  Bin packing is a classic optimization problem with a wide range of
applications, from load balancing to supply chain management. In this work, we
study the online variant of the problem, in which a sequence of items of
various sizes must be placed into a minimum number of bins of uniform capacity.
The online algorithm is enhanced with a (potentially erroneous) prediction
concerning the frequency of item sizes in the sequence. We design and analyze
online algorithms with efficient tradeoffs between the consistency (i.e., the
competitive ratio assuming no prediction error) and the robustness (i.e., the
competitive ratio under adversarial error), and whose performance degrades
near-optimally as a function of the prediction error. This is the first
theoretical and experimental study of online bin packing under competitive
analysis, in the realistic setting of learnable predictions. Previous work
addressed only extreme cases with respect to the prediction error, and relied
on overly powerful and error-free oracles.
