In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …

This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …

Abstract The integration of Artificial Intelligence (AI) into supply chain management has
emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary …

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to
visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or …

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs).
Recent works have shown that attention-based RL models outperform recurrent neural …

This paper studies a high-performance intelligent online adaptive robust saturated dynamic
surface control framework for underactuated autonomous underwater vehicles by engaging …