Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling-Reference-Cited by-同舟云学术

Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Zhang Fangfang¹,Mei Yi¹,Nguyen Su²,Zhang Mengjie¹

Affiliation:

1. School of Engineering and Computer Science, Victoria University of Wellington,New Zealand

2. Centre for Data Analytics and Cognition, La Trobe University,Victoria,Melbourne,Australia,3086

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870216/9870201/09870243.pdf?arnumber=9870243

Reference22 articles.

1. Towards improved dis-patching rules for complex shop floor scenarios: a genetic programming approach;hildebrandt;Proceedings of the Genetic and Evolutionary Computation Conference,0

2. Genetic Programming for Production Scheduling

3. Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

4. Comparison of schedule generation schemes for designing dispatching rules with genetic programming in the unrelated machines environment;jurasevic;Applied Soft Computing,2020

5. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems

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