Solving effectively the school timetabling problem using particle swarm optimization

A new adaptive algorithm based on particle swarm optimization (PSO) is designed, developed and applied to the high school timetabling problem. The proposed PSO algorithm is used in order to create feasible and very efficient timetables for high schools in Greece. Experiments with real-world data coming from many different high schools have been conducted in order to show the efficiency of the proposed PSO algorithm. As well as that, the algorithm has been compared with four other effective techniques found in the literature in order to demonstrate its efficiency and superior performance. The proposed PSO algorithm outperforms, in most cases, other existing attempts to solve the same problem as shown by experimental results.

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

[2]  Anthony Wren,et al.  Scheduling, Timetabling and Rostering - A Special Relationship? , 1995, PATAT.

[3]  Sanja Petrovic,et al.  Case-based selection of initialisation heuristics for metaheuristic examination timetabling , 2007, Expert Syst. Appl..

[4]  Çagdas Hakan Aladag,et al.  The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem , 2009, Expert Syst. Appl..

[5]  A. Najafi-Ardabili,et al.  Finding Feasible Timetables with Particle Swarm Optimization , 2007, 2007 Innovations in Information Technologies (IIT).

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

[7]  Huub M. M. ten Eikelder,et al.  Some Complexity Aspects of Secondary School Timetabling Problems , 2000, PATAT.

[8]  Hana Rudová,et al.  University Course Timetabling with Soft Constraints , 2002, PATAT.

[9]  Jeffrey H. Kingston.  A Tiling Algorithm for High School Timetabling , 2004, PATAT.

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

[11]  Gerhard F. Post,et al.  A Four-phase Approach to a Timetabling Problem in Secondary Schools , 2006 .

[12]  Michael Sampels,et al.  A MAX-MIN Ant System for the University Course Timetabling Problem , 2002, Ant Algorithms.

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

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

[15]  Kate A. Smith,et al.  Hopfield neural networks for timetabling: formulations, methods, and comparative results , 2003 .

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

[17]  Barry McCollum,et al.  University Timetabling: Bridging the Gap between Research and Practice , 2006 .

[18]  Yen-Zen Wang,et al.  Using genetic algorithm methods to solve course scheduling problems , 2003, Expert Syst. Appl..

[19]  Ben Paechter,et al.  A Comparison of the Performance of Different Metaheuristics on the Timetabling Problem , 2002, PATAT.

[20]  Grigorios N. Beligiannis,et al.  Applying evolutionary computation to the school timetabling problem: The Greek case , 2008, Comput. Oper. Res..

[21]  Efthymios Housos,et al.  A column generation approach for the timetabling problem of Greek high schools , 2003, J. Oper. Res. Soc..

[22]  Michael A. Trick.  A Schedule-Then-Break Approach to Sports Timetabling , 2000, PATAT.

[23]  Grigorios N. Beligiannis,et al.  A genetic algorithm approach to school timetabling , 2009, J. Oper. Res. Soc..

[24]  Hadrien Cambazard,et al.  Interactively Solving School Timetabling Problems Using Extensions of Constraint Programming , 2004, PATAT.

[25]  Edmund K. Burke,et al.  Decomposition, reformulation, and diving in university course timetabling , 2009, Comput. Oper. Res..

[26]  Jeffrey H. Kingston.  The KTS High School Timetabling System , 2006, PATAT.

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

[28]  Shu-Chuan Chu,et al.  Timetable Scheduling Using Particle Swarm Optimization , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

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

[30]  Efthymios Housos,et al.  Constraint programming approach for school timetabling , 2003, Comput. Oper. Res..

[31]  Hermann Gehring,et al.  Timetabling at German Secondary Schools: Tabu Search versus Constraint Programming , 2006 .

[32]  Nadia Nedjah,et al.  Evolutionary time scheduling , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

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

[34]  Amnon Meisels,et al.  Solving Employee Timetabling Problems by Generalized Local Search , 1999, AI*IA.