Title

Learning Effective Dispatching Rules For Batch Processor Scheduling

Keywords

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

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.

Publication Date

3-1-2008

Publication Title

International Journal of Production Research

Volume

46

Issue

6

Number of Pages

1431-1454

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/00207540600993360

Socpus ID

36348991068 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/36348991068

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