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 …

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …

Routing problems are a class of combinatorial problems with many practical applications.
Recently, end-to-end deep learning methods have been proposed to learn approximate …

Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …

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 …

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 …

Using machine learning to solve combinatorial optimization (CO) problems is challenging,
especially when the data is unlabeled. This work proposes an unsupervised learning …

Large-scale Vehicle Routing Problems (VRPs) are widely used in logistics, transportation,
supply chain, and robotic systems. Recently, data-driven VRP heuristics are proposed to …

Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an
extremely large number of variables (columns). CG is the workhorse for tackling large-scale …