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Evolutionary multi-objective optimization platform
LucasBoTang / Multi-Task_Predict-then-Optimize
Forked from khalil-research/Multi-Task_Predict-then-OptimizeMulti-task end-to-end predict-then-optimize
Learning-to-Optimize for Mixed-Integer Non-Linear Programming
Transit Network Design Instances for Research
Accessibility indicators for public transport network analysis
Optimisation model for determining optimal location of electric vehicle charging stations as to maximise electric vehicle adoption
Implementation of greedy, branch-and-cut, unaccelerated and accelerated branch-and-Benders-cut, and accelerated branch-and-Benders-cut for the dynamic maximum covering location problem (MCLP).
Collection of open data resources for traffic information
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
scikit-mobility: mobility analysis in Python
A networkx backend that uses joblib to run graph algorithms in parallel.
Gallery of OSMnx tutorials, usage examples, and feature demonstations.
This is the official code for the published paper 'Solve routing problems with a residual edge-graph attention neural network'
"Attention, Learn to Solve Routing Problems!"[Kool+, 2019], Capacitated Vehicle Routing Problem solver
This simple script computes the traffic assignment using the Frank-Wolfe algorithm (FW) or the Method of successive averages (MSA). It can compute the User Equilibrium (UE) assignment or the System…
This is the repository for the collection of Graph Neural Network for Traffic Forecasting.
Collected study materials in Numerical Optimization ANU@MATH3514(HPC)
Appendix repository for Medium article "Routing Traveling Salesmen on Random Graphs using Reinforcement Learning, in PyTorch"
Deep Reinforcement Learning Based on Graph Neural Networks for Job-shop Scheduling
Attention based model for learning to solve different routing problems
Python Implementation of Reinforcement Learning: An Introduction
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Bayesian methods and hierarchical models