Multiple-objective scheduling for interbay AMHS by using genetic-programming-based composite dispatching rules generator
Created by W.Langdon from
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- @Article{Qin:2013:CI,
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author = "Wei Qin and Jie Zhang and Yinbin Sun",
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title = "Multiple-objective scheduling for interbay {AMHS} by
using genetic-programming-based composite dispatching
rules generator",
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journal = "Computers in Industry",
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volume = "64",
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number = "6",
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pages = "694--707",
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year = "2013",
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keywords = "genetic algorithms, genetic programming, Interbay
material handling system, Multiple-objective
scheduling, Composite dispatching rules",
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ISSN = "0166-3615",
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DOI = "doi:10.1016/j.compind.2013.03.009",
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URL = "http://www.sciencedirect.com/science/article/pii/S0166361513000626",
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abstract = "Semiconductor wafer fabrication system (SWFS) is one
of the most complicate discrete processing systems in
the world. As the wafer size grows from 200 to 300 mm
and then to 450 mm in recent years, the interbay
automated material handling system (AMHS) has been
widely adopted. How to improve the overall efficiency
of AMHS has therefore become a crucial and urgent
problem to wafer manufacturers. However, the
large-scale, dynamic and stochastic production
environment significantly substantiates the complexity
of the scheduling problem for interbay AMHS. Aiming to
meet the demand of multiple-objective optimisation,
composite dispatching rules (CDR) are applied. The
system parameters, including wafer cassettes due date,
waiting time, and stocker buffer status are
simultaneously considered. In order that the composite
dispatching rules can be used in real-life dynamic
production, a genetic programming based CDR generator
is proposed. Discrete event simulation models are
constructed using the eM-Plant software to simulate the
300 mm SWFS. The numerical study indicates that by
using the generated composite dispatching rules the
transport efficiency is improved, meanwhile, the wafer
throughput is increased and the processing cycle time
is shortened. The experimental results also demonstrate
that the GP-based generating algorithm is effective and
efficient for a dynamic environment. Further
comparisons with other scheduling methods show that the
proposed approach performs better in most scenarios.",
- }
Genetic Programming entries for
Wei Qin
Jie Zhang
Yinbin Sun
Citations