Matching Algorithm for More Effective Faculty-Class Schedule Pairings
Disciplines
Data Science | Other Mathematics
Abstract (300 words maximum)
Assigning faculty to courses is a crucial part of academic planning, as both parts are needed to run a class. However, many factors limit the pairings which makes forming a cohesive schedule more difficult and different from semester to semester. This project aimed to use graph theory to create a scheduling solution using past schedule data to algorithmically assign faculty to courses. We built a bipartite graph with weights being assigned based on prior experiences teaching and use maximal weight matching to distribute course load evenly across faculty. This method improves scheduling efficiency and adapts dynamically, making the process more flexible and effective.
Academic department under which the project should be listed
CSM - Mathematics
Primary Investigator (PI) Name
Dr. Joseph DeMaio
Matching Algorithm for More Effective Faculty-Class Schedule Pairings
Assigning faculty to courses is a crucial part of academic planning, as both parts are needed to run a class. However, many factors limit the pairings which makes forming a cohesive schedule more difficult and different from semester to semester. This project aimed to use graph theory to create a scheduling solution using past schedule data to algorithmically assign faculty to courses. We built a bipartite graph with weights being assigned based on prior experiences teaching and use maximal weight matching to distribute course load evenly across faculty. This method improves scheduling efficiency and adapts dynamically, making the process more flexible and effective.