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A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

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journal contribution
posted on 29.10.2020, 00:49 by Su Nguyen, Yi Mei, Bing Xue, Mengjie Zhang
© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

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Preferred citation

Nguyen, S., Mei, Y., Xue, B. & Zhang, M. (2018). A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules. Evolutionary Computation, 27(3), 467-596. https://doi.org/10.1162/evco_a_00230

Journal title

Evolutionary Computation

Volume

27

Issue

3

Publication date

01/01/2018

Pagination

467-596

Publisher

MIT Press

Publication status

Published

ISSN

1063-6560

eISSN

1530-9304

Language

en

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