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 their consistency (i.e., the competitive ratio assuming no prediction error) and their robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades gently as a function of the error. Previous work on this problem has only addressed the extreme cases with respect to the prediction error, and has relied on overly powerful and error-free prediction oracles.

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数的垃圾箱中。在线算法因对物品大小在序列中的频率的预测(可能是错误的)而得到加强。我们设计和分析在线算法,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)之间作出有效的权衡(即对抗性错误下的竞争性比率),其性能会轻轻地降低作为错误的函数。以前关于该问题的工作只处理了预测错误的极端情况,并依靠了过于强大和无错误的预测。