Hyper-heuristics and fairness in examination timetabling problems

Muklason, Ahmad (2017) Hyper-heuristics and fairness in examination timetabling problems. PhD thesis, University of Nottingham.

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Examination timetabling is a challenging optimisation problem in operations research and artificial intelligence. The main aim is to spread exams evenly throughout the overall time period to facilitate student comfort and success; however, existing examination timetabling solvers neglect fairness by optimising the sum or average of the objective function value without considering its distribution among students or other stakeholders. The balance between quality of the overall timetable and fairness (global fairness and within a cohort) is a major concern, thus the latter is added as a new objective function and quality indicator of examination timetables. The objective function is also considered from the perspectives of multiple stakeholders of examination timetabling (i.e. students, invigilators, markers and estates), as opposed to viewing the objective function as an aggregate function. These notions make the problem become a multi-objective optimisation problem.

We study sum of power rather than linear summation to enforce fairness and concurrently minimise the objective function, using some perturbation-based hyper- heuristics approaches to optimise the standard objective function. Secondly, multi-stage approach is studied (generating initial feasible solution, improving the standard quality of solution and then improving fairness), to improve the fairness objective function. Given that the standard objective function and fairness objective function conflict, we then studied several multi-objective algorithms employed within the framework of hyper-heuristics. The proposed hyper-heuristic algorithms mainly can be divided into two approaches: classical scalarisation technique-based weighted sum and Tchebyce↵; and population-based non-dominated sorting memetic algorithm II (NSMA-II) and artificial bee colony and strength pareto evolutionary 2 (SPEA2) hybrid (ABC-SPEA2).

The experiments were conducted over two multi-objective examination timetabling problem formulations (i.e. with fairness and with multiple stakeholder perspectives), tested over problem instances from four different datasets: Carter, Nottingham, Yeditepe and ITC 2007. The experimental results over multi-objective examination timetabling problem with fairness showed that in terms of the standard objective function the proposed approach could produce results comparable with the best known solutions reported in the literature, whilst in the same time could be forced to be fairer that does or does not compensate on worsening the standard objective function. Fairness within a cohort could be improved much better than global fairness and treating as multi-objective problem could help the search for near-optimal standard objective function escape from local optima trap. The scalarisation technique based hyper-heuristics outperforms the population-based hyper-heuristic.

The advantage of treating examination timetabling problem as multi-objective problem is that approximations of the Pareto optimal solutions give the optimal trade-o↵ between standard objective function, fairness among all students, and fairness within a cohort. In addition, the decision maker also can view the solution from multiple stakeholders view. We believe that by giving this more detailed information, the decision maker of examination timetable could make better decisions.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Parkes, Andrew
Ozcan, Ender
Keywords: hyper-heuristic, exam timetabling, fairness, automated timetabling
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 42912
Depositing User: Muklason, Ahmad
Date Deposited: 25 Aug 2017 14:17
Last Modified: 08 Sep 2017 14:29
URI: http://eprints.nottingham.ac.uk/id/eprint/42912

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