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22-09-2023 | S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)

A novel method with constraints embedded into a cuckoo search for steelmaking–continuous casting scheduling

Authors: Haihong Wang, Hui Feng, Zhikao Ren, Chen Ye, Tongtong Zhao, Yue Sun, Xiuying Wang

Published in: Neural Computing and Applications

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Abstract

Featured by multi-charge, multi-process integration, multi-constraint, steelmaking and continuous casting (SCC) scheduling is a complex and industrial synthesis process. Generally, it is solved by the two-stage or multistage approach. To reduce time consumption, we propose a “one-stage” optimization method that integrates the constraints into the cuckoo search algorithm (CICSA). To obtain the minimum total waiting time (TWT), we built an SCC scheduling optimization model. Firstly, we integrate machine uniqueness constraints and the process sequence into the coding of the nests. Then, non-conflict constraints and casting on time constraints are converted into the fitness values of the cuckoo search algorithm (CSA). Thus, the solutions obtained in the population after iteration meet the process constraints. The non-conflict optimal nest is taken as the optimal solution. Simulations are conducted using the actual industrial data. Comparisons among the proposed algorithm, the two-stage algorithm, and the original CSA are presented. The result shows the proposed approach achieves better performance.

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Metadata
Title
A novel method with constraints embedded into a cuckoo search for steelmaking–continuous casting scheduling
Authors
Haihong Wang
Hui Feng
Zhikao Ren
Chen Ye
Tongtong Zhao
Yue Sun
Xiuying Wang
Publication date
22-09-2023
Publisher
Springer London
Published in
Neural Computing and Applications
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-08973-4

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