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 …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
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 …
Neurolkh: Combining deep learning model with lin-kernighan-helsgaun heuristic for solving the traveling salesman problem
We present NeuroLKH, a novel algorithm that combines deep learning with the strong
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …
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 …
Let the flows tell: Solving graph combinatorial problems with gflownets
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …
algorithms, making them a tempting domain to apply machine learning methods. The highly …
Learning to iteratively solve routing problems with dual-aspect collaborative transformer
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 …
problems (VRPs). However, it is less effective in learning improvement models for VRP …
Dimes: A differentiable meta solver for combinatorial optimization problems
Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
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 …