Online Bin Packing with Predictions

TL;DR

This paper introduces online bin packing algorithms that leverage learnable predictions to improve efficiency, balancing performance under accurate predictions and robustness against errors, supported by theoretical and experimental analysis.

Contribution

It presents the first study of online bin packing algorithms that incorporate learnable predictions, analyzing their tradeoffs between consistency and robustness.

Findings

Algorithms achieve near-optimal performance with low prediction error.

Performance degrades gracefully as prediction error increases.

Experimental results validate theoretical guarantees.

Abstract

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.