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On Three-Dimensional Packing

Authors: Keqin Li 0001; Kam-Hoi Cheng;

On Three-Dimensional Packing

Abstract

The three-dimensional packing problem is discussed in this paper. The problem is a generalization of the one- and two-dimensional packing problems. It is demonstrated that some basic packing strategies such as NFDH and FFDH for two-dimensional packing have unbounded worst-case performance ratios in the three-dimensional case. Let $r(A)$ denote the asymptotic performance bound of an approximation algorithm A. An approximation algorithm G is developed, and it is shown that $4.333 \leqq r(G) \leqq 4.571$. The algorithm is improved to algorithm C and it is proven that $r(C) = 3.25$. For the special case when all boxes have square bottoms, the two algorithms are adapted to algorithms $G_1 $ and $C_1 $, respectively, with $r(G_1 ) = 4$ and $r(C_1 ) = 2.6875$. For the case when both sides of the bottom of a box are no larger then ${1 / m}$, two families of algorithms, $G^* _m (m \geqq 3)$ and $C^* _m (m \geqq 2)$, are presented. It is shown that $r(G^* _m ) = {m / {(m -2)}}$ and $r(C^* _m ) = {{(m+1)} / {(m-1)}}...

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
48
Top 10%
Top 1%
Average
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