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

There has been a paradigm-shift in urban logistic services in the last years; demand for real-
time, instant mobility and delivery services grows. This poses new challenges to logistic …

We propose an end-to-end learning framework based on hierarchical reinforcement
learning, called H-TSP, for addressing the large-scale Traveling Salesman Problem (TSP) …

Offline reinforcement learning typically introduces a hierarchical structure to solve the long-
horizon problem so as to address its thorny issue of variance accumulation. Problems of …

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 …

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 …

Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …

The deployment of Industry 4.0 emerging technologies such as Augmented reality (AR),
Virtual reality (VR), and collaborative Robots enhances flexibility and precision in the …

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and
delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows …

Due to the ubiquitous real-world applications of logistics and supply chain management
over the past two decades, dynamic pickup and delivery problems (DPDPs), as a subclass …