Skip to main content
SpringerLink
Log in

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

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Wang X-Y, Feng H, Ren Z-K, Zhou Y-P (2016) Two-stage optimal modelling and algorithm of production scheduling for steelmaking and continuous casting. Acta Autom Sin 42:1702–1710

    Google Scholar 

  2. Hong J, Moon K, Lee K et al (2022) An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process. Int J Prod Res 60:623–643

    Article  Google Scholar 

  3. Cui H, Luo X, Wang Y (2020) Scheduling of steelmaking-continuous casting process using deflected surrogate Lagrangian relaxation approach and DC algorithm. Comput Ind Eng 140:106271

    Article  Google Scholar 

  4. Cui H, Luo X (2017) An improved lagrangian relaxation approach to scheduling steelmaking–continuous casting process. Comput Chem Eng 106:133–146

    Article  Google Scholar 

  5. Han D, Tang Q, Zhang Z et al (2021) An efficient augmented lagrange multiplier method for steelmaking and continuous casting production scheduling. Chem Eng Res Des 168:169–192

    Article  Google Scholar 

  6. Pan R, Wang Q, Li Z et al (2022) Steelmaking-continuous casting scheduling problem with multi-position refining furnaces under time-of-use tariffs. Ann Oper Res 310:119–151

    Article  MathSciNet  Google Scholar 

  7. Pan QK, Wang L, Mao K et al (2013) An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Trans Autom Sci Eng 10:307–322

    Article  Google Scholar 

  8. Feng H, Wang X (2016) A novel schedule method for steelmaking and continuous casting base on cuckoo search algorithm. In: 2016 5th International conference on computer science and network technology (ICCSNT). IEEE, pp 45–49

  9. Pan QK (2016) An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling. Eur J Oper Res 250:702–714

    Article  MathSciNet  Google Scholar 

  10. Yang J, Wang B, Liu Q et al (2020) Scheduling model for the practical steelmaking-continuous casting production and heuristic algorithm based on the optimization of “furnace-caster matching” mode. ISIJ Int 60:1213–1224

    Article  Google Scholar 

  11. Yu S, Chai T, Tang Y (2016) An effective heuristic rescheduling method for steelmaking and continuous casting production process with multirefining modes. IEEE Trans Syst Man Cybern Syst 46:1675–1688

    Article  Google Scholar 

  12. Wang S, Shi Y, Liu S (2020) Integrated scheduling for steelmaking continuous casting—hot rolling processes considering hot chain logistics. Math Probl Eng 2020:1–10

    Google Scholar 

  13. Fazel Zarandi M, Dorry F (2018) A hybrid fuzzy PSO algorithm for solving steelmaking–continuous casting scheduling problem. Int J Fuzzy Syst 20:219–235

    Article  Google Scholar 

  14. Tang L, Liu J, Rong A, Yang Z (2000) A mathematical programming model for scheduling steelmaking-continuous casting production. Eur J Oper Res 120:423–435

    Article  Google Scholar 

  15. Bellabdaoui A, Teghem J (2006) A mixed-integer linear programming model for the continuous casting planning. Int J Prod Econ 104:260–270

    Article  Google Scholar 

  16. Tan YY, Huang YL et al (2013) Two-stage mathematical programming approach for steelmaking process scheduling under variable electricity price. J Iron Steel Res Int 20:1–8. https://doi.org/10.1016/S1006-706X(13)60118-1

    Article  Google Scholar 

  17. Kumar V, Kumar S, Tiwari MK, Chan FTS (2006) Auction-based approach to resolve the scheduling problem in the steel making process. Int J Prod Res 44:1503–1522

    Article  Google Scholar 

  18. Atighehchian A, Bijari M, Tarkesh H (2009) A novel hybrid algorithm for scheduling steel-making continuous casting production. Comput Oper Res 36:2450–2461

    Article  Google Scholar 

  19. Yun Y, Jie L, Li Z et al (2014) Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty. Comput Chem Eng 66:165–185

    Article  Google Scholar 

  20. Jiang S, Min L, Hao J, Qian W (2015) A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry. Comput Ind Eng 87:518–531

    Article  Google Scholar 

  21. De Moerloose P, Maenhout B (2023) A two-stage local search heuristic for solving the steelmaking continuous casting scheduling problem with dual shared-resource and blocking constraints. Oper Res 23:2

    Google Scholar 

  22. Jiang S, Zheng Z, Liu M (2017) A multi-stage dynamic soft scheduling algorithm for the uncertain steelmaking-continuous casting scheduling problem. Appl Soft Comput 60:722–736. https://doi.org/10.1016/j.asoc.2017.07.016

    Article  Google Scholar 

  23. Sun L, Luan F, Ying Y, Mao K (2017) Rescheduling optimization of steelmaking-continuous casting process based on the lagrangian heuristic algorithm. J Ind Manag Optim 13:

  24. Sheng-ping YU et al (2012) A rescheduling method for operation time delay disturbance in steelmaking and continuous casting production process—sciencedirect. J Iron Steel Res Int 19:9

    Google Scholar 

  25. Hu X, Yin Y (2013) Cooperative co-evolutionary cuckoo search algorithm for continuous function optimization problems. Pattern Recognit Artif Intell 26:1041–1049

    Google Scholar 

  26. Jawad K, Mahto R, Das A et al (2023) Novel cuckoo search-based metaheuristic approach for deep learning prediction of depression. Appl Sci 13:5322

    Article  Google Scholar 

  27. Ikram RMA, Dehrashid AA, Zhang B et al (2023) A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment. Stoch Environ Res Risk Assess 37:1717–1743

    Article  Google Scholar 

  28. Boveiri HR, Elhoseny M (2020) A-COA: an adaptive cuckoo optimization algorithm for continuous and combinatorial optimization. Neural Comput Appl 32:681–705

    Article  Google Scholar 

  29. Alkhateeb F, Abed-alguni BH, Al-rousan MH (2022) Discrete hybrid cuckoo search and simulated annealing algorithm for solving the job shop scheduling problem. J Supercomput 1–28

  30. Lin C, Cao Z, Zhou M (2022) Learning-based cuckoo search algorithm to schedule a flexible job shop with sequencing flexibility. IEEE Transactions of Cybernetics

  31. Li X, Yin M (2013) A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem. Int J Prod Res 51:4732–4754

    Article  Google Scholar 

  32. Burnwal S, Deb S (2013) Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol 64:951–959

    Article  Google Scholar 

  33. Li X, Guo X, Tang H et al (2023) An improved cuckoo search algorithm for the hybrid flow-shop scheduling problem in sand casting enterprises considering batch processing. Comput Ind Eng 176:108921

    Article  Google Scholar 

  34. Laha D, Behera DK (2014) An improved cuckoo search algorithm for parallel machine scheduling. In: International conference on swarm, evolutionary, and memetic computing. Springer, pp 788–800

  35. Bibiks K, Hu Y-F, Li J-P et al (2018) Improved discrete cuckoo search for the resource-constrained project scheduling problem. Appl Soft Comput 69:493–503

    Article  Google Scholar 

  36. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16

    Article  Google Scholar 

  37. Nguyen TT, Vo DN (2015) Modified cuckoo search algorithm for short-term hydrothermal scheduling. Int J Electr Power Energy Syst 65:271–281

    Article  Google Scholar 

  38. Abiodun OI, Jantan A, Omolara AE et al (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4:e00938

    Article  Google Scholar 

  39. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Article  Google Scholar 

  40. Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174

    Article  Google Scholar 

  41. Yang X-S, Deb S (2017) Cuckoo search: state-of-the-art and opportunities. In: 2017 IEEE 4th international conference on soft computing & machine intelligence (ISCMI). IEEE, pp 55–59

Download references

Funding

This research was funded by the National Natural Science Foundation of China No. 61773107, No.61104004, Joint Fund Project of the National Natural Science Foundation of China No. U1806201, Natural Science Foundation of Shandong Province No. ZR2021ZD12.

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao City, Shandong Province, China

    Haihong Wang, Zhikao Ren, Chen Ye, Tongtong Zhao, Yue Sun & Xiuying Wang

  2. College of Information Science and Engineering, Ocean University of China, Qingdao, Shandong Province, China

    Hui Feng

Authors
  1. Haihong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhikao Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tongtong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yue Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiuying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HW contributed to conception or design of the work, data analysis and interpretation, funding acquisition, critical revision of the article. HF contributed to original draft preparation, data collection. ZR and CY contributed to investigation, data collection. TZ and YS contributed to original draft preparation, software validation. XW contributed to conception or design of the work, methodology, formal analysis, data analysis and interpretation, critical revision of the article.

Corresponding author

Correspondence to Haihong Wang.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare A co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.

Consent for publication

This is to state that the authors give full permission for the publication of the article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Feng, H., Ren, Z. et al. A novel method with constraints embedded into a cuckoo search for steelmaking–continuous casting scheduling. Neural Comput & Applic 36, 2131–2140 (2024). https://doi.org/10.1007/s00521-023-08973-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08973-4

Keywords

Search

Navigation