A dynamic material handling scheduling method based on elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) and knowledge base (KB) for line-integrated supermarkets
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-021-06385-x.pdf
Reference44 articles.
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3. Boysen N, Emde S (2014) Scheduling the part supply of mixed-model assembly lines in line-integrated supermarkets. Eur J Oper Res 239(3):820–829. https://doi.org/10.1016/j.ejor.2014.05.029
4. Brest J, Bošković B, Greiner S, Žumer V, Maučec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput 11(7):617–629. https://doi.org/10.1007/s00500-006-0124-0
5. Chakravarty S, Mohapatra P, Dash PK (2016) Evolutionary extreme learning machine for energy price forecasting. Int J Knowl-Based Intell Eng Syst 20(2):75–96. https://doi.org/10.3233/kes-160331
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