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Found 9 papers, 6 papers with code
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last eight years.
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing Problems
In recent years new deep learning approaches to solve combinatorial optimization problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed.
Neural Capacitated Clustering
Recent work on deep clustering has found new promising methods also for constrained clustering problems.
Attention, Filling in The Gaps for Generalization in Routing Problems
Machine Learning (ML) methods have become a useful tool for tackling vehicle routing problems, either in combination with popular heuristics or as standalone models.
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
In this work, we generalize this concept of heterogeneous attentions to the TSPPC.
Learning to Control Local Search for Combinatorial Optimization
Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually NP-hard for considerable problem sizes.
Large Neighborhood Search based on Neural Construction Heuristics
The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach.
RP-DQN: An application of Q-Learning to Vehicle Routing Problems
In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods.
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Many real-world vehicle routing problems involve rich sets of constraints with respect to the capacities of the vehicles, time windows for customers etc.
