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 …

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …

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 …

In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …

We present NeuroLKH, a novel algorithm that combines deep learning with the strong
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …

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 …

Existing approaches to solving combinatorial optimization problems on graphs suffer from
the need to engineer each problem algorithmically, with practical problems recurring in …