Hybrid variable neighbourhood approaches to university exam timetabling

In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects appropriate neighbourhoods to use within the biased VNS, produces the best known results in the literature, in terms of solution quality, on some of the benchmark instances. However, it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed.

[1]  Moshe Dror,et al.  A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem , 2007, J. Oper. Res. Soc..

[2]  H. Terashima-Marín,et al.  Evolution of Constraint Satisfaction strategies in examination timetabling , 1999 .

[3]  Edmund K. Burke,et al.  A multistage evolutionary algorithm for the timetable problem , 1999, IEEE Trans. Evol. Comput..

[4]  Edmund K. Burke,et al.  A new model for automated examination timetabling , 2012, Ann. Oper. Res..

[5]  Raymond S. K. Kwan,et al.  Distributed Choice Function Hyper-heuristics for Timetabling and Scheduling , 2004, PATAT.

[6]  Edmund K. Burke,et al.  Examination Timetabling in British Universities: A Survey , 1995, PATAT.

[7]  Edmund K. Burke,et al.  Automated University Timetabling: The State of the Art , 1997, Comput. J..

[8]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling IV , 2002, Lecture Notes in Computer Science.

[9]  Luca Di Gaspero,et al.  Measurability and Reproducibility in Timetabling Research: State-of-the-Art and Discussion , 2006 .

[10]  L vanRijswijk.  Bridging the gap between research and practice. , 2004 .

[11]  Edmund K. Burke,et al.  Solving Exam Timetabling Problems with the Flex-Deluge Algorithm , 2006 .

[12]  Michael Eley,et al.  Ant Algorithms for the Exam Timetabling Problem , 2006, PATAT.

[13]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[14]  Andrea Schaerf,et al.  A Survey of Automated Timetabling , 1999, Artificial Intelligence Review.

[15]  Sanja Petrovic,et al.  A Tabu Search Approach for Graph- Structured Case Retrieval , 2002 .

[16]  Adli Mustafa,et al.  Artificial Immune Algorithms for University Timetabling , 2006 .

[17]  Edmund K. Burke,et al.  A survey of search methodologies and automated system development for examination timetabling , 2009, J. Sched..

[18]  Peter Ross,et al.  Some Observations about GA-Based Exam Timetabling , 1997, PATAT.

[19]  Graham Kendall,et al.  An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[20]  Graham Kendall,et al.  A honey-bee mating optimization algorithm for educational timetabling problems , 2012, Eur. J. Oper. Res..

[21]  Graham Kendall,et al.  An Investigation of a Tabu-Search-Based Hyper-Heuristic for Examination Timetabling , 2005 .

[22]  Edmund K. Burke,et al.  Solving Examination Timetabling Problems through Adaption of Heuristic Orderings , 2004, Ann. Oper. Res..

[23]  Ender Özcan,et al.  An Experimental Study on Hyper-heuristics and Exam Timetabling , 2006, PATAT.

[24]  Edmund K. Burke,et al.  Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems , 2009, J. Oper. Res. Soc..

[25]  Gilbert Laporte,et al.  Recent Developments in Practical Examination Timetabling , 1995, PATAT.

[26]  George M. White,et al.  Using tabu search with longer-term memory and relaxation to create examination timetables , 2004, Eur. J. Oper. Res..

[27]  Sanja Petrovic,et al.  University Timetabling , 2004, Handbook of Scheduling.

[28]  Sanja Petrovic,et al.  A Novel Similarity Measure for Heuristic Selection in Examination Timetabling , 2004, PATAT.

[29]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[30]  Graham Kendall,et al.  An investigation of a tabu assisted hyper-heuristic genetic algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[32]  Edmund K. Burke,et al.  Analyzing the landscape of a graph based hyper-heuristic for timetabling problems , 2009, GECCO.

[33]  Ben Paechter,et al.  Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition , 2010, INFORMS J. Comput..

[34]  Hishammuddin Asmuni,et al.  A Novel Fuzzy Approach to Evaluate the Quality of Examination Timetabling , 2006, PATAT.

[35]  Graham Kendall,et al.  Guided Operators for a Hyper-Heuristic Genetic Algorithm , 2003, Australian Conference on Artificial Intelligence.

[36]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[37]  Donald McIntyre,et al.  Bridging the gap between research and practice , 2005 .

[38]  Luca Di Gaspero,et al.  Tabu Search Techniques for Examination Timetabling , 2000, PATAT.

[39]  Sanja Petrovic,et al.  A time-predefined local search approach to exam timetabling problems , 2004 .

[40]  Edmund K. Burke,et al.  Hybridizing Integer Programming Models with an Adaptive Decomposition Approach for Exam Timetabling Problems , 2009 .

[41]  Alan J. Hu,et al.  Boosting Verification by Automatic Tuning of Decision Procedures , 2007 .

[42]  Gilbert Laporte,et al.  Examination Timetabling: Algorithmic Strategies and Applications , 1994 .

[43]  Edmund K. Burke,et al.  Enhancing Timetable Solutions with Local Search Methods , 2002, PATAT.

[44]  Jonathan M. Thompson,et al.  GRASPing the Examination Scheduling Problem , 2002, PATAT.

[45]  Graham Kendall,et al.  Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques , 2013 .

[46]  James B. Orlin,et al.  Very Large-Scale Neighborhood Search Techniques in Timetabling Problems , 2006, PATAT.

[47]  Michael W. Carter,et al.  OR Practice - A Survey of Practical Applications of Examination Timetabling Algorithms , 1986, Oper. Res..

[48]  Peter J. Stuckey,et al.  A Hybrid Algorithm for the Examination Timetabling Problem , 2002, PATAT.

[49]  Jonathan L. Gross,et al.  Handbook of graph theory , 2007, Discrete mathematics and its applications.

[50]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[51]  Edmund K. Burke,et al.  Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems , 2009, Eur. J. Oper. Res..

[52]  Joseph Y.-T. Leung,et al.  Handbook of Scheduling: Algorithms, Models, and Performance Analysis , 2004 .

[53]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

[54]  Edmund K. Burke,et al.  Adaptive Decomposition and Construction for Examination Timetabling Problems , 2007 .

[55]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[56]  Pierre Hansen,et al.  Variable Neighbourhood Search , 2003 .

[57]  Edmund K. Burke,et al.  Applications to timetabling , 2004 .

[58]  Giuseppe F. Italiano,et al.  New Algorithms for Examination Timetabling , 2000, WAE.

[59]  Graham Kendall,et al.  An adaptive Length chromosome Hyper-Heuristic Genetic Algorithm for a Trainer Scheduling Problem , 2002, SEAL.

[60]  Sanja Petrovic,et al.  A Multicriteria Approach to Examination Timetabling , 2000, PATAT.

[61]  J. Potvin,et al.  Tabu Search , 2018, Handbook of Metaheuristics.

[62]  Moshe Dror,et al.  Investigating Ahuja–Orlin’s large neighbourhood search approach for examination timetabling , 2007, OR Spectr..

[63]  Edmund K. Burke,et al.  An Extended Great Deluge Approach to the Examination Timetabling Problem , 2009 .

[64]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling III , 2001, Lecture Notes in Computer Science.

[65]  Peter Rossmanith,et al.  Simulated Annealing , 2008, Taschenbuch der Algorithmen.

[66]  Sanja Petrovic,et al.  Recent research directions in automated timetabling , 2002, Eur. J. Oper. Res..

[67]  Kathryn A. Dowsland,et al.  Variants of simulated annealing for the examination timetabling problem , 1996, Ann. Oper. Res..

[68]  Edmund K. Burke,et al.  A Memetic Algorithm for University Exam Timetabling , 1995, PATAT.

[69]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[70]  Peter Ross,et al.  Hyper-heuristics applied to class and exam timetabling problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[71]  E. Burke,et al.  A Multi-stage Evolutionary Algorithm for the Timetable Problem General Cooling Schedules for a Simulated Annealing Based Timetabling System. References a Multi-stage Evolutionary Algorithm for the Timetable Problem , 1998 .

[72]  Edmund K. Burke,et al.  Selected papers from the First International Conference on Practice and Theory of Automated Timetabling , 1995 .

[73]  Edmund K. Burke,et al.  PATAT 2006: Proceedings of the 6th International Conference onthe Practice and Theory of Automated Timetabling , 2006 .

[74]  Kathryn A. Dowsland,et al.  A robust simulated annealing based examination timetabling system , 1998, Comput. Oper. Res..

[75]  Luís Paquete,et al.  Empirical Analysis of Tabu Search for the Lexicographic Optimization of the Examination Timetabling Problem , 2002 .

[76]  Sanja Petrovic,et al.  A graph-based hyper-heuristic for educational timetabling problems , 2007, Eur. J. Oper. Res..

[77]  Ender Özcan,et al.  Hill Climbers and Mutational Heuristics in Hyperheuristics , 2006, PPSN.

[78]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[79]  Sanja Petrovic,et al.  Case-based heuristic selection for timetabling problems , 2006, J. Sched..