Jump to Content
Your Recent History
View Cart
Logged Out Log In
SIAM J. Comput. 22, pp. 46-56 (11 pages)
Tight Worst-Case Performance Bounds for Next-$k$-Fit Bin Packing
Alerts
Alert Me When Cited
Alert Me When Corrected
Tools
Download Citation
Add to MyScitation
Blog This Article
Print-Friendly
Research Toolkit
Share
Email Abstract
Connotea
CiteULike
del.icio.us
BibSonomy
Tweet this Article
Add to FacebookThe bin packing problem is to pack a list of reals in $( {0,1} ]$ into unit-capacity bins using the minimum number of bins. Let $R[A]$ be the limiting worst value for the ratio ${{A(L)} / {L^ * }}$ as $L^ * $ goes to $\infty $, where $A(L)$ denotes the number of bins used in the approximation algorithm $A$, and $L^ * $ denotes the minimum number of bins needed to pack $L$. Obviously, $R[A]$ reflects the worst-case behavior of $A$. For Next-$k$-Fit($NkF$ for short, $k \geqslant 2$), which is a linear time approximation algorithm for bin packing, it was known that $1.7 + \frac{3}{{10(k - 1)}} \leqslant R[NkF] \leqslant 2$. In this paper, a tight bound $R[NkF] = 1.7 + \frac{3}{{10(k - 1)}}$ is proved.
© 1993 Society for Industrial and Applied Mathematics
KEYWORDS
PUBLICATION DATA
ARTICLE DATA
History
Received May 06, 1989
Accepted October 01, 1991
Accepted October 01, 1991
Digital Object Identifier
For access to fully linked references, you need to log in.
For access to citing articles, you need to log in.
ALL SIAM Content
Scitation
Google Scholar