A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem.pdf (3.82 MB)

A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

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journal contribution
posted on 26.10.2020, 23:48 by S Nguyen, Mengjie Zhang, M Johnston, K Chen Tan
Designing effective dispatching rules is an important factor for many manufacturing systems. However, this time-consuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature are newly proposed in this paper and are compared and analysed. Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP. © 1997-2012 IEEE.

Funding

Genetic Programming for Job Shop Scheduling | Funder: ROYAL SOCIETY OF NEW ZEALAND | Grant ID: 12-VUW-134

History

Preferred citation

Nguyen, S., Zhang, M., Johnston, M. & Chen Tan, K. (2013). A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem. IEEE Transactions on Evolutionary Computation, 17(5), 621-639. https://doi.org/10.1109/TEVC.2012.2227326

Journal title

IEEE Transactions on Evolutionary Computation

Volume

17

Issue

5

Publication date

01/10/2013

Pagination

621-639

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

Online publication date

16/11/2012

ISSN

1089-778X

eISSN

1941-0026

Exports