Online Bin Packing with Advice
Joan Boyar, Shahin Kamali, Kim Skak Larsen, Alejandro López-Ortiz

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 requests. 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 + ε. 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 5/4.

open access (269 KB)
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Last modified: Wed Apr 5 14:06:28 CEST 2017


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