Traveling Salesman Problem
67 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Traveling Salesman Problem
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Traveling Salesman Problem models and implementations
TSP/HCP Benchmark set
Latest papers with no code
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem
We make a comparison of our method and baselines, including classic OR algorithms and existing learning methods.
Proposed modified computational model for the amoeba-inspired combinatorial optimization machine
A single-celled amoeba can solve the traveling salesman problem through its shape-changing dynamics.
A Rolling Horizon Restoration Framework for Post-disaster Restoration of Electrical Distribution Networks
Severe weather events such as floods, hurricanes, earthquakes, and large wind or ice storms can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort.
Orchestrating UAVs for Prioritized Data Harvesting: A Cross-Layer Optimization Perspective
This work describes the orchestration of a fleet of rotary-wing Unmanned Aerial Vehicles (UAVs) for harvesting prioritized traffic from random distributions of heterogeneous users with Multiple Input Multiple Output (MIMO) capabilities.
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning
In this paper, we propose the deep reinforcement learning model called the two-stage graph pointer network (GPN) for solving QAP.
Less Is More - On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP
Furthermore, we propose ensembles of different sparsification levels allowing models to focus on the most promising parts while also allowing information flow between all nodes of a TSP instance.
Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers
Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years.
Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering
At the first step, a constrained centroid-based clustering algorithm generates feasible clusters of customers.
Learning-guided iterated local search for the minmax multiple traveling salesman problem
The minmax multiple traveling salesman problem involves minimizing the longest tour among a set of tours.
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions.
