A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance

Authors:
Kate A. Smith-Miles;Ross J. James;John W. Giffin;Yiqing Tu
Affiliations:
School of Mathematical Sciences, Monash University, Melbourne, Australia;Department of Management, University of Canterbury, Christchurch, New Zealand;Department of Management, University of Canterbury, Christchurch, New Zealand;School of Mathematical Sciences, Monash University, Melbourne, Australia
Venue:
Learning and Intelligent Optimization
Year:
2009

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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.