Research on Capacity Planning of Cold Rolled Steel Production Line Based on the Modified Differential Evolution Algorithm
- https://doi.org/10.2991/assehr.k.200328.054How to use a DOI?
- differential evolution algorithm, capacity planning of cold rolling production line, multi-objective optimization
The problem of steel cold rolling production capacity is an important issue in steel production. In this paper, mathematical modeling is carried out to meet the actual constraints of production process, inventory, flow balance, etc. And to determine the type and corresponding output of a certain product in each machine in every process in the production network, then obtain the inventory of each product between processes. The objectives of the model are to maximize unit capacity and minimize production switching costs. Differential evolution is a population-based evolutionary algorithm. It has the characteristics of recording the best solution during searching history and exchanging and sharing information within the population. That is, the optimization problem can be solved through the cooperation and competition among individuals within the population. In this paper, the differential evolution algorithm is modified for steel cold rolling production line capacity planning problem, the modified differential evolution algorithm comparing with other multi-objectives genetic algorithm. The results show that the proposed algorithm is superior to other standard genetic algorithm and can fast convergence, thus verified the feasibility and effectiveness of the algorithm.
- © 2020, the Authors. Published by Atlantis Press.
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- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhengqi Sui AU - Ziyang Yu PY - 2020 DA - 2020/04/01 TI - Research on Capacity Planning of Cold Rolled Steel Production Line Based on the Modified Differential Evolution Algorithm BT - Proceedings of the International Conference on Arts, Humanity and Economics, Management (ICAHEM 2019) PB - Atlantis Press SP - 293 EP - 305 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200328.054 DO - https://doi.org/10.2991/assehr.k.200328.054 ID - Sui2020 ER -