A constructive approach to examination timetabling based on adaptive decomposition and ordering

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches.

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

[2]  Laurent Péridy,et al.  Constraint Logic Programming for Examination Timetabling , 1996, J. Log. Program..

[3]  Philippe David A Constraint-Based Approach for Examination Timetabling Using Local Repair Techniques , 1997, PATAT.

[4]  Wolfgang Banzhaf,et al.  A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem , 2009, Eur. J. Oper. Res..

[5]  Edmund K. Burke,et al.  A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems , 1995, ICGA.

[6]  Ender Özcan,et al.  Linear Linkage Encoding in Grouping Problems: Applications on Graph Coloring and Timetabling , 2006, PATAT.

[7]  Edmund K. Burke,et al.  A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling , 2010, Int. J. Appl. Metaheuristic Comput..

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

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

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

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

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

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

[14]  Graham Kendall,et al.  Monte Carlo hyper-heuristics for examination timetabling , 2012, Ann. Oper. Res..

[15]  Alon Itai,et al.  On the Complexity of Timetable and Multicommodity Flow Problems , 1976, SIAM J. Comput..

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

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

[18]  Sanja Petrovic,et al.  Hybrid variable neighbourhood approaches to university exam timetabling , 2010, Eur. J. Oper. Res..

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

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

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

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

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

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

[25]  George M. White,et al.  Examination Timetables and Tabu Search with Longer-Term Memory , 2000, PATAT.

[26]  Barry McCollum,et al.  A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem , 2006, PPSN.

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

[28]  David Joslin,et al.  "Squeaky Wheel" Optimization , 1998, AAAI/IAAI.

[29]  Christine L. Mumford,et al.  A multiobjective framework for heavily constrained examination timetabling problems , 2010, Ann. Oper. Res..

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

[31]  KorkmazEmin Erkan,et al.  A comprehensive analysis of hyper-heuristics , 2008 .

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

[33]  Andrzej Bargiela,et al.  Construction of examination timetables based on ordering heuristics , 2009, 2009 24th International Symposium on Computer and Information Sciences.

[34]  Edmund K. Burke,et al.  Parallel Problem Solving from Nature - PPSN IX: 9th International Conference, Reykjavik, Iceland, September 9-13, 2006, Proceedings , 2006, PPSN.

[35]  Sanja Petrovic,et al.  A Multiobjective Optimisation Technique for Exam Timetabling Based on Trajectories , 2002, PATAT.

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

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

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

[39]  Ender Özcan,et al.  A comprehensive analysis of hyper-heuristics , 2008, Intell. Data Anal..

[40]  Edmund K. Burke,et al.  Examination timetabling using late acceptance hyper-heuristics , 2009, 2009 IEEE Congress on Evolutionary Computation.

[41]  Nagraj Balakrishnan,et al.  Scheduling examinations to reduce second-order conflicts , 1992, Comput. Oper. Res..

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

[43]  Hishammuddin Asmuni,et al.  An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables , 2009, Comput. Oper. Res..

[44]  Ersan Ersoy,et al.  Memetic Algorithms and Hyperhill-climbers , 2008 .

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

[46]  Giuseppe F. Italiano,et al.  Novel Local-Search-Based Approaches to University Examination Timetabling , 2008, INFORMS J. Comput..

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

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

[49]  Edmund K. Burke,et al.  The practice and theory of automated timetabling , 2014, Annals of Operations Research.

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

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

[52]  Julie Stanik-Hutt,et al.  Be a Squeaky Wheel , 2008 .

[53]  Edmund K. Burke,et al.  Linear combinations of heuristics for examination timetabling , 2011, Annals of Operations Research.

[54]  Ender Özcan,et al.  Final exam scheduler - FES , 2005, 2005 IEEE Congress on Evolutionary Computation.