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 …
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 …
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 …
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 …
Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift
While performing favourably on the independent and identically distributed (iid) instances,
most of the existing neural methods for vehicle routing problems (VRPs) struggle to …
most of the existing neural methods for vehicle routing problems (VRPs) struggle to …
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 …
Multi-task learning for routing problem with cross-problem zero-shot generalization
Vehicle routing problems (VRP) are very important in many real-world applications and has
been studied for several decades. Recently, neural combinatorial optimization (NCO) has …
been studied for several decades. Recently, neural combinatorial optimization (NCO) has …
Learning to construct a solution for the agile satellite scheduling problem with time-dependent transition times
The agile earth observation satellite scheduling problem (AEOSSP) with time-dependent
transition times is a complex combinational optimization problem that has emerged from the …
transition times is a complex combinational optimization problem that has emerged from the …
Neural multi-objective combinatorial optimization with diversity enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO)
problems solely rely on decomposition, which often leads to repetitive solutions for the …
problems solely rely on decomposition, which often leads to repetitive solutions for the …