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,

• author = "Yong Zhou and Jian-jun Yang",
• title = "Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic",
• journal = "Procedia CIRP",
• volume = "79",
• pages = "439--444",
• year = "2019",
• note = "12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy",
• ISSN = "2212-8271",
• DOI = "doi:10.1016/j.procir.2019.02.118",
• URL = "http://www.sciencedirect.com/science/article/pii/S2212827119302355",
• keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, scheduling policies, multi-objective genetic programming, cooperative coevolution",
• 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

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