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- W2980654321 abstract "Dynamic Job shop scheduling problem (DJSSP) which involves the dynamic characteristic of job arriving over time is one of the most popular scheduling problems in manufacturing system. In the paper, a machine learning approach is proposed for DJSSP. The proposed machine learning approach employs gene expression programming (GEP) which introduces automatically defined functions (ADF) into the representation of chromosome to construct efficient scheduling rules (SRs) automatically for DJSSP. The proposed GEP-based machine learning approach is evaluated and the results show that the approach is effective." @default.
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- W2980654321 date "2019-10-19" @default.
- W2980654321 modified "2023-09-26" @default.
- W2980654321 title "Machine Learning Approach Based on Gene Expression Programming for Dynamic Production Scheduling Problem" @default.
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- W2980654321 doi "https://doi.org/10.1088/1757-899x/612/3/032169" @default.
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