Very large-scale neighborhood search techniques in timetabling problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3867 LNCS, Issue , 2006, Pages 24-39

  Meyers, Carol ^a   Orlin, James B ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; GENETIC ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; SCHEDULING ALGORITHMS; SIMULATED ANNEALING;

FUNCTIONAL ANNEALING; VERY LARGE SCALE NEIGHBORHOOD SEARCH (VLSN) TECHNIQUES;

LARGE SCALE SYSTEMS;

EID: 38549108935     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (52)

1. Abdullah, S., Ahmadi, S., Burke, E.K., Dror, M.: Investigating Ahuja-Orlin's large neighbourhood search approach for examination timetabling. OR Spectrum 29, 351-372 (2007)
2. Abdullah, S., Ahmadi, S., Burke, E.K., Dror, M., McCollum, B.: A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem. Journal of the Operational Research Society (to appear, 2007)
3. Agarwal, R., Ahuja, R., Laporte, G., Shen, Z.: A composite very large-scale neighborhood search algorithm for the vehicle routing problem. In: Handbook of Scheduling: Algorithms, Models and Performance Analysis, ch. 49, Chapman and Hall/CRC, Boca Raton, FL (2003)
4. Agarwal, R., Ergun, Ö., Orlin, J., Potts, C.: Solving parallel machine scheduling problems with variable depth local search. Working Paper, Operations Research Center, MIT, Cambridge, MA (2004)
5. Aggarwal, C., Orlin, J., Tai, R.: Optimized crossover for the independent set problem. Operations Research 45, 226-234 (1997)
6. Ahuja, R., Ergun, Ö., Orlin, J., Punnen, A.: A survey of very large-scale neighborhood search techniques. Discrete Applied Mathematics 123, 75-102 (2002)
7. Ahuja, R., Ergun, Ö., Orlin, J., Punnen, A.: Very large-scale neighborhood search: theory, algorithms, and applications. Working Paper, Operations Research Center, MIT, Cambridge, MA (2006)
8. Ahuja, R., Jha, K., Orlin, J., Sharma, D.: Very large-scale neighborhood search for the quadratic assignment problem. Working Paper, Operations Research Center, MIT, Cambridge, MA (2002)
9. Ahuja, R., Orlin, J., Magnanti, T.: Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Upper Saddle River, NJ (1993)
10. Ahuja, R., Orlin, J., Sharma, D.: Very large-scale neighborhood search. International Transactions in Operational Research 7, 301-317 (2000)
11. Ahuja, R., Orlin, J., Sharma, D.: Multi-exchange neighborhood structures for the capacitated minimum spanning tree problem. Mathematical Programming 91, 71-97 (2001)
12. Ahuja, R., Orlin, J., Tiwari, A.: A greedy genetic algorithm for the quadratic assignment problem. Computers and Operations Research 27, 917-934 (2000)
13. Alvarez-Valdes, R., Martin, G., Tamarit, J.: Constructing good solutions for the Spanish school timetabling problem. Journal of the Operational Research Society 47, 1203-1215 (1996)
14. Avella, P., D'Auria, B., Salerno, S., Vasil'ev, I.: Computational experience with very large-scale neighborhood search for high-school timetabling. Working Paper, Research Center on Software Technology, Universita del Sannio, Italy (2006)
15. Ayob, M., Kendall, G.: A Monte Carlo hyper heuristic to optimise component placement sequencing for multi-head placement machine. In: Proceedings of the 4th International Conference on Intelligent Technologies, Chiang Mai, Thailand, pp. 132-141. Institute for Science and Technology Research and Development, Chiang Mai University (2003)
16. Balas, E., Niehaus, W.: Finding large cliques in arbitrary graphs by bipartite matching. In: Clique, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, pp. 29-53. AMS (1996)
17. Balas, E., Niehaus, W.: Optimized crossover-based genetic algorithms for the maximum cardinality and maximum weight clique problems. Journal of Heuristics 4, 107-122 (1998)
18. Bertsimas, D., Tsitsiklis, J.: Introduction to Linear Optimization, Athena Scientific, Belmont, MA (1997)
19. Bullnheimer, B.: An examination scheduling model to maximize students' study time. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 78-91. Springer, Heidelberg (1998)
20. Burke, E.K., Elliman, D., Weare, R.: A hybrid genetic algorithm for highly constrained timetabling problems. In: Proceedings of the 6th International Conference on Genetic Algorithms, Pittsburgh, PA, pp. 605-610. Morgan Kaufmann, San Mateo, CA (1995)
21. Burke, E.K., Newall, J., Weare, R.: A memetic algorithm for university exam timetabling. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp, 241-250. Springer, Heidelberg (1996)
22. Burke, E.K., Petrovic, S.: Recent research directions in automated timetabling. European Journal of Operational Research 140, 266-280 (2002)
23. Carter, M.: A comprehensive course timetabling and student scheduling system at the University of Waterloo. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 64-82. Springer, Heidelberg (2001)
24. Carter, M., Laporte, G.: Recent developments in practical examination timetabling. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp. 3-21. Springer, Heidelberg (1996)
25. Carter, M., Laporte, G.: Recent developments in practical course timetabling. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 3-19. Springer, Heidelberg (1998)
26. Cheng, C., Kang, L., Leung, N., White, G.: Investigations of a constraint logic programming approach to university timetabling. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp. 112-129. Springer, Heidelberg (1996)
27. Colorni, A., Dorigo, M., Maniezzo, V.: Metaheuristics for high-school timetabling. Computational Optimization and Applications 9, 275-298 (1998)
28. Corne, D., Ross, P., Fang, H.: Fast practical evolutionary timetabling. In: Fogarty, T.C. (ed.) Evolutionary Computing. LNCS, vol. 865, pp. 251-263. Springer, Heidelberg (1994)
29. Deineko, V., Woeginger, G.: A study of exponential neighborhoods for the travelling salesman problem and for the quadratic assignment problem. Mathematical Programming 87, 255-279 (2000)
30. Ergun, O.: New neighborhood search algorithms based on exponentially large neighborhoods. Ph.D. Dissertation, Massachusetts Institute of Technology (June 2001)
31. Fang, H.: Genetic algorithms in timetabling and scheduling. Ph.D. Dissertation, University of Edinburgh (September 1994)
32. Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)
33. Glover, F.: Ejection chains, reference structures and alternating path methods for traveling salesman problems. Discrete Applied Mathematics 65, 223-253 (1996)
34. Jha, K.: Very large-scale neighborhood search heuristics for combinatorial optimization problems. Ph.D. Dissertation, University of Florida (June 2004)
35. Junginger, W.: Timetabling in Germany - a survey. Interfaces 16, 66-74 (1986)
36. Lin, S., Kernighan, B.: An effective heuristic algorithm for the traveling salesman problem. Operations Research 21, 498-516 (1973)
37. Lourenço, H., Paixão, J., Portugal, R.: Multiobjective metaheuristics for the bus driver scheduling problem. Transportation Science 35, 331-343 (2001)
38. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)
39. Neufeld, G., Tartar, J.: Graph coloring conditions for the existence of solutions to the timetable problem. Communications of the ACM 17, 450-453 (1974)
40. Punnen, A., Kabadi, S.: Domination analysis of some heuristics for the traveling salesman problem. Discrete Applied Mathematics 119, 117-128 (2002)
41. Ribeiro, C., Vianna, D.: A genetic algorithm for the phylogeny problem using an optimized crossover strategy based on path relinking. In: Proceedings of the 2nd Brazilian Workshop on Bioinformatics, Macaé, Brazil, pp. 97-102. SBC (2003)
42. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Working Paper, Department of Computer Science, University of Copenhagen, Denmark (2005)
43. Schaerf, A.: Local search techniques for large high school timetabling problems. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 29, 368-377 (1999)
44. Schaerf, A.: A survey of automated timetabling. Artificial Intelligence Review 13, 87-127 (1999)
45. Sharma, D., Sukhapesna, S.: Functional annealing technique for very large-scale neighborhood search. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI (in preparation)
46. Sukhapesna, S.: Generalized annealing algorithms for discrete optimization problems. Ph.D. Dissertation, University of Michigan (September 2005)
47. Thompson, P.: Local search algorithms for vehicle routing and other combinatorial problems. Ph.D. Dissertation, Massachusetts Institute of Technology (May 1988)
48. Thompson, J., Dowsland, K.: A robust simulated annealing based examination timetabling system. Computers and Operations Research 25, 637-648 (1998)
49. Thompson, P., Orlin, J.: The theory of cyclic transfers. Working Paper OR 200-89, Operations Research Center, MIT, Cambridge, MA (1989)
50. Thompson, P., Psaraftis, H.: Cyclic transfer algorithms for multivehicle routing and scheduling problems. Operations Research 41, 935-946 (1993)
51. Yagiura, M., Ibaraki, T.: Recent metaheuristic algorithms for the generalized assignment problem. In: Proceedings of the 12th International Conference on Informatics Research for Development of Knowledge Society Infrastructure, Kyoto, Japan, pp. 229-237. IEEE Computer Society Press, Los Alamitos, CA (2004)
52. Yagiura, M., Iwasaki, S., Ibaraki, T., Glover, F.: A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem. Discrete Optimization 1, 87-98 (2004)