Decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies for shop floor multi-crane scheduling problems-Reference-Cited by-同舟云学术

Decomposition-based 2-echelon multi-objective evolutionary algorithm with energy-efficient local search strategies for shop floor multi-crane scheduling problems

Published:2020-01-30 Issue:14 Volume:32 Page:10719-10739
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhou Binghai^ORCID,Liao Xiumei

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04608-9.pdf

Reference56 articles.

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3. Salahi N, Jafari MA (2016) Energy-performance as a driver for optimal production planning. Appl Energ 174:88–100. https://doi.org/10.1016/j.apenergy.2016.04.085

4. Reddy SS, Bijwe PR (2017) Differential evolution-based efficient multi-objective optimal power flow. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3009-5

5. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731. https://doi.org/10.1109/TEVC.2007.892759

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