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A review on learning to solve combinatorial optimisation problems in manufacturing
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 …
rapid development of science and technology and the progress of human society, the …
Neural combinatorial optimization with heavy decoder: Toward large scale generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …
challenging combinatorial optimization problems without specialized algorithm design by …
T2t: From distribution learning in training to gradient search in testing for combinatorial optimization
Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
DeepACO: Neural-enhanced ant systems for combinatorial optimization
Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …
Towards omni-generalizable neural methods for vehicle routing problems
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 …
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
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 …
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
Deep policy dynamic programming for vehicle routing problems
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 …
Recently, end-to-end deep learning methods have been proposed to learn approximate …
Glop: Learning global partition and local construction for solving large-scale routing problems in real-time
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 …
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …
Simulation-guided beam search for neural combinatorial optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …
discover powerful heuristics for solving complex real-world problems. While neural …
Machine learning to solve vehicle routing problems: A survey
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …