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 …

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) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …

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 …

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

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 …

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

Abstract Traveling Salesman Problem (TSP), as a classic routing optimization problem
originally arising in the domain of transportation and logistics, has become a critical task in …

Traditional solvers for tackling combinatorial optimization (CO) problems are usually
designed by human experts. Recently, there has been a surge of interest in utilizing deep …