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Online Bin Packing with Advice of Small Size

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Abstract

In this paper, we study the advice complexity of the online bin packing problem. In this well-studied setting, the online algorithm is supplemented with some additional information concerning the input. We improve upon both known upper and lower bounds of online algorithms for this problem. On the positive side, we first provide a relatively simple algorithm that achieves a competitive ratio arbitrarily close to 1.5, using constant-size advice. Our result implies that 16 bits of advice suffice to obtain a competitive ratio better than any online algorithm without advice, thus improving the previously known bound of O(log(n)) bits required to attain this performance. In addition, we introduce a more complex algorithm that still requires only constant-size advice, and has a competitive ratio arbitrarily close to 1.47012. This is the currently best performance of any online bin packing algorithm with sublinear advice. On the negative side, we extend a construction due to Boyar et al. (Algorithmica 74(1), 507–527 2016) so as to show that no online algorithm with sub-linear advice can be 7/6-competitive, improving on the lower bound of 9/8 from Boyar et al.

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Notes

  1. Technically, the statement of Lemma 16 is very similar to Lemma 9 in [13]. We note, however, that the latter is correct only when the number of 0s is n/2. To avoid any ambiguity, the statement of Lemma 16 is parameterized by β, as opposed to Lemma 9 in [13].

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Acknowledgments

Research supported in part by project ANR-11-BS02-0015 “New Techniques in Online Computation–NeTOC”. A preliminary version of this paper appeared in the Proceedings of the 14th International Symposium on Algorithms and Data Structures (WADS), 2015 [2].

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Authors and Affiliations

  1. Laboratoire d’informatique de Paris-6, LIP6, Sorbonne Université, Paris, France

    Spyros Angelopoulos & Christoph Dürr

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada

    Shahin Kamali

  3. Department of Computer Science, University of Wisconsin, Madison, WI, USA

    Marc P. Renault

  4. CNRS, Paris, France

    Adi Rosén

  5. Université Paris Diderot, Paris, France

    Adi Rosén

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  1. Spyros Angelopoulos
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  2. Christoph Dürr
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Correspondence to Shahin Kamali.

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Angelopoulos, S., Dürr, C., Kamali, S. et al. Online Bin Packing with Advice of Small Size. Theory Comput Syst 62, 2006–2034 (2018). https://doi.org/10.1007/s00224-018-9862-5

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