A Genetic Programming-Based Evolutionary Approach for Flexible Job Shop Scheduling with Multiple Process Plans
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Zhu:2020:CASE,
-
author = "Xuedong Zhu and Weihao Wang and Xinxing Guo and
Leyuan Shi",
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title = "A Genetic Programming-Based Evolutionary Approach for
Flexible Job Shop Scheduling with Multiple Process
Plans",
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booktitle = "2020 IEEE 16th International Conference on Automation
Science and Engineering (CASE)",
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year = "2020",
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pages = "49--54",
-
abstract = "This paper investigates a more general flexible job
shop scheduling problem with multiple process plans
which is common in the modern manufacturing system. As
an extension of the traditional flexible job shop
scheduling problem, various realistic flexibility such
as processing flexibility, machine flexibility and
sequencing flexibility are considered in this problem.
Due to the high complexity and the real-time
requirement of this problem, a genetic
programming-based evolutionary approach is proposed to
automatically generate effective dispatching rules for
this problem, and an evaluation method is developed to
evaluate the generated dispatching rules. Three
experiments are conducted to evaluate the performance
of the proposed approach for real cases with
large-scale test problems. Numerical results show that
the proposed approach outperforms the classical
dispatching rules and the state-of-theart algorithms,
and is able to provide higher-quality solutions with
less computational time.",
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keywords = "genetic algorithms, genetic programming, Dispatching,
Sequential analysis, Job shop scheduling, Manufacturing
systems, Conferences",
-
DOI = "doi:10.1109/CASE48305.2020.9216783",
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ISSN = "2161-8089",
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month = aug,
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notes = "Also known as \cite{9216783}",
- }
Genetic Programming entries for
Xuedong Zhu
Weihao Wang
Xinxing Guo
Leyuan Shi
Citations