An Evolutionary Method of Computation for Dynamic Scheduling Problems with Periodic Demand
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- @InProceedings{Hirotani:2021:IWCIA,
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author = "Daisuke Hirotani and Tomohiro Hayashida and
Ichiro Nishizaki and Shinya Sekizaki and Ibuki Maeda",
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title = "An Evolutionary Method of Computation for Dynamic
Scheduling Problems with Periodic Demand",
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booktitle = "2021 IEEE 12th International Workshop on Computational
Intelligence and Applications (IWCIA)",
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year = "2021",
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abstract = "Dynamic scheduling for irregularly arriving jobs is
considered. In the real world, demands often change for
some reason suddenly. In a previous paper (Eguchi et
al., 2006), the optimal schedule was determined by
using a neural network. That method was based on
existing dispatching rules that determined the job
order sequence. Here, a new method using genetic
programing is proposed, in which new dispatching rules
are generated. By generating a new rule, performance
can be increased. Also, in the real world, job arrivals
vary periodically depending on the season or month. By
using past data, scheduling can be done effectively.
Therefore, this paper proposes a new parallel genetic
programming introducing long-term memories to use past
data. The results of numerical experiments indicate the
effectiveness of the proposed method.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/IWCIA52852.2021.9626045",
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ISSN = "1883-3977",
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month = nov,
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notes = "Also known as \cite{9626045}",
- }
Genetic Programming entries for
Daisuke Hirotani
Tomohiro Hayashida
Ichiro Nishizaki
Shinya Sekizaki
Ibuki Maeda
Citations