Please use this identifier to cite or link to this item: https://doi.org/10.1162/EVCO_a_00105
Title: Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments
Authors: Nguyen, S.
Zhang, M.
Johnston, M.
Tan, K.C. 
Keywords: Due-date assignment
Genetic programming
Hyper-heuristics
Job shop
Issue Date: 2014
Citation: Nguyen, S., Zhang, M., Johnston, M., Tan, K.C. (2014). Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments. Evolutionary Computation 22 (1) : 105-138. ScholarBank@NUS Repository. https://doi.org/10.1162/EVCO_a_00105
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. © 2014 by the Massachusetts Institute of Technology.
Source Title: Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/84410
ISSN: 10636560
DOI: 10.1162/EVCO_a_00105
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