Bin packing is a classic optimization problem with a wide range of applications from load balancing in networks 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 consistency (i.e., the competitive ratio assuming no prediction error) and robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades gently as a function of the prediction error. This is the first theoretical study of online bin packing in the realistic setting of erroneous predictions, as well as the first experimental study in the setting in which the input is generated according to both static and evolving distributions. Previous work on this problem has only addressed the extreme cases with respect to the prediction error, has relied on overly powerful and error-free prediction oracles, and has focused on experimental evaluation based on static input distributions.

翻译：Bin包装是一个典型的优化问题,其应用范围很广,从网络的负载平衡到供应链管理。在这项工作中,我们研究了这一问题的在线变式,在这种变式中,各种大小的物品的顺序必须放在一个统一容量的最小箱数中。在线算法得到加强,对序列中物品大小的频率进行了(可能错误的)预测。我们设计并分析了在线算法,在一致性(即假设没有预测错误的竞争性比率)和稳健性(即对抗性错误下的竞争性比率)和稳健性(即对抗性错误下的竞争性比率)之间进行了有效的权衡,其性能作为预测错误的函数而轻度下降。这是在现实的错误预测环境中对在线垃圾包装进行的第一次理论研究,也是在根据静态和变化分布生成投入的环境下进行的第一次实验性研究。以前关于这一问题的工作仅涉及预测错误的极端案例,它依靠过强和无错误的预测,并侧重于基于静态输入分布的实验性评估。