Stars
Codes and datasets of the ARA competition @ AAMAS 2025
Transportation Networks for Research
以Sioux_Falls网络为研究对象，对基础的最短路算法的学习，包括Dijkstra算法、Bellman-Ford算法、Floyd算法、A*算法。
分别使用Gurobi对VRP、CVRP、CVRPTW、CVRPPDTW四类问题进行了建模和求解，所用数据集主要为Solomon（R-101）和东南大学九龙湖校区部分路网。
This dataset comprises sets of instances used in the related paper, containing space-time-state network data for 170 instances.
2015年Murray关于卡车无人机协同配送问题的复现代码，包括建模和文中的启发式求解，全网首发。
（1）调用Gurobi加入MTZ破子圈约束求解TSP，（2）使用蚁群算法求解TSP商旅问题的最短访问路线；其中使用2-邻边算法进行局部调整，可视化展示求解结果，附带实验报告说明。案例包括“五角星小型案例”, “100结点的实际结点案例”。
LLM4AD: A Platform for Algorithm Design with Large Language Model
Evolutionary multi-objective optimization platform
EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Re…
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
OptiML's contribution to the EURO meets NeurIPS 2022 vehicle routing competition.
A repository with instances for the TSP with Drones
Code for the Travelling Salesman Problem with Drone
A package that uses Hybrid Genetic Algorithm to solve any TSPD or FSTSP instance
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Quickstart for EURO Meets NeurIPS 2022 Vehicle Routing Competition
[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940
Attention based model for learning to solve different routing problems
yimengmin / eco-dqn
Forked from tomdbar/eco-dqnImplementation of ECO-DQN as reported in "Exploratory Combinatorial Optimization with Reinforcement Learning".