Traveling Salesman Problem
67 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Traveling Salesman Problem
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Libraries
Use these libraries to find Traveling Salesman Problem models and implementations
TSP/HCP Benchmark set
Latest papers
RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library
ai4co/rl4co
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To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
Policy-Based Self-Competition for Planning Problems
grimmlab/policy-based-self-competition
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AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well.
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
kaist-silab/equity-transformer
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Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53\% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1, 000 cities of mTSP.
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.
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
abureyanahmed/gnn-mcts-steiner
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Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem
pointerformer/pointerformer
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Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology.
Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning
This brings in a dynamic version of the traveling salesman problem (DTSP), which takes into account the information of real-time traffic and customer requests.
Preference-Aware Delivery Planning for Last-Mile Logistics
However, despite many decades of solid research on solving these VRP instances, we still see significant gaps between optimized routes and the routes that are actually preferred by the practitioners.
ASP: Learn a Universal Neural Solver!
LOGO-CUHKSZ/ASP
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Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy.
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
edward-sun/difusco
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NeurIPS 2023
We evaluate our methods on two well-studied NPC combinatorial optimization problems: Traveling Salesman Problem (TSP) and Maximal Independent Set (MIS).
