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 …

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …

Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …

Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …

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 …

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …

The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …

Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …