Abstract Three-Dimensional Packing Problems (3D-PPs) can be applied to effectively
reduce logistics costs in various areas, such as airline cargo management and warehouse …

Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

The advice model of online computation captures the setting in which the online algorithm is
given some information concerning the request sequence. This paradigm allows to establish …

In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive
sequentially and require immediate packing decisions without any information about the …

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 …

We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D
bin packing problem for an arbitrary number of bins and any bin size. The focus is on …

We improve the lower bound on the asymptotic competitive ratio of any online algorithm for
bin packing to above 1.54278. We demonstrate for the first time the advantage of branching …

We consider the fully dynamic bin packing problem, where items arrive and depart in an
online fashion and repacking of previously packed items is allowed. The goal is, of course …

The online bin packing problem is a well-known optimization challenge that finds application
in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called …

With declaring the highly transmissible COVID-19 as a pandemic, an unprecedented strain
on healthcare infrastructures worldwide occurred. An enormous shortage in the personal …