Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-29139-5_11
Title: Evolving reusable operation-based due-date assignment models for job shop scheduling with genetic programming
Authors: Nguyen, S.
Zhang, M.
Johnston, M.
Tan, K.C. 
Keywords: Due-date assignment
Genetic programming
Job shop
Issue Date: 2012
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)
URI: http://scholarbank.nus.edu.sg/handle/10635/70228
ISBN: 9783642291388
ISSN: 03029743
DOI: 10.1007/978-3-642-29139-5_11
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