Multiple-objective scheduling for interbay AMHS by using genetic-programming-based composite dispatching rules generator

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Qin:2013:CI,

• author = "Wei Qin and Jie Zhang and Yinbin Sun",
• title = "Multiple-objective scheduling for interbay {AMHS} by using genetic-programming-based composite dispatching rules generator",
• journal = "Computers in Industry",
• volume = "64",
• number = "6",
• pages = "694--707",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Interbay material handling system, Multiple-objective scheduling, Composite dispatching rules",
• ISSN = "0166-3615",
• DOI = "doi:10.1016/j.compind.2013.03.009",
• URL = "http://www.sciencedirect.com/science/article/pii/S0166361513000626",
• 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