Abstract
This paper addresses the question of selecting an algorithm from a predefined set that will have the best performance on a scheduling problem instance. Our goal is to reduce the expertise needed to apply constraint technology. Therefore, we investigate simple rules that make predictions based on limited problem instance knowledge. Our results indicate that it is possible to achieve superior performance over choosing the algorithm that performs best on average on the problem set. The results hold over a variety of different run lengths and on different types of scheduling problems and algorithms. We argue that low-knowledge approaches are important in reducing expertise required to exploit optimization technology.
This work has received support from Science Foundation Ireland under Grant 00/PI.1/C075 and ILOG, SA.
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References
Rice, J.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)
Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 556–572. Springer, Heidelberg (2002)
Minton, S.: Automatically configuring constraint satisfaction programs: A case study. CONSTRAINTS 1, 7–43 (1996)
Horvitz, E., Ruan, Y., Gomes, C., Kautz, H., Selman, B., Chickering, M.: A bayesian approach to tacking hard computational problems. In: Proceedings of the Seventeenth Conference on uncertainty and Artificial Intelligence (UAI 2001), pp. 235–244 (2001)
Kautz, H., Horvitz, E., Ruan, Y., Gomes, C., Selman, B.: Dynamic restart policies. In: Proceedings of the Eighteenth National Conference on Artifiical Intelligence (AAAI 2002), pp. 674–681 (2002)
Ruan, Y., Horvitz, E., Kautz, H.: Restart policies with dependence among runs: A dynamic programming approach. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 573–586. Springer, Heidelberg (2002)
Watson, J.P.: Empirical Modeling and Analysis of Local Search Algorithms for the Job-Shop Scheduling Problem. PhD thesis, Dept. of Computer Science, Colorado State University (2003)
Watson, J.P., Barbulescu, L., Whitley, L., Howe, A.: Constrasting structured and random permutation flow-shop scheduling problems: search-space topology and algorithm performance. INFORMS Journal on Computing 14 (2002)
Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Management Science 42, 797–813 (1996)
Beck, J.C., Fox, M.S.: Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics. Artificial Intelligence 117, 31–81 (2000)
Nuijten, W.P.M.: Time and resource constrained scheduling: a constraint satisfaction approach. PhD thesis, Department of Mathematics and Computing Science, Eindhoven University of Technology (1994)
Laborie, P.: Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results. Artificial Intelligence 143, 151–188 (2003)
Luby, M., Sinclair, A., Zuckerman, D.: Optimal speedup of Las Vegas algorithms. Information Processing Letters 47, 173–180 (1993)
Scheduler: ILOG Scheduler 5.2 User’s Manual and Reference Manual. ILOG, S.A. (2001)
Beck, J.C., Perron, L.: Discrepancy-bounded depth first search. In: Proceedings of the Second International Workshop on Integration of AI and OR Technologies for Combinatorial Optimization Problems, CPAIOR 2000 (2000)
Cohen, P.R.: Empirical Methods for Artificial Intelligence. The MIT Press, Cambridge (1995)
Beck, J.C., Refalo, P.: Combining local search and linear programming to solve earliness/tardiness scheduling problems. In: Proceedings of the Fourth International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2002 (2002)
Vazquez, M., Whitley, L.D.: A comparision of genetic algorithms for the dynamic job shop scheduling problem. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 1011–1018. Morgan Kaufmann, San Francisco (2000)
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Beck, J.C., Freuder, E.C. (2004). Simple Rules for Low-Knowledge Algorithm Selection. In: Régin, JC., Rueher, M. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2004. Lecture Notes in Computer Science, vol 3011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24664-0_4
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DOI: https://doi.org/10.1007/978-3-540-24664-0_4
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