Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic
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- @Article{ZHOU:2019:procir,
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author = "Yong Zhou and Jian-jun Yang",
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title = "Automatic design of scheduling policies for dynamic
flexible job shop scheduling by multi-objective genetic
programming based hyper-heuristic",
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journal = "Procedia CIRP",
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volume = "79",
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pages = "439--444",
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year = "2019",
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note = "12th CIRP Conference on Intelligent Computation in
Manufacturing Engineering, 18-20 July 2018, Gulf of
Naples, Italy",
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ISSN = "2212-8271",
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DOI = "doi:10.1016/j.procir.2019.02.118",
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URL = "http://www.sciencedirect.com/science/article/pii/S2212827119302355",
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keywords = "genetic algorithms, genetic programming, dynamic
flexible job shop scheduling, scheduling policies,
multi-objective genetic programming, cooperative
coevolution",
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abstract = "This study proposes four multi-objective genetic
programming based hyper-heuristic methods(MO-GPHH) for
automated heuristic design to solve the multi-objective
dynamic flexible job shop scheduling problem(MO-DFJSP).
A scheduling policy(SP) used in the MO-DFJSP includes
two decision rules: a job sequencing rule(JSR) and a
machine assignment rule(MAR). These two rules are
simultaneously evolved to solve three scheduling
objectives (mean weighted tardiness, maximum tardiness
and mean flow time). The results demonstrate that the
pareto front of the proposed methods dominate that of
320 human-made SPs which are selected from literatures
on training set, and the evolved SPs outperform manual
SPs in 58/64 test scenarios",
- }
Genetic Programming entries for
Yong Zhou
Jian-Jun Yang
Citations