mamunur-ipe

Pinned Repositories

Algorithms_for_solving_VRP
realize the common algorithms for sovling VRP
Language:Python00
CMAT
The CMAT software provides the user with a fully customizable decision support framework that analyzes the optimal supply chain configurations for a given industrial electronic waste (e-waste) recycling and refurbishment process. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the model is to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.
Language:Python00
CNN_rice_leaf
Language:Python0 2 00
Cplex
Language:AMPL00
HACO
Language:Python0 1 00
ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Language:Jupyter Notebook00
localsearchtsp
Python implementation of different algorithms for solving basic TSP.
Language:Python00
ML-model-deployment
Language:HTML0 2 00
MonteCarloSimulation
Language:Python00
movie_recommendation_engine
Language:Python00

mamunur-ipe's Repositories

mamunur-ipe/Algorithms_for_solving_VRP
realize the common algorithms for sovling VRP
Language:Python00
mamunur-ipe/CMAT
The CMAT software provides the user with a fully customizable decision support framework that analyzes the optimal supply chain configurations for a given industrial electronic waste (e-waste) recycling and refurbishment process. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the model is to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.
Language:Python00
mamunur-ipe/CNN_rice_leaf
Language:Python0 2 00
mamunur-ipe/Cplex
Language:AMPL00
mamunur-ipe/HACO
Language:Python0 1 00
mamunur-ipe/ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Language:Jupyter Notebook00
mamunur-ipe/localsearchtsp
Python implementation of different algorithms for solving basic TSP.
Language:Python00
mamunur-ipe/ML-model-deployment
Language:HTML0 2 00
mamunur-ipe/MonteCarloSimulation
Language:Python00
mamunur-ipe/movie_recommendation_engine
Language:Python00
mamunur-ipe/or-tools
Google's Operations Research tools:
mamunur-ipe/plots
Language:Python
mamunur-ipe/PyEmission
PyEmission is a Python library for estimation of vehicular emissions and fuel consumption. This tool covers a wide range of light duty motor vehicles including passenger car, SUV, passenger truck, and light commercial truck. The tool only takes second-by-second driving cycle and vehicle characteristics data as inputs and generate results of vehicular emissions (CO2, CO, NOx, and HC) and fuel consumption.
Language:Python
mamunur-ipe/scikit-feature
open-source feature selection repository in python
mamunur-ipe/SQL
Language:Python
mamunur-ipe/vidstream
Streaming video data via networks
mamunur-ipe/vrpy
A python framework for solving the VRP and its variants with column generation.
mamunur-ipe/wart-treatment
Language:Python
mamunur-ipe/web_scrapping
Language:Jupyter Notebook