Developing an algorithm-based course scheduling tool

Sunset Learning Institute (SLI), a Cisco training firm based in Reston, Virginia, needs a better course scheduling system to increase profits through reducing the time and money spent on canceling and rescheduling classes. Historically, SLIpsilas staff has spent hundreds of man hours scheduling courses, only to have less than 30 percent of scheduled courses run. To achieve the goal of improving both the scheduling process and the proposed course schedule, a new Web-based tool accesses a database of historical course information and runs a scheduling algorithm to predict SLIpsilas course schedule faster and more accurately than current scheduling methods. The scheduling algorithm is the heart of this project. This algorithm pulls course data from a continually expanding database of historical information. The relevant historical data includes statistics such as the number of times a course has run in the past, the number of students in those classes, the coursepsilas profitability, and indicators concerning whether the course is a part of one or more academic sequences that SLI offers. With this data, the algorithm generates a 90 day schedule which is then run through a logic based constraint identification system. Based on current analysis, this tool improves the scheduling process for Sunset Learning Institute. It makes course scheduling faster by automating a process that had previously been completed manually. In addition, the tool allows SLI to increase the percentage of scheduled courses that run.