Online Bin Packing with Advice

Joan Boyar, Shahin Kamali, Kim Skak Larsen, Alejandro López-Ortiz

Research output: Contribution to journalJournal articleResearchpeer-review


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 logn+o(logn) 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 9/8.
Original languageEnglish
Issue number1
Pages (from-to)507-527
Publication statusPublished - 2016


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