An Evolutionary Method of Computation for Dynamic Scheduling Problems with Periodic Demand

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@InProceedings{Hirotani:2021:IWCIA,

• author = "Daisuke Hirotani and Tomohiro Hayashida and Ichiro Nishizaki and Shinya Sekizaki and Ibuki Maeda",
• title = "An Evolutionary Method of Computation for Dynamic Scheduling Problems with Periodic Demand",
• booktitle = "2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA)",
• year = "2021",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IWCIA52852.2021.9626045",
• ISSN = "1883-3977",
• month = nov,
• notes = "Also known as \cite{9626045}",

}

Genetic Programming entries for Daisuke Hirotani Tomohiro Hayashida Ichiro Nishizaki Shinya Sekizaki Ibuki Maeda

Citations