FangfangLi-DUT
I'm a Ph. D. candidate in Dalian University of technology, Dalian, China. My research interests are intelligent transport logistics and data-driven decision.
FangfangLi-DUT's Stars
INFORMSJoC/2022.0136
FinnSWZ/sun
Adam-R26/ReddiKnowSparse-Infer-Traits-of-Reddit-Users-from-Their-Comments
Adaptive Machine Learning Pipeline that performs binary classification between the comments of any two sub-reddits.
wanweiwei07/wrs
The WRS Robot Planning & Control System
carlson9/KocPython2021
course materials for QMBU/INTL 450/550 advanced data analysis in Python
kevin031060/CSP_Attention
jimmykimmy68/Deep-SVDD-PyTorch
A PyTorch implementation of the Deep SVDD anomaly detection method
ShadowOS/-Multi-period-Managing-Inventory-and-Cash-Distribution-in-ATMs
This is generalize model for multi-product, multi-fleet, multi-period inventory routing problem. For ease of understanding i have taken a small example of managing inventory and cash distribution in ATMs. I try to address all the possible scenario or flexibility in this model
GowthamBabu2074/Machine-Learning-Tutorials
chaitu67/Online-Retail-Segmentation-and-Classification
An online retail store data's transactional data is converted and clustered as per customers and products,futher a classification model that identifies the new data has been developed
Veganveins/LSTM
Sanjana7395/Grape-disease-classification
This project classifies diseases in grape plant using various Machine Learning classification algorithms.
danielpang/decision-trees
Python Implementation of the Machine Learning Decision Tree Algorithm for Classification problems.
ShadowOS/Vechicle-Routing-Problem-VRP-with-Pickup-and-Delivery
Pickup-and-Delivery Problems (PDPs) constitute an important family of routing problems in which goods or passengers have to be transported from different origins to different destinations. These problems are usually defined on a graph in which vertices represent origins or destinations for the different entities (or commodities) to be transported. PDPs can be classified into three main categories according to the type of demand and route structure being considered. In many-to-many (M-M) problems, each commodity may have multiple origins and destinations and any location may be the origin or destination of multiple commodities. These problems arise, for example, in the repositioning of inventory between retail stores or in the management of bicycle or car sharing systems. One-tomany- to-one (1-M-1) problems are characterized by the presence of some commodities to be delivered from a depot to many customers and of other commodities to be collected at the customers and transported back to the depot. These have applications, for example, in the distribution of beverages and the collection of empty cans and bottles. They also arise in forward and reverse logistics systems where, in addition to delivering new products, one must plan the collection of used, defective, or obsolete products. Finally, in one-to-one (1-1) problems, each commodity has a single origin and a single destination between which it must be transported. Typical applications of these problems are less than- truckload transportation and urban courier operations.
patankaraditya1/Comparison-of-Multi-item-capacitated-lot-sizing-heuristics-using-Python
The problem associated with the project was to minimize the combined inventory holding and setup costs over the planning horizon. Pandas, Numpy libraries used for comparison of Python code and research paper heuristic outcomes and made animation plots for visualization.
kodum13/Genetic-Algorithm
Python code for Genetic Algorithm based on “A Simulation-based decision support for multi-echelon inventory problem with service level constraints” by Shing Chih Tsai and Chung Hung Liu.
Bounteous-Inc/Magento-Multi-Location-Inventory
anshul-musing/multi-echelon-inventory-optimization
multi-echelon inventory optimization with SimPy, SciPy, sklearn, and RBFOpt
tong-wang/FNV-MMFE
Numerical experiments in the paper "A Multiordering Newsvendor Model with Dynamic Forecast Evolution"
BinarywoodB/GCM
GCM
TerryNick/quasar-samples
Sample notebooks to optimize different use cases
jackyjkchan/surgical_consumable_supplychain
New cleaner version of scm
longgb246/pythonstudy
a repository to study python!
umichwolf/Stochastic-Inventory-Control
brightyoun/De-Weathering-AI
xenakas/small_sample
iterater/ensemble-forecasting
Ensemble management experiments with sea level forecasting
ChileWang0228/DeepLearningTutorial
深度学习代码
tinyzqh/code-of-csdn
csdn上面的一些相关代码
UnofficialJuliaMirror/Changepoints.jl-98700a41-f20d-59c7-9e81-44d0470ae598
Last mirrored from https://github.com/STOR-i/Changepoints.jl.git on 2019-11-18T19:15:53.064-05:00 by @UnofficialJuliaMirrorBot via Travis job 481.7 , triggered by Travis cron job on branch "master"