Evolving Dispatching Rules Using Genetic Programming for Multi-objective Dynamic Job Shop Scheduling with Machine Breakdowns
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @Article{SHADY:2021:PC,
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author = "Salama Shady and Toshiya Kaihara and
Nobutada Fujii and Daisuke Kokuryo",
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title = "Evolving Dispatching Rules Using Genetic Programming
for Multi-objective Dynamic Job Shop Scheduling with
Machine Breakdowns",
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journal = "Procedia CIRP",
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volume = "104",
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pages = "411--416",
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year = "2021",
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note = "54th CIRP CMS 2021 - Towards Digitalized Manufacturing
4.0",
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ISSN = "2212-8271",
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DOI = "doi:10.1016/j.procir.2021.11.069",
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URL = "https://www.sciencedirect.com/science/article/pii/S2212827121009677",
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keywords = "genetic algorithms, genetic programming,
Multi-objective dynamic job shop scheduling, machine
breakdowns, dispatching rules",
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abstract = "Dynamic Job Shop Scheduling Problem (DJSSP) is an
NP-hard problem that has a great impact on production
performance in practice. The design of Dispatching
Rules (DRs) is very challenging because many shop
attributes need to be investigated. Therefore, this
paper proposes a Genetic Programming (GP) approach to
generate DRs automatically for multi-objective DJSSP
considering machine breakdowns. Computational
experiments are conducted to compare the GP rule
performance with 12 literature rules. The results
indicate the superiority of the GP rule in minimizing
mean flow time and makespan simultaneously. Finally,
the best evolved rule is analyzed, and the significant
attributes are extracted",
- }
Genetic Programming entries for
Salama Shady
Toshiya Kaihara
Nobutada Fujii
Daisuke Kokuryo
Citations