Hopfield neural networks for timetabling: Formulations, methods, and comparative results

Computers and Industrial Engineering

Volumn 44, Issue 2, 2003, Pages 283-305

  Smith, Kate A ^a   Abramson, David ^a   Duke, David ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Combinatorial optimisation; Hopfield neural networks; Simulated annealing; Tabu search; Timetabling

Indexed keywords

ALGORITHMS; BENCHMARKING; HEURISTIC METHODS; OPTIMIZATION; PROBLEM SOLVING; SIMULATED ANNEALING;

HOPFIELD NEURAL NETWORKS;

NEURAL NETWORKS;

EID: 0037291553     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0360-8352(02)00180-8     Document Type: Article

References (58)

1. Abramson D. Constructing school timetables using simulated annealing: Sequential and parallel algorithms. Management Science. 37:(1):1991;98-113.
2. Abramson D., Dang H. School timetables: A case study in simulated annealing. Vidal V. Applied simulated annealing, Lecture notes in economics and mathematics systems. 1993;103-124 Springer, Berlin. Chapter 5.
3. Abramson D., Randall M. A simulated annealing code for general integer linear programs. Annals of Operations Research. 86:1999;3-21.
4. Abramson D., Smith K.A., Logethetis P., Duke D. FPGA based implementation of a Hopfield neural network for solving constraint satisfaction problems. Proceedings of EuroMicro Workshop on Computational Intelligence. 1998;688-693.
5. Bianco L., Bielli M., Mingozzi A., Ricciardelli S. A heuristic procedure for the crew rostering problem. European Journal of Operational Research. 58:(2):1992;272-283.
6. Burke E., Carter M. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Lecture notes in computer science. Vol. 1408:1998;Springer, Berlin.
7. Burke E., Elliman D., Weare R. Specialised recombinative operators for timetabling problems. Fogarty T.C. Evolutionary computing, AISB workshop. 1995;75-85 Springer, Berlin.
8. Burke E., Ross P. Practice and Theory of Automated Timetabling, Proceedings of First International Conference, Lecture notes in computer science. Vol. 1153:1996;Springer, Berlin.
9. Carter M., Laporte G. Recent developments in practical course timetabling, Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;3-19 Springer, Berlin.
10. Chan H.W., Lau C.K., Sheung J. Practical school timetabling: A hybrid approach using solution synthesis and iterative repair. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;Springer, Berlin.
11. Colorni A., Dorigo M., Maniezzo V. Genetic algorithms - A new approach to the timetable problem. Akgul A. Lecture notes in computer science. Vol. F82:1992;235-239 Springer, Berlin.
12. Connolly D. An improved annealing for the QAP. European Journal of Operational Research. 46:1990;93-100.
13. Connolly D. General purpose simulated annealing. Journal of the Operational Research Society. 43:1992;495-505.
14. Cooper T.B., Kingston J.H. The solution of real instances of the timetabling problem. The Computer Journal. 36:(7):1993;645-653.
15. Costa D. A tabu search algorithm for computing an operational timetable. European Journal of Operational Research. 76:1994;98-110.
16. Dowsland K.A. Off-the-peg or made-to-measure? Timetabling and scheduling with SA and TS. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;37-52 Springer, Berlin.
17. Elmohamed M.A.S., Coddington P., Fox G. A comparison of annealing techniques for academic course scheduling. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;92-112 Springer, Berlin.
18. Erben W., Keppler J. A genetic algorithm solving a weekly course-timetabling problem. Practice and Theory of Automated Timetabling, Proceedings of First International Conference, Burke E., Ross P. Lecture notes in computer science. Vol. 1153:1996;198-211 Springer, Berlin.
19. Even S., Itai A., Shamir A. On the complexity of timetable and multicommodity flow problems. SIAM Journal of Computing. 5:(4):1976;691-703.
20. Ferland J.A., Lavoie A. Exchange procedures for timetabling problems. Discrete Applied Mathematics. 35:(3):1992;237-253.
21. Gianoglio P. Application of neural networks to timetable construction. Neuro-Nimes'90, Proceedings of Third International Workshop on Neural Networks and their Applications. 1990;53-67.
22. Gislen L., Peterson C., Soderberg B. Teachers and classes with neural networks. International Journal of Neural Systems. 1:(2):1989;167-176.
23. Glover F., Laguna M. Tabu search. 1997;Kluwer, Boston.
24. Guéret C., Jussien N., Boizumault P., Prins C. Building university timetables using constraint logic programming. Practice and Theory of Automated Timetabling, Proceedings of First International Conference, Burke E., Ross P. Lecture notes in computer science. Vol. 1153:1996;130-145 Springer, Berlin.
25. Hertz A. Tabu search for large scale timetabling problems. European Journal of Operational Research. 54:1991;39-47.
26. Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of National Academy of Sciences. 79:1982;2554-2558.
27. Hopfield J.J. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of National Academy of Sciences. 81:1984;3088-3092.
28. Hopfield J.J., Tank D.W. Neural computation of decisions in optimization problems. Biological Cybernetics. 52:1985;141-152.
29. Hsu C.C., Lin D.M., Hu C.C. A timetabling model based on constraint checking technique for rostering problems. Proceedings of Third Pacific Rim International Conference on Artificial Intelligence. 1:1994;84-89.
30. Kamgar-Parsi B., Kamgar-Parsi B. An efficient model of neural networks for optimisation. Proceedings of IEEE International Conference on Neural Networks. 3:1987;785-790.
31. Kang L., White G.M. A logic approach to the resolution of constraints in timetabling. European Journal of Operational Research. 61:1992;306-317.
32. Kovacic M. Timetable construction with Markovian neural network. European Journal of Operational Research. 69:(1):1993;92-96.
33. Krarup J. Chromatic optimisation: Limitations, objectives, uses, references. European Journal of Operational Research. 11:1982;1-19.
34. Lim J.H., Loe K.F. Timetable scheduling using neural networks with parallel implementation on transputers. Proceedings of IEEE International Joint Conference on Neural Networks. 1:1991;122-127.
35. Mausser H.E., Magazine M.J. Comparison of neural and heuristic methods for a timetabling problem. European Journal of Operational Research. 93:(2):1996;271-287.
36. Mausser H.E., Magazine M.J., Moore J.B. Application of an annealed neural network to a timetabling problem. INFORMS Journal on Computing. 8:(2):1996;103-117.
37. Mehta N.K. The application of a graph coloring method to an examination scheduling problem. Interfaces. 11:1981;57-64.
38. Nepal T., Melville S.W., Ally M.I. A brute force and heuristics approach to tertiary timetabling. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;254-265 Springer, Berlin.
39. Osman I.H. Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research. 41:1993;421-451.
40. Osman I.H. Heuristics for the generalised assignment problem: Simulated annealing and tabu search approaches. Operations Research Spektrum. 17:1995;211-225.
41. Osman I.H., Laporte G. Metaheuristics: A bibliography. Annals of Operations Research. 63:1996;513-623.
42. Papageorgiou G., Likas A., Stafylopatis A. Improved exploration in Hopfield network state-space through parameter perturbation driven by simulated annealing. European Journal of Operational Research. 108:(2):1998;283-292.
43. Pellerin D., Herault J. Scheduling with neural networks: Application to timetable construction. Neurocomputing. 6:(4):1994;419-442.
44. Randall, M., Abramson, D., & Wild, C. (1999). A general meta-heuristic based solver for combinatorial optimisation problems. Technical Report, TR99-01. School of Information Technology, Bold University, Gold Coast, Queensland 4229, Australia.
45. Ross P., Corne D. Comparing genetic algorithms, simulated annealing, and stochastic hillclimbing on timetabling problems. Fogarty T.C. Evolutionary computing, AISB workshop. 1995;94-102 Springer, Berlin.
46. Ross P., Hart E., Corne D. Some observations about GA-based exam timetabling. Practice and Theory of Automated Timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;115-129 Springer, Berlin.
47. Schreuder J.A.M., van der Velde J.A. Timetables in Dutch high schools. Brans J.P. Operational research'84. 1984;601-612 North-Holland, Amsterdam.
48. Smith K.A. Neural networks for combinatorial optimisation: A review of more than a decade of research. INFORMS Journal on Computing. 11:(1):1999;15-34.
49. Smith K.A., Palaniswami M., Krishnamoorthy M. Traditional heuristic versus Hopfield neural network approaches to a car sequencing problem. European Journal of Operational Research. 93:(2):1996;300-316.
50. Smith K.A., Palaniswami M., Krishnamoorthy M. Neural techniques for combinatorial optimisation with applications. IEEE Transactions on Neural Networks. 9:(6):1998;1301-1318.
51. Taillard E. Robust taboo search for the quadratic assignment problem. Parallel Computing. 17:1991;443-455.
52. Tsuchiya K., Takefuji Y. A neural network parallel algorithm for meeting scheduling problems. Applied Intelligence. 7:(3):1997;205-213.
53. Van Den Bout D.E., Miller T.K. Improving the performance of the Hopfield-Tank neural network through normalization and annealing. Biological Cybernetics. 62:1989;129-139.
54. de Werra D. An introduction to timetabling. European Journal of Operational Research. 19:1985;151-162.
55. White G.M., Haddad M. A heuristic method for optimizing examination schedules which have day and night courses. Computers and Education. 7:(4):1983;235-238.
56. White G.M., Zhang J. Generating complete university timetables by combining tabu search with constraint logic. Practice and Theory of Automated timetabling, Proceedings of Second International Conference, Burke E., Carter M. Lecture notes in computer science. Vol. 1408:1998;187-198 Springer, Berlin.
57. Wilson G.V., Pawley G.S. On the stability of the TSP algorithm of Hopfield and Tank. Biological Cybernetics. 58:1988;63-70.
58. Wright M. School timetabling using heuristic search. Journal of the Operational Research Society. 47:(3):1996;347-357.