Note: Random-order bin packing

Authors:
Edward G. Coffman, Jr.;János Csirik;Lajos Rónyai;Ambrus Zsbán
Affiliations:
Department of Electrical Engineering, Columbia University, 1312 S.W. Mudd, 500 West 120th Street, New York, NY 10027, USA;Department of Computer Science, University of Szeged, Árpád tér 2, H-6720 Szeged, Hungary;MTA SZTAKI, Kende u. 13-17, Budapest, Hungary and Department of Algebra, Budapest University of Technology and Economics, Budapest, Hungary;Department of Algebra, Budapest University of Technology and Economics, Budapest, Hungary
Venue:
Discrete Applied Mathematics
Year:
2008

Citing 4
Cited 1

Best-fit bin-packing with random order

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Abstract

The average-case analysis of algorithms usually assumes independent, identical distributions for the inputs. In [C. Kenyon, Best-fit bin-packing with random order, in: Proc. of the Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, 1996, pp. 359-364] Kenyon introduced the random-order ratio, a new average-case performance metric for bin packing heuristics, and gave upper and lower bounds for it for the Best Fit heuristics. We introduce an alternative definition of the random-order ratio and show that the two definitions give the same result for Next Fit. We also show that the random-order ratio of Next Fit equals to its asymptotic worst-case, i.e., it is 2.