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Algorithmica

, Volume 74, Issue 1, pp 507–527 | Cite as

Online Bin Packing with Advice

  • Joan Boyar
  • Shahin Kamali
  • Kim S. Larsen
  • Alejandro López-Ortiz
Article

Abstract

We consider the online bin packing problem under the advice complexity model where the “online constraint” is relaxed and an algorithm receives partial information about the future items. We provide tight upper and lower bounds for the amount of advice an algorithm needs to achieve an optimal packing. We also introduce an algorithm that, when provided with \(\log n + o(\log n)\) bits of advice, achieves a competitive ratio of \(3/2\) for the general problem. This algorithm is simple and is expected to find real-world applications. We introduce another algorithm that receives \(2n + o(n)\) bits of advice and achieves a competitive ratio of \(4/3 + \varepsilon \). Finally, we provide a lower bound argument that implies that advice of linear size is required for an algorithm to achieve a competitive ratio better than 9/8.

Keywords

Online algorithms Advice complexity Bin packing 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Joan Boyar
    • 1
  • Shahin Kamali
    • 2
  • Kim S. Larsen
    • 1
  • Alejandro López-Ortiz
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark
  2. 2.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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