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 …
A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot
L Wu, X Huang, J Cui, C Liu, W Xiao - Expert Systems with Applications, 2023 - Elsevier
As the key point for auto-navigation of mobile robot, path planning is a research hotspot in
the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the …
the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the …
Large-scale dynamic scheduling for flexible job-shop with random arrivals of new jobs by hierarchical reinforcement learning
As the intelligent manufacturing paradigm evolves, it is urgent to design a near real-time
decision-making framework for handling the uncertainty and complexity of production line …
decision-making framework for handling the uncertainty and complexity of production line …
Deep reinforcement learning for the dynamic and uncertain vehicle routing problem
W Pan, SQ Liu - Applied Intelligence, 2023 - Springer
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 …
research topic in the field of intelligent transportation. While the routing of urban logistic …
Solving combinatorial optimization problems over graphs with BERT-Based Deep Reinforcement Learning
Q Wang, KH Lai, C Tang - Information Sciences, 2023 - Elsevier
Combinatorial optimization, such as vehicle routing and traveling salesman problems for
graphs, is NP-hard and has been studied for decades. Many methods have been proposed …
graphs, is NP-hard and has been studied for decades. Many methods have been proposed …
Machine learning approach for truck-drones based last-mile delivery in the era of industry 4.0
A Arishi, K Krishnan, M Arishi - Engineering Applications of Artificial …, 2022 - Elsevier
Under the vision of industry 4.0, the integration of drones in last-mile delivery can transform
traditional delivery practices and provide competitive advantages. However, the …
traditional delivery practices and provide competitive advantages. However, the …
Efficient meta neural heuristic for multi-objective combinatorial optimization
J Chen, J Wang, Z Zhang, Z Cao… - Advances in Neural …, 2024 - proceedings.neurips.cc
Recently, neural heuristics based on deep reinforcement learning have exhibited promise in
solving multi-objective combinatorial optimization problems (MOCOPs). However, they are …
solving multi-objective combinatorial optimization problems (MOCOPs). However, they are …
Smart visual sensing for overcrowding in COVID-19 infected cities using modified deep transfer learning
Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19
can be prevented from spreading rapidly in crowded areas by implementing multiple …
can be prevented from spreading rapidly in crowded areas by implementing multiple …
Generative ai and process systems engineering: The next frontier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …