Pinned Repositories
aimms
AIMMS exe
betterOptionsTrading
This repository has 2 userscripts. (1) To add features to kite.zerodha.com (2) To add features to pro.upstox.com
breakeven_v1
Design-and-Analysis-of-Experiments
Lecture Notes on Design & Analysis of Experiments
EvoloPy
Facility-Location-Set-Covering-MIP-model
-Developed a supply chain network baseline MIP model for a glass manufacuterer with multiple products, manufacuting facilites, and production costs (Regular/Overtime) to find optimal product flow as per sourcing policies and capacity constraints. -To improve the service levels, developed a multi-objective MIP scenario model which finds the minmum number of warehouses to be built such that 80% of the demand is covered with in 500 miles of the nearest source. -Scenario model suggested to build 5 warehouses with their exact location and product flow information and was able to achieve reduction in transporatation cost by 19.75% with 80% demand served within 500 miles compared to 11% of demand within 500 miles in baseline model. -Coded in Python and performed optimization using Gurobi: pandas, dictionaries, loops, gurobi packages, csv package.
price-of-chair
Pricing service which allows for setting up alerts for prices in online stores and then receive e-mails when prices go under a certain price.
PSO
Particle Swarm Optimization
pwc
Papers with code. Sorted by stars. Updated weekly.
pytudes
Python programs to practice or demonstrate skills.
modiyamrugank's Repositories
modiyamrugank/aimms
AIMMS exe
modiyamrugank/betterOptionsTrading
This repository has 2 userscripts. (1) To add features to kite.zerodha.com (2) To add features to pro.upstox.com
modiyamrugank/breakeven_v1
modiyamrugank/Design-and-Analysis-of-Experiments
Lecture Notes on Design & Analysis of Experiments
modiyamrugank/EvoloPy
modiyamrugank/Facility-Location-Set-Covering-MIP-model
-Developed a supply chain network baseline MIP model for a glass manufacuterer with multiple products, manufacuting facilites, and production costs (Regular/Overtime) to find optimal product flow as per sourcing policies and capacity constraints. -To improve the service levels, developed a multi-objective MIP scenario model which finds the minmum number of warehouses to be built such that 80% of the demand is covered with in 500 miles of the nearest source. -Scenario model suggested to build 5 warehouses with their exact location and product flow information and was able to achieve reduction in transporatation cost by 19.75% with 80% demand served within 500 miles compared to 11% of demand within 500 miles in baseline model. -Coded in Python and performed optimization using Gurobi: pandas, dictionaries, loops, gurobi packages, csv package.
modiyamrugank/price-of-chair
Pricing service which allows for setting up alerts for prices in online stores and then receive e-mails when prices go under a certain price.
modiyamrugank/PSO
Particle Swarm Optimization
modiyamrugank/pwc
Papers with code. Sorted by stars. Updated weekly.
modiyamrugank/pytudes
Python programs to practice or demonstrate skills.
modiyamrugank/ST-matching
A Python implementation of the ST map-matching algorithm
modiyamrugank/stock-analyser
modiyamrugank/StockTrade
ChartingProject
modiyamrugank/SwarmPackagePy
Library of swarm optimization algorithms.