• Home
  • Project
    Introduction
    • Introduction
    • Background and Motivation
    • What is a Good Timetable?
    • Project Aims and Scope
  • Graph Data
    Model
    • Graph vs Relational Data Models
    • Graph Data Model for Timetabling
    • Early Insights
    • Model Expansion
    • Graphing Time
  • Data
    Pipeline
    • ETL Overview
    • Approach
    • Configuration and Logging
    • Extract
    • Transform
    • Google Drive Load
    • Neo4j Load
    • Reflection
  • Timetable
    Metrics
    • Timetable Metrics
    • Metric Aggregations
    • Implementing Metrics
    • TQI Summary
  • Final
    Thoughts
  • Appendices
    & Extras
    • Appendix Table of Contents
    • References
    • Acknowledgements
  • Word
  1. Project Introduction
  2. Introduction
  • Home
  • Project Introduction
    • Introduction
    • Background and Motivation
    • What is a Good Timetable?
    • Project Aims and Scope
  • Graph Data Model
    • Graph vs Relational Data Models
    • Graph Data Model for Timetabling
    • Early Insights
    • Model Expansion
    • Graphing Time
  • Data Pipeline
    • ETL Overview
    • Approach
    • Configuration and Logging
    • Extract
    • Transform
    • Google Drive Load
    • Neo4j Load
    • Reflection
  • Timetable Metrics
    • Timetable Metrics
    • Metric Aggregations
    • Implementing Metrics
    • TQI Summary
  • Final Thoughts
  • Appendices
    • Random Graph Generator
    • Technology Stack
    • Configuration
    • Anonymisation
    • ETL Summary and Code
      • ETL Summary
      • ETL Code
      • Config and Misc
      • Extract-SQL
      • Extract
      • Google Drive Load
      • Transform
      • Neo4j Load
    • Neo4j & Cypher Code
      • Cypher Queries
      • Creating Nodes and Relationships
      • Deleting Nodes and Relationships
      • General Queries
      • Count Queries
      • Hard (timetabling) Constraints
      • Student Clashes
      • Soft Constraints
      • Rooms and Spaces
      • Perspectives
      • Blue Skies Opportunities
  • Supervision
    • Supervision
    • Notes Example 1
    • Notes Example 2
    • Notes Example 3
  • References
  • Acknowledgements
  1. Project Introduction
  2. Introduction

Introduction

University timetabling is the process of scheduling resources within the constraints of an academic institution and calendar. At its core, it involves collecting and combining time slots, rooms, students, and other resources while satisfying a multitude of constraints and preferences to achieve a viable outcome.

However, the reality of timetabling is far more complicated than this simple definition suggests.

Timetablers must juggle numerous hard constraints (e.g., room capacities, pre-assigned times) and soft constraints (e.g., staff preferences, student travel times) to reach a workable solution. The scale of this task, combined with interdependencies between scheduling decisions, makes university timetabling one of the most challenging administrative tasks in higher education (de Werra, 1997).

Timetables can make or break a university - they shape the daily experiences of students and staff, influence resource utilisation, and play a significant role in institutional efficiency. The complexity of timetabling stems from various factors:

  • Scale: Tens of thousands of students and activities, and limited resources create a logistical nightmare.
  • Constraints: Juggling hard limits (room capacities) and soft preferences (College desires) is a constant balancing act.
  • Interdependencies: Changes in one part of the schedule can have cascading effects throughout the entire timetable.
  • Diversity of Needs: Different organisational units (colleges, faculties, schools, departments) have varying requirements and preferences.
  • Optimisation Goals: Timetablers must balance efficiency, fairness, and quality of education.

While traditional studies on “timetabling” focus heavily on generating or optimising feasible timetables (Bonutti et al., 2012; Ceschia, Di Gaspero and Schaerf, 2023; Rudová, Müller and Murray, 2011) – ensuring no clashes or rule violations – this project explores a different facet: how analysing scheduled timetables can lead to deeper insights and ultimately, improved quality for all stakeholders.

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Background and Motivation

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