UNIVERSITY LECTURER TIMETABLING: A COMPREHENSIVE LITERATURE REVIEW OF CHALLENGES, SOLUTIONS, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.35631/JISTM.1144050Keywords:
Course Scheduling, Lecturer Preferences, Literature Review, Optimisation, Timetable Fairness, University Lecturer TimetablingAbstract
Timetable preparation for university lecturers is a complex activity that has implications for the delivery of academic programs, use of resources, lecturers’ working patterns, and stakeholder satisfaction. The aim of this review is to identify and synthesize the main challenges, solution approaches, research gaps, and future directions for scheduling university lecturers. A structured literature review was performed using the Scopus-indexed publications for the period 2009-2025. Nineteen relevant studies were identified by predefined screening and eligibility criteria and analysed by descriptive classification and thematic synthesis. The results show that the lecturer timetabling depends on the computational complexity, the necessity to satisfy hard and soft constraints, the availability of rooms and teaching periods, interdepartmental coordination, lecturer availability, and scheduling policies that are specific to each institution. The existing methodologies include mathematical programming, graph-colouring techniques, heuristics, genetic algorithms, hybrid metaheuristics, fuzzy constraint models, automated systems, and adjustable timetabling frameworks. These advances in timetable feasibility and computational performance are however still limited in terms of scalability, institutional adaptability, practical implementation, preference handling, and the limited evaluation of workload balance and timetable fairness. Recent research indicates a gradual shift from feasibility-centred optimisation to more flexible, preference-aware, and stakeholder-responsive systems. Integrating computational, institutional, and lecturer-centred perspectives, this review demonstrates that future timetabling systems should balance operational efficiency with adaptability, transparency, and fair scheduling outcomes.
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