Genetic Programming for Evolving Due-date Assignment Models in Job Shop Environments

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@Article{Nguyen:2013:EC,

• author = "Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan",
• title = "Genetic Programming for Evolving Due-date Assignment Models in Job Shop Environments",
• journal = "Evolutionary Computation",
• year = "2014",
• volume = "22",
• number = "1",
• pages = "105--138",
• month = "Spring",
• keywords = "genetic algorithms, genetic programming, job shop, due-date assignment, hyper-heuristics",
• ISSN = "1063-6560",
• URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_a_00105",
• DOI = "doi:10.1162/EVCO_a_00105",
• size = "27 pages",
• 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 nature 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.",

}

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

Citations