A tool for the Kellogg Board Fellows program to optimally match students to board fellowships based on their ranked preferences (using the Hungarian algorithm). Fully client-side, using web workers API in-browser to perform async computation.
This is a tool for the Kellogg Board Fellows program to optimally match students to board fellowships based on their ranked preferences (using the Hungarian algorithm). Fully client-side in the browser, using web workers API to perform computation asynchronously.
Using:
Currently (Januaary 16, 2023) hosted here: https://kbfoptimizer.apps.kellogg.northwestern.edu/
It is a bit wonky in terms of the process, but it’s essentially:
I separated out the Qualtrics parsing function from the optimizer function in case the Qualtrics format ever changes in a way that would break the whole process. If Qualtrics changes, then worst case scenario you could still manually extract the results and make a new CSV spreadsheet following the form indicated in the Optimizer help:
If you copy the fake data it generates out of this text area, you can paste it into an Excel spreadsheet, do the Data > Text to Columns function with commas selected as the delimiter, and you’ll get an idea of the matrix form. Modify with the real company names, students, and rankings, and you’d be able to paste this back into the optimizer for calculation.
npm install
npm run serve
npm run build
npm run lint
Until the docker image is avaiabile on dockerhub, the image is created locally.
Clone the repo and cd into the root directory. Run
docker build --tag kbfoptimizer .
To run / deploy run
docker compose up