Evolving Reusable Operation-Based Due-Date Assignment Models for Job Shop Scheduling with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{nguyen2:2012:EuroGP,

• author = "Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan",
• title = "Evolving Reusable Operation-Based Due-Date Assignment Models for Job Shop Scheduling with Genetic Programming",
• booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012",
• year = "2012",
• month = "11-13 " # apr,
• editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
• series = "LNCS",
• volume = "7244",
• publisher = "Springer Verlag",
• address = "Malaga, Spain",
• pages = "121--133",
• organisation = "EvoStar",
• isbn13 = "978-3-642-29138-8",
• DOI = "doi:10.1007/978-3-642-29139-5_11",
• keywords = "genetic algorithms, genetic programming, Job shop, Due-date assignment",
• abstract = "Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic features of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.",
• notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012",

}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan

Citations