Optimal minimal-perturbation university timetabling with faculty preferences

TL;DR

This paper introduces an ILP-based framework for university timetabling that minimizes course swaps and maximizes faculty preferences, addressing last-minute scheduling changes efficiently.

Contribution

It presents a novel ILP model that balances minimizing course swaps with maximizing faculty preferences in university scheduling.

Findings

The ILP model effectively reduces course swaps in simulations.

Faculty preferences are significantly improved using the proposed approach.

The framework adapts well to last-minute timetable adjustments.

Abstract

In the university timetabling problem, sometimes additions or cancellations of course sections occur shortly before the beginning of the academic term, necessitating last-minute teaching staffing changes. We present a decision-making framework that both minimizes the number of course swaps, which are inconvenient to faculty members, and maximizes faculty members' preferences for times they wish to teach. The model is formulated as an integer linear program (ILP). Numerical simulations for a hypothetical mid-sized academic department are presented.