Linking search space structure, run-time dynamics, and problem difficulty: A step toward demystifying tabu search

Journal of Artificial Intelligence Research

Volumn 24, Issue , 2005, Pages 221-261

  Watson, Jean Paul ^a   Darrell Whitley, L ^b   Howe, Adele E ^b  

^a SANDIA NATIONAL LABORATORIES   (United States)

^b Colorado State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; HEURISTIC METHODS; OPTIMIZATION; PROBLEM SOLVING; SCHEDULING;

JOB-SHOP SCHEDULING PROBLEM (JSP); RUN-TIME DYNAMICS; SPACE STRUCTURE; TABU SEARCH;

SEARCH ENGINES;

EID: 31144450958     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1576     Document Type: Article

References (42)

1. Applegate, D., & Cook, W. (1991). A computational study of the job-shop scheduling problem. ORSA Journal on Computing, 5(2), 149-156.
2. Barnes, J., & Chambers, J. (1995). Solving the job shop scheduling problem with tabu search. IIE Transactions, 27, 257-263.
3. Beck, J., & Fox, M. (2000). Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics. Artificial Intelligence, 117(1), 31-81.
4. Blażewicz, J., Domschke, W., & Pesch, E. (1996). The job shop scheduling problem: Conventional and new solution techniques. European Journal of Operational Research, 93(1), 1-33.
5. Boese, K., Kahng, A., & Muddu, S. (1994). A new adaptive multi-start technique for combinatorial global optimizations. Operations Research Letters, 16(2), 101-113.
6. Cheeseman, P., Kanefsky, B., & Taylor, W. (1991). Where the really hard problems are. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91), pp. 331-337.
7. Clark, D., Frank, J., Gent, I., MacIntyre, E., Tomov, N., & Walsh, T. (1996). Local search and the number of solutions. In Freuder, E. C. (Ed.), Proceedings of the Second International Conference on Principles and Practices of Constraint Programming (CP-96), pp. 119-133. Springer-Verlag.
8. Demirkol, E., Mehta, S., & Uzsoy, R. (1998). Benchmarks for shop scheduling problems. European Journal of Operational Research, 109(1), 137-141.
9. Feller, W. (1968). An Introduction to Probability Theory and Its Applications (Third edition)., Vol. 1. John Wiley and Sons.
10. Garey, M., Johnson, D., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, 1(2), 117-129.
11. Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations Research, 8(4), 487-503.
12. Glover, F., & Laguna, M. (1997). Tabu Search. Kluwer Academic Publishers, Boston, Massachusetts.
13. Hajek, B. (1988). Cooling schedules for optimal annealing. Mathematics of Operations Research, 13(2), 311-329.
14. Hoos, H. (2002). A mixture-model for the behaviour of SLS algorithms for SAT. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 661-667. AAAI Press/The MIT Press.
15. Hoos, H. (1998). Stochastic Local Search - Methods, Models, Applications. Ph.D. thesis, Darmstadt University of Technology.
16. Jain, A., & Meeran, S. (1999). Deterministic job-shop scheduling: Past, present, and future. European Journal of Operational Research, 113(2), 390-434.
17. Jain, A., Rangaswamy, B., & Meeran, S. (2000). New and "stronger" job-shop neighborhoods: A focus on the method of Nowicki and Smutnicki(1996). Journal of Heuristics, 6(4), 457-480.
18. Kemeny, J., & Snell, J. (1960). Finite Markov Chains (First edition). D. Van Nostrand.
19. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 220, 671-680.
20. Lourenco, H., Martin, O., & Stützle, T. (2003). Iterated local search. In Glover, F., & Kochenberger, G. (Eds.), Handbook of Metaheuristics. Kluwer Academic Publishers.
21. Mattfeld, D. (1996). Evolutionary Search and the Job Shop. Physica-Verlag, Heidelberg.
22. Mattfeld, D., Bierwirth, C., & Kopfer, H. (1999). A search space analysis of the job shop scheduling problem. Annals of Operations Research, 86, 441-453.
23. Nowicki, E., & Smutnicki, C. (1996). A fast taboo search algorithm for the job shop problem. Management Science, 42(6), 797-813.
24. Nowicki, E., & Smutnicki, C. (2005). Metaheuristic Optimization Via Memory and Evolution, chap. Some New Ideas in TS for Job Shop Scheduling, pp. 165-190. Kluwer Academic Publishers.
25. Pezzella, F., & Merelli, E. (2000). A tabu search method guided by shifting bottleneck for the job shop scheduling problem. European Journal of Operational Research, 120(2), 297-310.
26. Reeves, C. (1998). Landscapes, operators and heuristic search. Annals of Operations Research, 86, 473-490.
27. Scheaffer, R., & McClave, J. (1990). Probability and Statistics for Engineers (3rd edition). PWS-Kent Publishing Company, Boston.
28. Schuurmans, D., & Southey, F. (2001). Local search characteristics of incomplete SAT procedures. Artificial Intelligence, 132(2), 121-150.
29. Singer, J. (2000). Why solutions can be hard to find: A featural theory of cost for a local search algorithm on random satisfiability instances. Ph.D. thesis, University of Edinburgh.
30. Singer, J., Gent, I., & Smaill, A. (2000). Backbone fragility and the local search cost peak. Journal of Artificial Intelligence Research, 12, 235-270.
31. Slaney, J., & Walsh, T. (2001). Backbones in optimization and approximation. In Nebel, B. (Ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 254-259. Morgan Kaufmann.
32. Stadler, P. (2002). Fitness landscapes. In Lassig, M., & Valleriani, A. (Eds.), Biological Evolution and Statistical Physics, pp. 187-207. Springer-Verlag, Berlin.
33. Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New search spaces for sequencing problems with application to job shop scheduling. Management Science, 88(10), 1495-1509.
34. Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285.
35. Taillard, E. (1994). Parallel taboo search techniques for the job shop scheduling problem. ORSA Journal on Computing, 6(2), 108-117.
36. Vaessens, R. (1995). Generalized Job Shop Scheduling: Complexity and Local Search. Ph.D. thesis, Eindhoven University of Technology.
37. van Laarhoven, P., Aarts, E., & Lenstra, J. (1992). Job shop scheduling by simulated annealing. Operations Research, 40(1), 113-125.
38. Watson, J. (2003). Empirical Modeling and Analysis of Local Search Algorithms for the Job-Shop Scheduling Problem. Ph.D. thesis, Department of Computer Science, Colorado State University.
39. Watson, J., Barbulescu, L., Whitley, L., & Howe, A. (2002). Contrasting structured and random permutation flow-shop scheduling problems: Search space topology and algorithm performance. INFORMS Journal on Computing, 14(2), 98-123.
40. Watson, J., Beck, J., Howe, A., & Whitley, L. (2001). Toward a descriptive model of local search cost in job-shop scheduling. In Workshop on Stochastic Search Algorithms at the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01).
41. Watson, J., Beck, J., Howe, A., & Whitley, L. (2003). Problem difficulty for tabu search in job-shop scheduling. Artificial Intelligence, 143(2), 189-217.
42. Werner, F., & Winkler, A. (1995). Insertion techniques for the heuristic solution of the job shop problem. Discrete Applied Mathematics, 58, 191-211.