Case-based heuristic selection for timetabling problems

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems.

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

[2]  Edmund Ph. D. Burke,et al.  Practice and Theory of Automated Timetabling III: Third International Conference, PATAT 2000 Konstanz, Germany, August 16-18, 2000 Selected Papers , 2001 .

[3]  George L. Nemhauser,et al.  Sports Scheduling , 2004, Handbook of Scheduling.

[4]  Peter Ross,et al.  Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder , 1998, Evolutionary Computation.

[5]  Mahmood Amintoosi,et al.  Feature Selection in a Fuzzy Student Sectioning Algorithm , 2004, PATAT.

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

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

[8]  Ramón López de Mántaras,et al.  Case-Based Reasoning: An Overview , 1997, AI Commun..

[9]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

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

[11]  Kathryn A. Dowsland.  Review of Practice and theory of automated timetabling III (Third international conference, Patat 2000, Konstanz, Germany, August 2000, selected papers) by Edmund Burke and Wilhelm Erben (eds), Springer lecture notes in computer science, vol.2079, 2001 , 2003 .

[12]  Sanja Petrovic,et al.  Determining feature weights using a genetic algorithm in a case-based reasoning approach to personnel rostering , 2004 .

[13]  Jürgen Dorn,et al.  Case-based reactive scheduling , 1995 .

[14]  Kathryn A. Dowsland,et al.  Off-the-Peg or Made-to-Measure? Timetabling and Scheduling with SA and TS , 1997, PATAT.

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

[16]  Sanja Petrovic,et al.  Multiple-retrieval case-based reasoning for course timetabling problems , 2006, J. Oper. Res. Soc..

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

[18]  Paul Shaw,et al.  Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems , 1998, CP.

[19]  E. Burke,et al.  Case Based Heuristic Selection for Examination Timetabling , 2002 .

[20]  Gilbert Laporte,et al.  Recent Developments in Practical Course Timetabling , 1997, PATAT.

[21]  D. Costa,et al.  A tabu search algorithm for computing an operational timetable , 1994 .

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

[23]  Sanja Petrovic,et al.  Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering , 2006, Eur. J. Oper. Res..

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

[25]  Sanja Petrovic,et al.  Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling , 2002 .

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

[27]  Georges Weil,et al.  Cyclical Staff Scheduling Using Constraint Logic Programming , 2000, PATAT.

[28]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[29]  Margarida Vaz Pato,et al.  A Multiobjective Genetic Algorithm for the Class/Teacher Timetabling Problem , 2000, PATAT.

[30]  Hishammuddin Asmuni,et al.  Fuzzy Multiple Heuristic Orderings for Examination Timetabling , 2004, PATAT.

[31]  Kazuo Miyashita,et al.  CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair , 1995, Artif. Intell..

[32]  Andrew Lim,et al.  Nurse rostering problems - a bibliographic survey , 2003, Eur. J. Oper. Res..

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

[34]  Peter Ross,et al.  Comparing Genetic Algorithms, Simulated Annealing, and Stochastic Hillclimbing on Timetabling Problems , 1995, Evolutionary Computing, AISB Workshop.

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

[36]  John Williamson,et al.  Off‐the‐peg or made‐to‐measure? , 1990 .

[37]  Sanja Petrovic,et al.  Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems , 2002 .

[38]  Hendrik Van Landeghem,et al.  The State of the Art of Nurse Rostering , 2004, J. Sched..

[39]  Graham Kendall,et al.  A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology , 2004, PATAT.

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

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

[42]  Michel Gendreau,et al.  Multidisciplinary Scheduling: Theory and Applications , 2005 .

[43]  D. G. Johnson,et al.  SlotManager: a microcomputer-based decision support system for university timetabling , 2000, Decis. Support Syst..

[44]  Edmund Ph. D. Burke,et al.  Practice and theory of automated timetabling II : second International Conference, PATAT '97, Toronto, Canada, August 20-22, 1997 : selected papers , 1998 .

[45]  Michael W. Carter,et al.  A Lagrangian Relaxation Approach To The Classroom Assignment Problem , 1989 .

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

[47]  Sanja Petrovic,et al.  Structured cases in case-based reasoning - re-using and adapting cases for time-tabling problems , 2000, Knowl. Based Syst..

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

[49]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[50]  Edmund K. Burke,et al.  A simple heuristically guided search for the timetabling problem , 1998 .

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

[52]  Sanja Petrovic,et al.  Case-Based Reasoning in Course Timetabling: An Attribute Graph Approach , 2001, ICCBR.

[53]  Wilhelm Erben,et al.  A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling , 2000, PATAT.

[54]  E. Burke,et al.  Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems , 2005 .

[55]  Hishammuddin Asmuni,et al.  Fuzzy Multiple Ordering Criteria for Examination Timetabling , 2004 .

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

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

[58]  Jean Berger,et al.  A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints , 1999, GECCO.

[59]  Enric Plaza,et al.  CasedBased Reasoningc an overview , 1997 .

[60]  A Bender Michael,et al.  Journal of Scheduling , 2008 .

[61]  Günter Schmidt,et al.  Case-based reasoning for production scheduling , 1998 .

[62]  Sanja Petrovic,et al.  Examination Timetabling with Fuzzy Constraints , 2004, PATAT.

[63]  Sanja Petrovic,et al.  Similarity Measures for Exam Timetabling Problems , 2003 .

[64]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling II , 1997, Lecture Notes in Computer Science.

[65]  Sanja Petrovic,et al.  Storing and Adapting Repair Experiences in Employee Rostering , 2002, PATAT.

[66]  Siu Cheung Kong,et al.  A conceptual model of knowledge-based time-tabling system , 1999, Knowl. Based Syst..

[67]  Marco Dorigo,et al.  Metaheuristics for High School Timetabling , 1998, Comput. Optim. Appl..

[68]  Barry Smyth,et al.  Case-Based Reasoning in Scheduling: Reusing Solution Components. , 1996 .