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. © 2009 Springer-Verlag.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5851 LNCS, pp. 89–103). https://doi.org/10.1007/978-3-642-11169-3_7
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