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 …
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 …
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 …
Learning to delegate for large-scale vehicle routing
Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical
applications. While previous heuristic or learning-based works achieve decent solutions on …
applications. While previous heuristic or learning-based works achieve decent solutions on …
Operational Research: methods and applications
Abstract Throughout its history, Operational Research has evolved to include methods,
models and algorithms that have been applied to a wide range of contexts. This …
models and algorithms that have been applied to a wide range of contexts. This …
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 …
Winner takes it all: Training performant RL populations for combinatorial optimization
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …
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 …
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 …