Abstract
Using a knowledge discovery approach, we seek insights into the relationships between problem structure and the effectiveness of scheduling heuristics. A large collection of 75,000 instances of the single machine early/tardy scheduling problem is generated, characterized by six features, and used to explore the performance of two common scheduling heuristics. The best heuristic is selected using rules from a decision tree with accuracy exceeding 97%. A self-organizing map is used to visualize the feature space and generate insights into heuristic performance. This paper argues for such a knowledge discovery approach to be applied to other optimization problems, to contribute to automation of algorithm selection as well as insightful algorithm design.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rice, J.R.: The Algorithm Selection Problem. Adv. Comp. 15, 65–118 (1976)
Watson, J.P., Barbulescu, L., Howe, A.E., Whitley, L.D.: Algorithm Performance and Problem Structure for Flow-shop Scheduling. In: Proc. AAAI Conf. on Artificial Intelligence, pp. 688–694 (1999)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE T. Evolut. Comput. 1, 67 (1997)
Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla-07: The Design and Analysis of An Algorithm Portfolio For SAT. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 712–727. Springer, Heidelberg (2007)
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. 556–569. Springer, Heidelberg (2002)
Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A Portfolio Approach to Algorithm Selection. In: Proc. IJCAI, pp. 1542–1543 (2003)
Nudelman, E., Leyton-Brown, K., Hoos, H., Devkar, A., Shoham, Y.: Understanding Random SAT: Beyond the Clauses-To-Variables Ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438–452. Springer, Heidelberg (2004)
Horvitz, E., Ruan, Y., Gomes, C., Kautz, H., Selman, B., Chickering, M.: A Bayesian Approach to Tackling Hard Computational Problems. In: Proc. 17th Conf. on Uncertainty in Artificial Intelligence, pp. 235–244. Morgan Kaufmann, San Francisco (2001)
Samulowitz, H., Memisevic, R.: Learning to solve QBF. In: Proc. 22nd AAAI Conf. on Artificial Intelligence, pp. 255–260 (2007)
Streeter, M., Golovin, D., Smith, S.F.: Combining multiple heuristics online. In: Proc. 22nd AAAI Conf. on Artificial Intelligence, pp. 1197–1203 (2007)
Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18, 77–95 (2002)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Brazdil, P., Soares, C., Costa, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Mach. Learn. 50, 251–277 (2003)
Ali, S., Smith, K.: On Learning Algorithm Selection for Classification. Appl. Soft Comp. 6, 119–138 (2006)
Stützle, T., Fernandes, S.: New Benchmark Instances for the QAP and the Experimental Analysis of Algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 199–209. Springer, Heidelberg (2004)
Carchrae, T., Beck, J.C.: Applying Machine Learning to Low Knowledge Control of Optimization Algorithms. Comput. Intell. 21, 373–387 (2005)
Shaw, M.J., Park, S., Raman, N.: Intelligent Scheduling With Machine Learning Capabilities: The Induction of Scheduling Knowledge. IIE Trans. 24, 156–168 (1992)
Knowles, J.D., Corne, D.W.: Towards Landscape Analysis to Inform the Design of a Hybrid Local Search for the Multiobjective Quadratic Assignment Problem. In: Abraham, A., Ruiz-Del-Solar, J., Koppen, M. (eds.) Soft Computing Systems: Design, Management and Applications, pp. 271–279. IOS Press, Amsterdam (2002)
Merz, P.: Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms. Evol. Comp. 2, 303–325 (2004)
Watson, J., Beck, J.C., Howe, A.E., Whitley, L.D.: Problem Difficulty for Tabu Search in Job-Shop Scheduling. Artif. Intell. 143, 189–217 (2003)
Smith-Miles, K.A.: Cross-Disciplinary Perspectives on Meta-Learning For Algorithm Selection. ACM Computing Surveys (in press, 2009)
Baker, K.R., Scudder, G.D.: Sequencing With Earliness and Tardiness Penalties: A Review. Ops. Res. 38, 22–36 (1990)
James, R.J.W., Buchanan, J.T.: A Neighbourhood Scheme with a Compressed Solution Space for The Early/Tardy Scheduling Problem. Eur. J. Oper. Res. 102, 513–527 (1997)
Fry, T.D., Armstrong, R.D., Blackstone, J.H.: Minimizing Weighted Absolute Deviation in Single Machine Scheduling. IIE Transactions 19, 445–450 (1987)
Vollmann, T.E., Berry, W.L., Whybark, D.C., Jacobs, F.R.: Manufacturing Planning and Control for Supply Chain Management, 5th edn. McGraw Hill, New York (2005)
Krajewski, L.J., Ritzman, L.P.: Operations Management: Processes and Value Chains, 7th edn. Pearson Prentice Hall, New Jersey (2005)
Schiavinotto, T., Stützle, T.: A review of metrics on permutations for search landscape analysis. Comput. Oper. Res. 34, 3143–3153 (2007)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.G.: Meta-Learning by Landmarking Various Learning Algorithms. In: Proc. ICML, pp. 743–750 (2000)
Baker, K.B., Martin, J.B.: An Experimental Comparison of Solution Algorithms for the Single Machine Tardiness Problem. Nav. Res. Log. 21, 187–199 (1974)
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Meta-heuristics, pp. 457–474. Kluwer, Norwell (2002)
Smith, K.A.: Neural Networks for Prediction and Classification. In: Wang, J. (ed.) Encyclopaedia of Data Warehousing and Mining, vol. 2, pp. 865–869. Information Science Publishing, Hershey (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biol. Cyber. 43, 59–69 (1982)
Achlioptas, D., Naor, A., Peres, Y.: Rigorous Location of Phase Transitions in Hard Optimization Problems. Nature 435, 759–764 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Smith-Miles, K.A., James, R.J.W., Giffin, J.W., Tu, Y. (2009). A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-11169-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11168-6
Online ISBN: 978-3-642-11169-3
eBook Packages: Computer ScienceComputer Science (R0)