The bin packing problem is a well-studied problem in combinatorial optimization. In the
classical bin packing problem, we are given a list of real numbers in (0, 1] and the goal is to …

The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem,
which is defined as the placement of a set of different-sized items into identical bins such …

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the
best human-level performance in many domains, yet there are few use-cases in industrial or …

Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …

Logistical activities have a significant global environmental impact, necessitating the
adoption of green logistics practices to mitigate environmental effects. The COVID-19 …

We review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …

We consider a class of online decision-making problems with exchangeable actions, where
in each period a controller is presented an input type drawn from some stochastic arrival …

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 …

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as
robotics and games. We build on this previous work by applying RL algorithms to a selection …

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 …