Attention based model for learning to solve different routing problems
Long-term forecasting of traffic flow using the lstm method
Use reinforcement learning to solve jssp problems, using ZLP data sets
A data set that measures the level of excellence of learning algorithms, automatically generated JSSP questions, including generated code and several examples
This project is used to generate problems with flowshop problems and solutions to solve problems using the minizinc modeling tool.
Same Structural JSSP Scheduling Method Based on Hybrid Deep Neural Network Method - Source code
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
use a new DNN net work to solute the job shop problem, deep learning method named HDNNM
An example implementation of triplet-loss in tensorflow using keras
Part of the greedy algorithm used to solve the 2018 Alibaba Tianchi Competition-Server Dispatch Competition. This method ranks 66 in the preliminary round and is ranked second in the semi-finals. It is a good starting method.
This code solves the scheduling problem of a large number of servers. The 68219 tasks are scheduled to 6000 servers, taking into account the constraints of the server's cpu, MEM, DISK, and app constraints. I have tried to use PYHON, MINZINC, GUROBI and other tools to solve, using the firstfit method, localsearch method and random algorithm.
This project uses the statistical data of Hebei Province from 2011 to 2017 to analyze the consumer consumption index and the service price index and the consumer price index. Using python3.6 as a development tool, the correlation coefficient is calculated on a monthly basis, and the correlation curve is drawn.
