Title

Learning effective dispatching rules for batch processor scheduling

Authors

Authors

C. D. Geiger;R. Uzsoyz

Comments

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Abbreviated Journal Title

Int. J. Prod. Res.

Keywords

dispatching rules; AI in manufacturing systems; batch scheduling; genetic algorithms; INCOMPATIBLE JOB FAMILIES; TOTAL WEIGHTED TARDINESS; TOTAL; COMPLETION-TIME; GENETIC ALGORITHM; MACHINE; METHODOLOGY; OPERATIONS; SELECTION; SYSTEM; Engineering, Industrial; Engineering, Manufacturing; Operations Research; & Management Science

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.

Journal Title

International Journal of Production Research

Volume

46

Issue/Number

6

Publication Date

1-1-2008

Document Type

Article

Language

English

First Page

1431

Last Page

1454

WOS Identifier

WOS:000252338400003

ISSN

0020-7543

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