time approximation factor is 2+epsilon [Jansen and Zhang, SODA\u2704]. These problems admit a QPTAS for polynomially bounded item sizes [Adamaszek and Wiese, SODA\u2715]. We show that both variants are W[1]-hard. Furthermore, we present a PAS for 2DKR. For all considered problems, getting time f(k,epsilon)n^O(1), rather than f(k,epsilon)n^g(epsilon), would give FPT time f\u27(k)n^O(1) exact algorithms by setting epsilon=1/(k+1), contradicting W[1]-hardness. Instead, for each fixed epsilon>0, our PASs give (1+epsilon)-approximate solutions in FPT time. For both MISR and 2DKR our techniques also give rise to preprocessing algorithms that take n^g(epsilon) time and return a subset of at most k^g(epsilon) rectangles/items that contains a solution of size at least k/(1+epsilon) if a solution of size k exists. This is a special case of the recently introduced notion of a polynomial-size approximate kernelization scheme [Lokshtanov et al., STOC\u2717]

How to Maximize the Total Area of Rectangles Packed into a Rectangle?

We study an interesting geometric optimization problem. We are given a set of rectangles and a rectangular target area called bin. The goal is to find a feasible packing of a subset of the given rectangles into the bin, i.e. an orthogonal packing without rotation and overlap. The objective is to maximize the total area of rectangles packed. This problem is strongly $\mathcal{NP}$-hard even for squares, therefore there is no fully polynomial time approximation scheme (FPTAS) for this problem, unless $\mathcal{P}=\mathcal{NP}$. The previously best result is a \left(\nicefrac{1}{2}-\varepsilon\right)-approximation by Jansen \& Zhang for our problem. We present a polynomial time approximation scheme (PTAS) for this problem, i.e. a family of algorithms which compute for any accuracy $\varepsilon>0$ in polynomial time a solution with ratio $\left(1-\varepsilon\right)$