On the absolute approximation ratio for First Fit and related results

Joan Boyar, György Dósa, Leah Epstein

Research output: Contribution to journalArticlepeer-review


We revisit three famous bin packing algorithms, namely Next Fit (NF), Worst Fit (WF) and First Fit (FF). We compare the approximation ratio of these algorithms as a function of the total size of the input items α. We give a complete analysis of the worst case behavior of WF and NF, and determine the ranges of α for which FF has a smaller approximation ratio than WF and NF. In addition, we prove a new upper bound of 127≈1.7143 on the absolute approximation ratio of FF, improving over the previously known upper bound of 1.75, given by Simchi-Levi. This property of FF is in contrast to the absolute approximation ratios of WF and NF, which are both equal to 2.

Original languageEnglish
Pages (from-to)1914-1923
Number of pages10
JournalDiscrete Applied Mathematics
Issue number13-14
StatePublished - Sep 2012

Bibliographical note

Funding Information:
The first author was partially supported by the Danish Council for Independent Research, Natural Sciences . The second author was partially supported by Project K-TET_10-1-2011-0064833, Algorithms and Visualization for difficult combinatorial optimization problems .


  • Bin packing
  • First Fit
  • Next Fit
  • Worst Fit

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Applied Mathematics


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