A graph coloring constructive hyper-heuristic for examination timetabling problems

  • Authors:

  • Nasser R. Sabar;Masri Ayob;Rong Qu;Graham Kendall

  • Affiliations:

  • Data Mining and Optimisation Research Group (DMO), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia, Bangi, Malaysia 43600 UKM;Data Mining and Optimisation Research Group (DMO), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia, Bangi, Malaysia 43600 UKM;ASAP Research Group, School of Computer Science, The University of Nottingham, Nottingham, UK NG8 1BB;ASAP Research Group, School of Computer Science, The University of Nottingham, Nottingham, UK NG8 1BB and University of Nottingham Malaysia Campus, Semenyih, Malaysia 43500

  • Venue:
  • Applied Intelligence
  • Year:
  • 2012

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Abstract

In this work we investigate a new graph coloring constructive hyper-heuristic for solving examination timetabling problems. We utilize the hierarchical hybridizations of four low level graph coloring heuristics, these being largest degree, saturation degree, largest colored degree and largest enrollment. These are hybridized to produce four ordered lists. For each list, the difficulty index of scheduling the first exam is calculated by considering its order in all lists to obtain a combined evaluation of its difficulty. The most difficult exam to be scheduled is scheduled first (i.e. the one with the minimum difficulty index). To improve the effectiveness of timeslot selection, a roulette wheel selection mechanism is included in the algorithm to probabilistically select an appropriate timeslot for the chosen exam. We test our proposed approach on the most widely used un-capacitated Carter benchmarks and also on the recently introduced examination timetable dataset from the 2007 International Timetabling Competition. Compared against other methodologies, our results demonstrate that the graph coloring constructive hyper-heuristic produces good results and outperforms other approaches on some of the benchmark instances.