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|Title:||Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming|
|Source:||Nguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2012). Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7244 LNCS : 121-133. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-29139-5_11|
|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. © 2012 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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