daunfamily's repositories
VRPLite
A dynamic programming implementation for VRPPDTW based on state–space–time network representations
neural-combinatorial-rl-pytorch
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940
dynamic-seq2seq
seq2seq中文聊天机器人
VRPTW-senario
Vehicle Route Problem with Time Windows Optimization
Optimizing-Taxi-Cab-Dispatch
This project solves one of the variants of Vehicle Routing Problem (NP-hard) which is Cab Dispatch Problem using Julia. It is modelled as an optimization problem.
Forecasting-Solar-PV-Generation
Forecasting Solar PV Generation
VRPTW_CPP
Solving Vehicle Routing Problem with Time Windows using Hillclimbing, Genetic and Simulated Annealing algorithms + Push Foward Insert Heuristic
VRP2E
A coevolutionary-algorithm solver for multi-objective two-echelon Vehicle Routing Problems.
colaboratory
[deprecated] Jupyter CoLaboratory, goto google colab now
vrpwrp
VRPWRP (Vision-algorithms Requests Processing Wrappers), a pip package for running deep-learning Computer Vision algorithms from the cloud.
cplex-vrptw-implementation
An implementation of VRPTW in CPLEX
BiObjectiveBranchAndCut
Contains code for a bi objective branch and cut algorithm which can be used for bi objective combinatorial optimization in minimization form.
seq2seq
基于Pytorch的中文聊天机器人 集成BeamSearch算法
CuttingPlane-for-TSP
巡回セールスマンに対する切除平面法
findingLegs
Program used to generate routes for a branch-and-price algorithm to solve a drone routing problem with GENCOL.
maritime-vrp
Branch-and-price solver for a maritime VRP problem
localsolver_vrp
Solving VRP problems using LocalSolver (LSP models)
CVRP-genetic
The capacitated vehicle routing problem is a well-studied combinatorial computing problem. There are many ways to solve the problem like evolutionary computing, reinforcement learning and exact methods. A genetic algorithm was implemented to solve a given CVRP instance with 250 customers. An evolutionary computing approach was chosen because of the following reasons: 1. The exact methods like branch and cut explore the entire search space which is most of the time unfeasible given the computation time. A genetic algorithm can be fine tuned to explore the search space in a specific way. 2. In this case, the constraints are very simple; maximum vehicle capacity of 500 and a vehicle can visit a customer only once. A real world CVRP can have multiple objectives and constraints like traffic conditions, number of turns, multiple depots etc. A genetic algorithm can adapt to these constraints without the need to change the entire programmatic structure by simply formulating the fitness function again.
Multi-Objective-Genetic-Algorithms-VRPTW
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Cutting_stock_problem
Solving Cutting Stock Problem Using CPLEX
Multiple-Vehicle-Routing-visualization
Simple MVR simulation
knapsack
Python implementation of branch and cut algorithm
branch-and-cut
Branch-and-cut for the TSP
VRP-CG-DP
In this project we focus on the set covering based formulation for the capacitated vehicle routing problem (CVRP). A column generation approach based on dynamic programming has been used to find a lower bound to the optimal solution.
Open-VRP
Open-source framework for modeling Vehicle Routing Problems.
VRPTW-3
Vehicle Routing Problem with Time Windows (Almost done...到頭來好像還是贏不過學長orz)