Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2012.6252968
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dc.titleA coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems
dc.contributor.authorNguyen, S.
dc.contributor.authorZhang, M.
dc.contributor.authorJohnston, M.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-06-19T02:52:36Z
dc.date.available2014-06-19T02:52:36Z
dc.date.issued2012
dc.identifier.citationNguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2012). A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems. 2012 IEEE Congress on Evolutionary Computation, CEC 2012 : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CEC.2012.6252968" target="_blank">https://doi.org/10.1109/CEC.2012.6252968</a>
dc.identifier.isbn9781467315098
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68733
dc.description.abstractA scheduling policy (SP) strongly influences the performance of a manufacturing system. However, the design of an effective SP is complicated and time-consuming due to the complexity of each scheduling decision as well as the interactions between these decisions. This paper proposes novel multi-objective genetic programming based hyper-heuristic methods for automatic design of SPs including dispatching rules (DRs) and due-date assignment rules (DDARs) in job shop environments. The experimental results show that the evolved Pareto front contains effective SPs that can dominate various SPs from combinations of existing DRs with dynamic and regression-based DDARs. The evolved SPs also show promising performance on unseen simulation scenarios with different shop settings. On the other hand, the proposed Diversified Multi-Objective Cooperative Coevolution (DMOCC) method can effectively evolve Pareto fronts of SPs compared to NSGA-II and SPEA2 while the uniformity of SPs obtained by DMOCC is better than those evolved by NSGA-II and SPEA2. © 2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CEC.2012.6252968
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CEC.2012.6252968
dc.description.sourcetitle2012 IEEE Congress on Evolutionary Computation, CEC 2012
dc.description.page-
dc.identifier.isiutNOT_IN_WOS
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