Learning effective dispatching rules for batch processor scheduling

Abstract

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.

Keywords:

• Dispatching rules
• AI in manufacturing systems
• Batch scheduling
• Genetic algorithms

Acknowledgments

The authors would like to thank the four anonymous reviewers of the initial and revised versions of the manuscript for their thoroughness and constructive suggestions that helped the development of this paper. This research was supported by the Naval Research Laboratory and the Purdue Research Foundation.