2012
DOI: 10.1007/s10878-012-9457-z
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Online algorithms for 1-space bounded multi dimensional bin packing and hypercube packing

Abstract: In this paper, we study 1-space bounded multi-dimensional bin packing and hypercube packing. A sequence of items arrive over time, each item is a ddimensional hyperbox (in bin packing) or hypercube (in hypercube packing), and the length of each side is no more than 1. These items must be packed without overlapping into d-dimensional hypercubes with unit length on each side. In d-dimensional space, any two dimensions i and j define a space P ij . When an item arrives, we must pack it into an active bin immediat… Show more

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Cited by 9 publications
(7 citation statements)
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“…The last article also contains an algorithm for packing squares with competitive ratio at most 4.5. In [9], where a 2 d+1 -competitive algorithm was described. The aim of this paper is to improve the upper bound (2 3+1 ) in the 3-dimensional case.…”
Section: A Related Workmentioning
confidence: 99%
“…The last article also contains an algorithm for packing squares with competitive ratio at most 4.5. In [9], where a 2 d+1 -competitive algorithm was described. The aim of this paper is to improve the upper bound (2 3+1 ) in the 3-dimensional case.…”
Section: A Related Workmentioning
confidence: 99%
“…An algorithm with competitive 2 d+1 that uses only one active bin is presented in the article [31]. In the same paper the authors provide 1-space bounded algorithm for hyperbox packing with competitive ratio 4 d .…”
Section: Introductionmentioning
confidence: 99%
“…A 3-space bounded 3.577-competitive square packing method is given in Grzegorek and Januszewski (2014). The d-dimensional case of one-space bounded hyperbox packing is considered in Zhang et al (2013). The authors give an online algorithm with competitive ratio equal to 4 d .…”
Section: Introductionmentioning
confidence: 99%
“…We focus on the problem of online packing of d-dimensional hyperboxes into one active bin. The paper contains two algorithms D 1 (d) and D 2 (d): the first method is a 3.5 d -competitive algorithm and for d < 17 works better than the second algorithm having the 12 • 3 d competitive ratio, which is a significant improvement of the ratio 4 d from Zhang et al (2013). Both algorithms are defined inductively from lower dimensions to higher.…”
Section: Introductionmentioning
confidence: 99%