Analyzing the effectiveness of solution for matching project

 

We used the NetworkX to successfully build the model and obtain the final matching. Therefore, the only thing left it to analyze the effectiveness of our solution. As we mentioned in the last post, NetworkX is used to minimize the outcome. In our case, we used it to minimize the “negative” outcome. The final result we obtained, the total satisfaction degree between all advisors and advisees, is 9958.5; and the total time to run the solution is approximately 14.5 seconds.

[Read more…]

Building the Model Using NetworkX

After we obtained the pij matrix, we had the satisfaction level between each advisor and each student. Therefore, the next step is to build the proper model and match them.

[Read more…]

Update of the Matching Model–Transforming Data

We approach this project in three steps: 1. Transforming Data; 2. Building the Model; 3. Running the solution. The first step is transforming data we got from OAA. There are two initial resources from OAA: [Read more…]

A Mathematical Model of Maximizing Matching Rate Between Students and Advisors

There are so many exciting things in college life, and matching with a “like-minded” pre-major advisor is definitely one of the most important things! For incoming students, pre-major advisors will be the first to provide them with unique perspectives about College of William and Mary; in addition, their academic experience will certainly influence students’ major decisions in future. We believe that every student wants to match with a pre-major advisor who shares similar interests, but sometimes not everyone can get matched to desired advisors: I paired with the Professor in English Literature department though I am more interested in math. Therefore, maximizing the satisfaction degree between both students and advisors becomes the intention of this project.

[Read more…]