Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
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 …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
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 …
systems in recent years. The aim of this review article is to provide an overview of recent …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
Analytics and machine learning in vehicle routing research
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 …
optimisation problems for which numerous models and algorithms have been proposed. To …
[PDF][PDF] Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience
EA Abaku, TE Edunjobi… - International Journal of …, 2024 - pdfs.semanticscholar.org
Abstract The integration of Artificial Intelligence (AI) into supply chain management has
emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary …
emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary …
[HTML][HTML] A multi-agent approach to the truck multi-drone routing problem
JM Leon-Blanco, PL Gonzalez-R… - Expert Systems with …, 2022 - Elsevier
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 …
visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or …
Reinforcement learning with multiple relational attention for solving vehicle routing problems
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 …
Recent works have shown that attention-based RL models outperform recurrent neural …
Reinforcement learning-based saturated adaptive robust neural-network control of underactuated autonomous underwater vehicles
This paper studies a high-performance intelligent online adaptive robust saturated dynamic
surface control framework for underactuated autonomous underwater vehicles by engaging …
surface control framework for underactuated autonomous underwater vehicles by engaging …