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 …

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

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 …

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

Deep Reinforcement Learning has made significant progress in multi-agent systems in
recent years. In this review article, we have focused on presenting recent approaches on …

Accurate and real-time tracking for real-world urban logistics has become a popular
research topic in the field of intelligent transportation. While the routing of urban logistic …

Finding the best sequence of local search operators that yields the optimal performance of
Variable Neighborhood Search (VNS) is an important open research question in the field of …

This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …