Skip to main content
SpringerLink
Log in

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

  • Original Article
  • Published:
Transactions of the Indian Institute of Metals Aims and scope Submit manuscript

Abstract

Integrated steelmaking operations, from ironmaking to final steel categories/forms, are a complex industrial system. Decision-makers, in addition to the domain knowledge, need to understand many facets related to operations management, process execution, involved risk factors and material handling aspects, etc. The solution approach must address trade-offs, uncertainties, multiple stages, multiple objectives, many decision variables, and constraints. Literature has presented several Industrial Engineering approaches like multi-objective optimisation, heuristics, evolutionary algorithm, Simulations, lean six-sigma, etc., to address the iron and steel industry's complex problems like integrated production planning. The manuscript reviews recent application of these IE tools/solution approaches in iron and steel industries to enhanced productivity/competitiveness. A Systematic Literature Review (SLR) methodology has been adopted. The review will help the practitioners and academicians to know the different problem areas about iron and steel industries discussed in the literature, and the solution approaches used to solve the problem.

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. IISE Body of Knowledge, available at https://www.iise.org/details.aspx?id=43631, visited in October 2020.

  2. IISE Management Engineering, Industrial and Systems Engineering, available at https://www.iise.org/SHS/details.aspx?id=18280, visited October 2020.

  3. Steel sector achievement report, (2017). Department of Industrial Policy and Promotion, Ministry of Steel, available at https://drive.google.com/file/d/0B-Tv7_upCKANLVp6N0N3czhYa00/view, visited in October 2020.

  4. Steel industry in China - statistics and facts, 2020, available at https://www.statista.com/topics/5695/steel-industry-in-china/, visited in October 2020.

  5. Benjamin Bartholomew, (2019). THE STEEL INDUSTRY AND ITS PLACE IN THE AMERICAN ECONOMY, AUGUST 2019 available at, https://www.bdo.com/insights/business-financial-advisory/valuation-business-analytics/the-steel-industry-and-its-place-in-the-american-e, visited in October 2020.

  6. KAWASAKI STEEL 21st Century Foundation, Introduction of Iron and steel Processing. http://www.jfe-21st-cf.or.jp/index2.html. visited January 2021.

  7. Editorial, (2019). The Emergence of Digital Platforms: A Conceptual Platform Architecture and impact on Industrial Engineering, Computers & Industrial Engineering, 136:546-555.

    Article  Google Scholar 

  8. Thomé AMT, Scavarda LF & Scavarda AJ, (2016). Conducting systematic literature review in operations management, Production Planning & Control, 27:408-420.

    Article  Google Scholar 

  9. Garza-Reyes JA, Al-Balushi M, Antony J, and Kumar V, (2016). A Lean Six Sigma framework for the reduction of ship loading commercial time in the iron ore pelletising industry, Production Planning & Control, 27: 1092-1111.

    Article  Google Scholar 

  10. Li F, Chen ZL, and Tang L, (2017). Integrated Production, Inventory and Delivery Problems: Complexity and Algorithms, INFORMS Journal on Computing, 29: 232-250.

    Article  Google Scholar 

  11. Dutta G, Fourer R, (2001). A Survey of Mathematical Programming Applications in Integrated Steel Plants, Manufacturing & Service Operations Management, 3:387-400.

    Article  Google Scholar 

  12. Zhang T, Zheng QP, Fang Y, and Zhang Y, (2015). Multi-level inventory matching and order planning under the hybrid Make-To-Order/Make-To-Stock production environment for steel plants via Particle Swarm Optimization, Computers & Industrial Engineering, 87: 238-249.

    Article  Google Scholar 

  13. Lin J, Liu M, Hao J, and Jiang S, (2016). A multi-objective optimisation approach for integrated production planning under interval uncertainties in the steel industry, Computers & Operations Research, 72:189-203.

    Article  Google Scholar 

  14. Dong Y, and Zhao R, (2018). Solve train stowage planning problem of steel coil using a pointer-based discrete differential evolution, International Journal of Production Research, 56:6937-6955.

    Article  Google Scholar 

  15. Dong Y, and Zhao R, (2019). Solving steel coil ship stowage-planning problem using hybrid differential evolution, International Journal of Production Research, 57: 5767 -5786.

    Article  Google Scholar 

  16. Dong Y, Zhao R, Xu W, Yang M, and Jiang W, (2020). Integrated optimisation of consolidation and stowage planning of steel coil ships using differential evolution, International Journal of Production Research, https://doi.org/10.1080/00207543.2020.1722323.

    Article  Google Scholar 

  17. Tan Y, Zhou M, Wang Y, Guo X, and Qi L, (2019). A Hybrid MIP–CP Approach to Multistage Scheduling Problem in Continuous Casting and Hot-Rolling Processes, IEEE Transactions on Automation Science and Engineering, 16:1860-1869.

    Article  Google Scholar 

  18. Guo Q, and Tang L, (2019). Modelling and discrete differential evolution algorithm for order rescheduling problem in steel industry, Computers & Industrial Engineering, 130:586-596.

    Article  Google Scholar 

  19. Pan QK, (2016). An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling, European Journal of Operational Research, 250:702-714.

    Article  Google Scholar 

  20. Mao K, Pan QK, Chai T, and Luh PB, (2015). An Effective Subgradient Method for Scheduling a Steelmaking-Continuous Casting Process, IEEE Transactions on Automation Science and Engineering, 12:1140-1152.

    Article  Google Scholar 

  21. Li C, Qin J, Li J, and Hou Q, (2016). The accident early warning system for iron and steel enterprises based on combination weighting and Grey Prediction Model GM (1, 1), Safety Science, 89:19-27.

    Article  Google Scholar 

  22. Long J, Zheng Z, Gao X, and Pardalos PM, (2016). A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants, Journal of the Operational Research Society, 67:1184–1199.

    Article  Google Scholar 

  23. Zheng Z, Long JY, and Gao XQ, (2017). Production scheduling problems of steelmaking-continuous casting process in dynamic production environment, Journal of Iron and Steel Research International, 24:586-594.

    Article  Google Scholar 

  24. Hao J, Liu M, Jiang S, and Wu C, (2015). A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting process, European Journal of Operational Research, 244:966-979.

    Article  Google Scholar 

  25. Kano M, Nakagawa Y, (2008). Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry, Computers and Chemical Engineering, 32:12-24.

    Article  CAS  Google Scholar 

  26. Prinz K, Steinboeck A, Müller M, Ettl A, Schausberger F, and Kugi A, (2019). Online Parameter Estimation for Adaptive Feedforward Control of the Strip Thickness in a Hot Strip Rolling Mill, Journal of Manufacturing Science and Engineering, 141:071005. https://doi.org/https://doi.org/10.1115/1.4043575.

    Article  Google Scholar 

  27. Shahin A, Labib A, Emami S, and Karbasian M, (2019). Improving Decision-Making Grid based on interdependence among failures with a case study in the steel industry, The TQM Journal, 31:167-182.

    Article  Google Scholar 

  28. Mohanty I, Sarkar S, Jha B, Das S & Kumar R, (2014). Online mechanical property prediction system for hot rolled IF steel, Ironmaking & Steelmaking, 41(8):618-627, https://doi.org/10.1179/1743281214Y.0000000178.

    Article  CAS  Google Scholar 

  29. Jena N, Seth N, (2016). Factors influencing logistics cost and service quality: a survey within the Indian steel sector, Industrial and Commercial Training, 48:199-207. https://doi.org/10.1108/ICT-09-2015-0057.

    Article  Google Scholar 

  30. Ge P, Meng Y, Liu J, Tang L, and Zhao R, (2020). Logistics optimisation of slab pre-marshalling problem in steel industry, International Journal of Production Research, 58: 4050-4070.

    Article  Google Scholar 

  31. Nordlöf H, Wiitavaara B, Winblad U, Wijk K, and Westerling R, (2015). Safety culture and reasons for risk-taking at a large steel-manufacturing company: Investigating the worker perspective, Safety Science, 73:126-135.

    Article  Google Scholar 

  32. Koivupalo M, Sulasalmi M, Rodrigo P, and Väyrynen S, (2015). Health and safety management in a changing organisation: Case study global steel company, Safety Science, 74:28-139.

    Article  Google Scholar 

  33. International Labour Organization, 2005, https://www.ilo.org/global/publications/books/WCMS_081882/lang--en/index.htm.(visited February 2021)

  34. National Security Council (NSC), 1947. Milestones: 1945–1952 - Office of the Historian (state.gov).

  35. Nazaripour E, Halvani G, Jahangiri M, Fallahzadeh H, and Mohammadzadeh M, (2018). Safety performance evaluation in a steel industry: A short-term time series approach, Safety Science, 110:285-290.

    Article  Google Scholar 

  36. Waters T, Putz-Anderson V, Garg A, Fine L, (1993). Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics, 36:749-776.

    Article  CAS  Google Scholar 

  37. Hignett S and McAtamney L, (2000). Rapid Entire Body Assessment (REBA). Applied Ergonomics, 31:201-205. https://doi.org/10.1016/S0003-6870(99)00039-3.

    Article  CAS  Google Scholar 

  38. McAtamney L and Corlett NE, (1993). RULA: a survey method for the investigation of work-related upper limb disorders. Appl Ergon. 24:91-9. https://doi.org/10.1016/0003-6870(93)90080-s.

    Article  CAS  Google Scholar 

  39. Liu J, An R, Xiao R, Yang Y, Wang G, and Wang Q, (2017). Implications from substance flow analysis, supply chain and supplier’ risk evaluation in iron and steel industry in Mainland China, Resources Policy, 51:272-282.

    Article  Google Scholar 

  40. Jain N, and Singh AR, (2020). Sustainable supplier selection criteria classification for Indian iron and steel industry: a fuzzy modified Kano model approach, International Journal of Sustainable Engineering, 13:17-32.

    Article  Google Scholar 

  41. Pena JG, de Oliveira Junior VB, and Salles JL, (2019). Optimal scheduling of a by-product gas supply system in the iron- and steel-making process under uncertainties Computers & Chemical Engineering, 125:351-364.

    Article  CAS  Google Scholar 

  42. Mehmanpazir F, Khalili-Damghani K, and Hafezalkotob A, (2019). Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran), Resources Policy, 63:101409.

    Article  Google Scholar 

  43. Azimifard A, Moosavirad SH, and Ariafar S, (2018). Selecting sustainable supplier countries for Iran's steel industry at three levels by using AHP and TOPSIS methods, Resources Policy, 57:30-44.

    Article  Google Scholar 

  44. Daoutidis, P., Lee, J.H., Harjunkoski, I., Skogestad, S., Baldea, M. and Georgakis, C. (2018). Integrating operations and control: A perspective and roadmap for future research. Computers & Chemical Engineering, 115:179-184.

    Article  CAS  Google Scholar 

  45. Ghanbari H, Pettersson F, Saxén H, (2015). Optimal operation strategy and gas utilisation in a future integrated steel plant, Chemical Engineering Research and Design, 102:322-336.

    Article  CAS  Google Scholar 

  46. Ravi B, (2016). SMART Foundry 2020, IEEE POTENTIALS 0278–6648/16©2016 IEEE.

  47. Yokogawa, Industrial Automation and Test and Measurement solutions. https://www.yokogawa.com/in/library/resources/application-notes/instruments-andsolution-for-iron-steel-industry-pdf/ (visited on February 09, 2021).

  48. SICK sensor technology, (2021) https://sickusablog.com/optimizing-manufacturing-processes-steel-industry/ (visited on February 09, 2021).

  49. Neef C, Hirzel S, and Arens M, (2018). Industry 4.0 in the European Iron and Steel Industry: Towards an Overview of Implementations and Perspectives, Fraunhofer Institute for Systems and Innovation Research ISI Karlsruhe, Germany.

  50. Miśkiewicz R, and Wolniak R, (2020). Practical Application of the Industry 4.0 Concept in a Steel Company, Sustainability, 12:5776, https://doi.org/10.3390/su12145776.

  51. Casado S, Laguna M, Pacheco J, and Puche JC, (2020). Grouping products for the optimisation of production processes: A case in the steel manufacturing industry, European Journal of Operational Research, 286:190-202.

    Article  Google Scholar 

  52. Özgür A, Uygun Y, and Hütt MT, (2020). A review of planning and scheduling methods for hot rolling mills in steel production, Computers & Industrial Engineering (in press). https://doi.org/10.1016/j.cie.2020.106606.

    Article  Google Scholar 

  53. Fukuyama H, Liu HH, Song YY, and Yang GL, (2021). Measuring the capacity utilisation of the 48 largest iron and steel enterprises in China, European Journal of Operational Research, 288: 648-665.

    Article  Google Scholar 

  54. Tang L, Meng Y, Chen ZL, and Liu J, (2016). Coil Batching to Improve Productivity and Energy Utilization in Steel Production, Manufacturing & Service Operations Management 18:262-279.

    Article  Google Scholar 

  55. Lin J, Liu M, Hao J, and Gu P, (2017). Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants, International Journal of Production Research, 55:4003-4020.

    Article  Google Scholar 

  56. Wang Y, Chen C, Tao Y, Wen Z, Chen B, and Zhang H, (2019). A many-objective optimisation of industrial environmental management using NSGA-III: A case of China's iron and steel industry, Applied Energy, 242:46-56.

    Article  Google Scholar 

  57. Fanti MP, Rotunno G, Stecco G, Ukovich W, and Mininel S, (2016). An Integrated System for Production Scheduling in Steelmaking and Casting Plants, IEEE Transactions on Automation Science and Engineering, 13: 1112 - 1128.

    Article  Google Scholar 

  58. Browne F, Chiu GT, and Jain N, (2019). A Nonlinear Dynamic Switched-Mode Model of Twin-Roll Steel Strip Casting, Journal of Dynamic Systems, Measurement, and Control, 141: 081004 (10 pages).

  59. Zhao XC, Bai H, Lu X, Shi Q, and Han J, (2015). A MILP model concerning the optimisation of penalty factors for the short-term distribution of byproduct gases produced in the iron and steel making process. Applied Energy, 148:142-158.

    Article  CAS  Google Scholar 

  60. Maddaloni A, Porzio GF, Nastasi G, Colla V, and Branca TA, (2015). Multi-objective optimisation applied to retrofit analysis: A case study for the iron and steel industry, Applied Thermal Engineering, 91:638-646.

    Article  CAS  Google Scholar 

  61. Mele M, and Magazzino C, (2020). A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China, Journal of Cleaner Production, 277:123293. https://doi.org/10.1016/j.jclepro.2020.123293.

    Article  CAS  Google Scholar 

  62. Nielsen H, (2017). Productive efficiency in the iron and steel sector under state planning: The case of China and former Czechoslovakia in a comparative perspective, Applied Energy, 185: 1732-1743.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Industrial Engineering, Mumbai, India

    Milind Akarte, Vivek Khanzode, Rauf Iqbal & Manoj Kumar Tiwari

Authors
  1. Milind Akarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Khanzode
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rauf Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manoj Kumar Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Kumar Tiwari.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akarte, M., Khanzode, V., Iqbal, R. et al. IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review. Trans Indian Inst Met 74, 1065–1076 (2021). https://doi.org/10.1007/s12666-021-02228-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12666-021-02228-3

Keywords

Search

Navigation