For solving crane scheduling problem in steelmaking-continuous casting process, a hybrid method of integrating genetic algorithm and simulation was formulated to minimize the operation time of tasks by considering full ladles, empty ladles and auxiliary tasks as transportation tasks. The crane selected sequence was designed as chromosome coding. The initial population was generated by employing the crane selection rules in the simulation model, and then evolved optimized by using the genetic operations such as selection, crossover and mutation. The chromosome is evaluated through generating a feasible crane schedule by employing the attribute updating rules and collision eliminating rules in the simulation model. The new crane scheduling could be generated through simulation model based on the new species. Therefore, more excellent individuals will be produced along with the evolutionary process. Thus a better crane scheduling scheme could be obtained finally. In the process of initialization, the high quality initial population was generated in the simulation model. In the iterative process, simulation model also could generate the feasible crane scheduling schemes based on the given cranes selection sequence. To validate this model, experiments were conducted by using the production data in the casting span of a steel plant. The results demonstrate the feasibility and efficiency of this method, which provides a useful tool for crane scheduling in actual production.
| Published in | Science Innovation (Volume 4, Issue 6) |
| DOI | 10.11648/j.si.20160406.17 |
| Page(s) | 283-289 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Crane Scheduling, Simulation Model, Genetic Algorithm, Simulation Rules
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APA Style
Gao Xiaoqiang, Li Pan, Zheng Zhong, Jiang Shenglong, You Xiao. (2016). Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem. Science Innovation, 4(6), 283-289. https://doi.org/10.11648/j.si.20160406.17
ACS Style
Gao Xiaoqiang; Li Pan; Zheng Zhong; Jiang Shenglong; You Xiao. Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem. Sci. Innov. 2016, 4(6), 283-289. doi: 10.11648/j.si.20160406.17
AMA Style
Gao Xiaoqiang, Li Pan, Zheng Zhong, Jiang Shenglong, You Xiao. Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem. Sci Innov. 2016;4(6):283-289. doi: 10.11648/j.si.20160406.17
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Download@article{10.11648/j.si.20160406.17,
author = {Gao Xiaoqiang and Li Pan and Zheng Zhong and Jiang Shenglong and You Xiao},
title = {Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem},
journal = {Science Innovation},
volume = {4},
number = {6},
pages = {283-289},
doi = {10.11648/j.si.20160406.17},
url = {https://doi.org/10.11648/j.si.20160406.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20160406.17},
abstract = {For solving crane scheduling problem in steelmaking-continuous casting process, a hybrid method of integrating genetic algorithm and simulation was formulated to minimize the operation time of tasks by considering full ladles, empty ladles and auxiliary tasks as transportation tasks. The crane selected sequence was designed as chromosome coding. The initial population was generated by employing the crane selection rules in the simulation model, and then evolved optimized by using the genetic operations such as selection, crossover and mutation. The chromosome is evaluated through generating a feasible crane schedule by employing the attribute updating rules and collision eliminating rules in the simulation model. The new crane scheduling could be generated through simulation model based on the new species. Therefore, more excellent individuals will be produced along with the evolutionary process. Thus a better crane scheduling scheme could be obtained finally. In the process of initialization, the high quality initial population was generated in the simulation model. In the iterative process, simulation model also could generate the feasible crane scheduling schemes based on the given cranes selection sequence. To validate this model, experiments were conducted by using the production data in the casting span of a steel plant. The results demonstrate the feasibility and efficiency of this method, which provides a useful tool for crane scheduling in actual production.},
year = {2016}
}
TY - JOUR T1 - Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem AU - Gao Xiaoqiang AU - Li Pan AU - Zheng Zhong AU - Jiang Shenglong AU - You Xiao Y1 - 2016/12/07 PY - 2016 N1 - https://doi.org/10.11648/j.si.20160406.17 DO - 10.11648/j.si.20160406.17 T2 - Science Innovation JF - Science Innovation JO - Science Innovation SP - 283 EP - 289 PB - Science Publishing Group SN - 2328-787X UR - https://doi.org/10.11648/j.si.20160406.17 AB - For solving crane scheduling problem in steelmaking-continuous casting process, a hybrid method of integrating genetic algorithm and simulation was formulated to minimize the operation time of tasks by considering full ladles, empty ladles and auxiliary tasks as transportation tasks. The crane selected sequence was designed as chromosome coding. The initial population was generated by employing the crane selection rules in the simulation model, and then evolved optimized by using the genetic operations such as selection, crossover and mutation. The chromosome is evaluated through generating a feasible crane schedule by employing the attribute updating rules and collision eliminating rules in the simulation model. The new crane scheduling could be generated through simulation model based on the new species. Therefore, more excellent individuals will be produced along with the evolutionary process. Thus a better crane scheduling scheme could be obtained finally. In the process of initialization, the high quality initial population was generated in the simulation model. In the iterative process, simulation model also could generate the feasible crane scheduling schemes based on the given cranes selection sequence. To validate this model, experiments were conducted by using the production data in the casting span of a steel plant. The results demonstrate the feasibility and efficiency of this method, which provides a useful tool for crane scheduling in actual production. VL - 4 IS - 6 ER -
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