Specialised Recombinative Operators for Timetabling Problems

This paper discusses a series of recombination operators for the timetabling problem. These operators act upon a direct representation of the timetable and maintain the property of feasibility. That is that there are no conflicts and no overfilled rooms. Various approaches to solving the timetabling problem using evolutionary computing methods are first compared. The recombination operators are then presented and various alternatives for incorporating heuristic knowledge in the search are described. Finally, results are presented comparing the operators on a real timetabling problem.

[1]  George M. White,et al.  A logic approach to the resolution of constraints in timetabling , 1992 .

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

[3]  David Corne,et al.  Evolutionary Timetabling: Practice, Prospects and Work in Progress , 1994 .

[4]  Edmund K. Burke,et al.  A University Timetabling System Based on Graph Colouring and Constraint Manipulation , 1994 .

[5]  Marco Dorigo,et al.  Genetic Algorithms and Highly Constrained Problems: The Time-Table Case , 1990, PPSN.

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

[7]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[8]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning , 1991, Oper. Res..

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

[10]  A. Tripathy School Timetabling---A Case in Large Binary Integer Linear Programming , 1984 .

[11]  Patrick D. Surry,et al.  Formal Memetic Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[12]  Ben Paechter,et al.  Two solutions to the general timetable problem using evolutionary methods , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[13]  Peter J. B. Hancock,et al.  An Empirical Comparison of Selection Methods in Evolutionary Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[14]  Peter Ross,et al.  Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation , 1994, PPSN.

[15]  Patrice Boizumault,et al.  Efficient labeling and Constraint Relaxation for Solving Time Tabling Problems , 1994 .

[16]  Si-Eng Ling,et al.  Integrating Genetic Algorithms with a Prolog Assignment Program as a Hybrid Solution for a Polytechnic Timetable Problem , 1992, Parallel Problem Solving from Nature.

[17]  David Abramson,et al.  A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM , 1992 .

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

[19]  Peter Ross,et al.  Fast Practical Evolutionary Timetabling , 1994, Evolutionary Computing, AISB Workshop.

[20]  D. J. A. Welsh,et al.  An upper bound for the chromatic number of a graph and its application to timetabling problems , 1967, Comput. J..