Skip to main content

Advertisement

SpringerLink
Log in

Assessing feasibility of design constraints for mills in planning and scheduling of cold rolling: A case of steel industry

  • ORIGINAL ARTICLE
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

In the ongoing scenario of Industry 4.0 and automation, every enterprise needs to look forward to digitalizing its ongoing production planning and scheduling methods by employing different platforms available in the market. In early industrialized nations, the transition to the 4th stage of industrialization is now influencing how industrial value is created. The study is focused on India’s stainless steel manufacturing sector. The area of attention is the digitization of continuous planning and scheduling utilizing an automated model as opposed to a continuous system of planning which requires a significant amount of human labor. Consequently, information and technology are used to manage processes and quality. The research article discusses a problem with coil routing in cold rolling and proposes a solution using a multi-criteria decision-making technique called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). The selection criteria considered economic, social, and environmental factors, and the research focuses on planning feasible routing options by considering constraints associated with cold rolling mills. The issue with the current routing process, which is determined by an Enterprise resource planning (ERP) system, is that it does not consider any mill-specific constraints and schedules all orders to the first mill by default, resulting in manual planning. The proposed solution involves creating a real-time data model that can consider the constraints associated with each mill, thus enabling automated routing and optimizing the process. This study focuses on addressing the routing problem in the steel industry, which considers the design constraints associated with various mills. To accomplish this, a multi-criteria decision-making model is employed, which considers the rolled coil’s thickness, grade, and width as design criteria while also comparing mill-related restrictions. As a result, the model generates potential rolling alternatives for the coil. This approach contributes to sustainable steel production by reducing product quality deviations and minimizing the reliance on human planning, thereby promoting automation in the steel industry. Considering the limitations imposed by mills, a workable routing alternative is presented in this paper with the aid of information and technology. It will reduce the steel industry’s reliance on human scheduling. This model is a first step towards rationalizing the scheduling and planning of a product like steel, which has quite a few characteristics.

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

  • Ada N (2022) Sustainable supplier selection in agri-food supply chain management. Int J Math Eng Manage Sci 7(1):115–130

    Google Scholar 

  • Aggarwal S, Srivastava MK (2019) A grey-based DEMATEL model for building collaborative resilience in supply chain. Int J Qual Reliab Manage 36(8):1409–1437

    Article  Google Scholar 

  • Anon. (1988) ‘unacceptable’ energy policies could sap UK steel industry. Steel times 216(2):66

    Google Scholar 

  • Branca TA, Fornai B, Colla V, Murri MM, Streppa E, Schröder AJ (2020) The challenge of digitalization in the steel sector. Metals. https://doi.org/10.3390/met10020288

    Article  Google Scholar 

  • Brankley L, Camci L, Tugrul A, Yap CS, Woolnough A (2020), “Digital transformation for the reinforcing steel supply chain”, Proceedings of the Fib Symposium 2020: Concrete Structures for Resilient Society, pp. 344–350.

  • Chattopadhyay R, Chakraborty S, Chakraborty S (2020) An integrated d-marcos method for supplier selection in an iron and steel industry. Decis Mak Appl Manage Eng 3(2):49–69

    Article  Google Scholar 

  • Chen L, Wang X, Tang L (2014) Operation optimization in the hot-rolling production process. Ind Eng Chem Res 53(28):11393–11410

    Article  Google Scholar 

  • Costa Silva A (2019) Energy Challenges, Technology and New Energy Sources, The Future of Energy: Prospective Scenarios on EU-Russia Relations.

  • Fang S-C, Chen C-H, Yang C-W (2021) Implementing a value co-creation network: some lessons from taiwan’s steel industry. J Bus Bus Mark 28(1):67–79

    Article  Google Scholar 

  • Fazlollahi H, Qayyum O, Howlader J, Khademi A, Gu J (2013) Challenges of integration between MES and ERP systems in the steel industry. Aistech Iron Steel Technol Conf Proc 2:2487–2493

    Google Scholar 

  • Gajdzik B, Wolniak R (2021) Transitioning of steel producers to the steelworks 4.0––literature review with case studies. Energies. https://doi.org/10.3390/en14144109

    Article  Google Scholar 

  • Gefrerer R, Kraft G, Busch S, Winterhalder L, Garbracht K, Wiese T (2000) Supply chain management in the steel industry | supply chain management in der stahlindustrie. Stahl Eisen 120(3):31–36

    Google Scholar 

  • Iannino V, Colla V, Mocci C, Matino I, Dettori S, Kolb S, Plankenbühler T et al (2021) Multi-agent systems to improve efficiency in steelworks. Materiaux Et Tech 109:5–6. https://doi.org/10.1051/mattech/2022010

    Article  Google Scholar 

  • Jafarnejad A, Ansari M, Youshanlouei HR, Mood MM (2012) A hybrid MCDM approach for solving the ERP system selection problem with application to steel industry. Int J Enterp Inf Syst 8(3):54–73

    Article  Google Scholar 

  • Knoop P, Rezig W, Kranendonk H, Staats F (2002) Automated steel ordering optimization at corus. SEAISI Q 31(1):36–46

    Google Scholar 

  • Kumar P, Maiti J (2018) Productivity changes in Indian steel plants: DEA approach. Int J Qual Reliab Manage 35(5):1093–1114

    Article  MathSciNet  Google Scholar 

  • Lu K, Huang K, Wang D (2008) Integrated scheduling system on steel-making and continuous casting with application. IFAC Proc Vol. https://doi.org/10.3182/20080706-5-KR-1001.0332

    Article  Google Scholar 

  • Martensen A, Dahlgaard JJ (1999) Strategy and planning for innovation management - supported by creative and learning organisations. Int J Qual Reliab Manage 16(9):878–891

    Article  Google Scholar 

  • Nakasuka S, Yoshida T (1989) “New framework for dynamic scheduling of production systems. IEEE, Tokyo, pp 253–258

    Google Scholar 

  • Nakasuka S, Yoshida T (1990) Learning-aided dynamic scheduling and its application to routing problem. Proc IEEE Conf Decis Control 3:1612–1617

    Google Scholar 

  • Patra P, Kundu C, Ghosh A, Tudu B, Bandyopadhyay R (2018) Methods for automatic identification of coke ovens for auto positioning systems in coke plant of steel industries. Diagnostyka 19(2):95–103

    Article  Google Scholar 

  • Roy T (2010) IT interventions in the steel industry: bhilai steel plant––a case study. Steel times Int 34(7):43–46

    Google Scholar 

  • Stević Ž, Pamučar D, Puška A, Chatterjee P (2020) Sustainable supplier selection in healthcare industries using a new MCDM method: measurement of alternatives and ranking according to compromise solution (MARCOS). Comput Ind Eng 140:106231

    Article  Google Scholar 

  • Valamede LS, Akkari ACS (2020) Lean 4.0: a new holistic approach for the integration of lean manufacturing tools and digital technologies. Int J Math Eng Manage Sci 5(5):854–868

    Google Scholar 

  • Van den Berg JC (1996) The forces that drive research and development in the steel industry. Ironmaking Steelmaking 23(4):309–316

    Google Scholar 

  • Wang C-N, Nguyen TTT, Dang T-T, Nguyen N-A-T (2022) A hybrid OPA and fuzzy MARCOS methodology for sustainable supplier selection with technology 4.0 evaluation. Processes 10(11):2351. https://doi.org/10.3390/pr10112351

    Article  Google Scholar 

  • Witt A, Voß S (2011) Application of a mathematical model to an intermediate- to long-term real-world steel production planning problem based on standard software. Eur J Ind Eng 5(1):81–100

    Article  Google Scholar 

  • Wu Q, Jiang Y, Xiong T, Peng H (2020) Research and application of the optimal furnace and casting schedule for long steel products in iron and steel enterprises. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1578/1/012171

    Article  Google Scholar 

  • Zecchi M, La Bruna E (2014) Increasing productivity and safety in railroad operations at steel plants by implement railroad management system (RMS). Aistech Iron Steel Technol Conf Proc 3:3799–3808

    Google Scholar 

Download references

Funding

This research was conducted without any external funding.

Author information

Authors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, India

    Rohit Kumar & Devesh Kumar

  2. Graphic Era Deemed to Be University, Dehradun, India

    Bharti Ramtiyal

  3. Jaipuria Institute of Management, Jaipur, India

    Lokesh Vijayvargy

  4. Birla Institute of Technology, Pilani, India

    Surendra Bisht

Authors
  1. Rohit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bharti Ramtiyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lokesh Vijayvargy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Surendra Bisht
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Devesh Kumar.

Ethics declarations

Conflict of interest

The authors state that they do not have any conflicts of interest.

Ethics approval

None of the authors conducted any studies with human participants or animals for this publication.

Informed consent

The submission of the work to the journal has the approval of all authors.

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

Kumar, R., Kumar, D., Ramtiyal, B. et al. Assessing feasibility of design constraints for mills in planning and scheduling of cold rolling: A case of steel industry. Int J Syst Assur Eng Manag 15, 1519–1535 (2024). https://doi.org/10.1007/s13198-023-01982-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-023-01982-5

Keywords

Search

Navigation