A Coevolution Genetic Programming Method to Evolve Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling Problems
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- @InProceedings{Nguyen:2012:CECb,
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title = "A Coevolution Genetic Programming Method to Evolve
Scheduling Policies for Dynamic Multi-objective Job
Shop Scheduling Problems",
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author = "Su Nguyen and Mengjie Zhang and Mark Johnston and
Tan Kay Chen",
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pages = "3332--3339",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6252968",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Heuristics,
metaheuristics and hyper-heuristics, Evolutionary
simulation-based optimization",
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abstract = "A 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 that those evolved by NSGA-II and
SPEA2.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Su Nguyen
Mengjie Zhang
Mark Johnston
Kay Chen Tan
Citations