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 …

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 …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …

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 …

Recent neural methods for vehicle routing problems always train and test the deep models
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …

Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (ie,
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …

End-to-end training of neural network solvers for graph combinatorial optimization problems
such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently …

Learning-based algorithms to solve TSP are getting popular in recent years, but most
existing works cannot solve very large-scale TSP instances within a limited time. To solve …

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