Open Access
Issue
E3S Web Conf.
Volume 455, 2023
First International Conference on Green Energy, Environmental Engineering and Sustainable Technologies 2023 (ICGEST 2023)
Article Number 03021
Number of page(s) 10
Section Sustainable Technology in Construction
DOI https://doi.org/10.1051/e3sconf/202345503021
Published online 05 December 2023
  1. PWC, “The Indian steel industry: Growth, challenges and digital disruption,” (2019). [Online]. Available: https://www.pwc.in/assets/pdfs/consulting/technology/the-indian-steel-industry-growth-challenges-and-digital-disruption.pdf. [Google Scholar]
  2. LSC, “Overview On Logistics Industry,” Logistics Skill Councial, (2021). https://lsc-india.com/content/overview_on_logistics_industry. [Google Scholar]
  3. A. Bhardwaj et al., “Goods On The Moove Efficiency And Sustainability In Indian Logistics,” (2018). [Online]. Available: https://niti.gov.in/writereaddata/files/document_publication/Freight_report.pdf. [Google Scholar]
  4. T.-K. Liu, S.-S. Lin, and P.-W. Hsueh, “Optimal design for transport and logistics of steel mill by-product based on double-layer genetic algorithms,” J. Low Freq. Noise, Vib. Act. Control, vol. 40, no. 1, pp. 555–576, Mar. (2021) [CrossRef] [Google Scholar]
  5. A. Trivedi and A. Singh, “A hybrid multi-objective decision model for emergency shelter location-relocation projects using fuzzy analytic hierarchy process and goal programming approach,” Int. J. Proj. Manag., vol. 35, no. 5, pp. 827–840, Jul. (2017) [CrossRef] [Google Scholar]
  6. G. Xiong and P. Helo, “Challenges to the supply chain in the steel industry,” Int. J. Logist. Econ. Glob., vol. 1, no. 2, p. 160, (2008) [Google Scholar]
  7. A. Sabzevari Zadeh, R. Sahraeian, and S. M. Homayouni, “A dynamic multicommodity inventory and facility location problem in steel supply chain network design,” Int. J. Adv. Manuf. Technol., vol. 70, no. 5-8, pp. 1267–1282, Feb.( 2014) [CrossRef] [Google Scholar]
  8. M. Pourmehdi, M. M. Paydar, and E. Asadi-Gangraj, “Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology,” J. Clean. Prod., vol. 277, p. 123298, Dec. (2020), [CrossRef] [Google Scholar]
  9. A. Potter, R. Mason, M. Naim, and C. Lalwani, “The evolution towards an integrated steel supply chain: A case study from the UK,” Int. J. Prod. Econ., vol. 89, no. 2, pp. 207–216, May (2004) [CrossRef] [Google Scholar]
  10. P. Jula and R. C. Leachman, “A supply-chain optimization model of the allocation of containerized imports from Asia to the United States,” Transp. Res. Part E Logist. Transp. Rev., vol. 47, no. 5, pp. 609–622, Sep.( 2011). [CrossRef] [Google Scholar]
  11. E. Perea-López, B. E. Ydstie, and I. E. Grossmann, “A model predictive control strategy for supply chain optimization,” Comput. Chem. Eng., vol. 27, no. 8-9, pp. 1201–1218, Sep. (2003) [CrossRef] [Google Scholar]
  12. W. B. E. Al-Othman, H. M. S. Lababidi, I. M. Alatiqi, and K. Al-Shayji, “Supply chain optimization of petroleum organization under uncertainty in market demands and prices,” Eur. J. Oper. Res., vol. 189, no. 3, pp. 822–840, Sep. (2008). [CrossRef] [Google Scholar]
  13. S. M. J. Mirzapour Al-e-hashem, H. Malekly, and M. B. Aryanezhad, “A multiobjective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty,” Int. J. Prod. Econ., vol. 134, no. 1, pp. 28–42, Nov.( 2011) [CrossRef] [Google Scholar]
  14. J.-K. Bok, I. E. Grossmann, and S. Park, “Supply Chain Optimization in Continuous Flexible Process Networks,” Ind. Eng. Chem. Res., vol. 39, no. 5, pp. 1279–1290, May 2000, DOI: 10.1021/ie990526w. [CrossRef] [Google Scholar]
  15. D. Kaur, S. Mukherjee, and K. Basu, “Solution of a Multi-Objective and Multi-Index Real-Life Transportation Problem Using Different Fuzzy Membership Functions,” J. Optim. Theory Appl., vol. 164, no. 2, pp. 666–678, Feb. (2015) [CrossRef] [MathSciNet] [Google Scholar]
  16. P. Singh and P. K. Saxena, “The multiple objective time transportation problem with additional restrictions,” Eur. J. Oper. Res., vol. 146, no. 3, pp. 460–476, May( 2003) [CrossRef] [Google Scholar]
  17. R. Askerbeylİ, “Study Of Transportation Problem Of Iron And Steel Industry In Turkey Based On Linear Programming, VAM And MODI Methods,” Commun. Fac. Sci. Univ. Ankara Ser. A2-A3 Phys. Sci. Eng., pp. 62 (1), 79–99, (2020) [Google Scholar]
  18. J. Hong, A. Diabat, V. V. Panicker, and S. Rajagopalan, “A two-stage supply chain problem with fixed costs: An ant colony optimization approach,” Int. J. Prod. Econ., vol. 204, pp. 214–226, Oct. (2018), [CrossRef] [Google Scholar]
  19. S. R. Yadav, R. R. M. R. Muddada, M. K. Tiwari, and R. Shankar, “An algorithm portfolio based solution methodology to solve a supply chain optimization problem,” Expert Syst. Appl., vol. 36, no. 4, pp. 8407–8420, May (2009) [CrossRef] [Google Scholar]
  20. O. Cosma, P. C. Pop, and C. Sabo, “An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs,” Mathematics, vol. 8, no. 5, p. 712, May (2020) [CrossRef] [Google Scholar]
  21. F. Goodarzian, D. Shishebori, H. Nasseri, and F. Dadvar, “A bi-objective productiondistribution problem in a supply chain network under grey flexible conditions,” RAIRO - Oper. Res., vol. 55, pp. S1287–S1316, Mar. (2021) [CrossRef] [EDP Sciences] [Google Scholar]
  22. C. S. Grewal, S. T. Enns, and P. Rogers, “Dynamic reorder point replenishment strategies for a capacitated supply chain with seasonal demand,” Comput. Ind. Eng., vol. 80, pp. 97–110, Feb. (2015) [CrossRef] [Google Scholar]
  23. M. A. Sandhu, P. Helo, and Y. Kristianto, “Steel supply chain management by simulation modelling,” Benchmarking An Int. J., vol. 20, no. 1, pp. 45–61, Feb.( 2013) [CrossRef] [Google Scholar]
  24. G. Zäpfel and M. Wasner, “Warehouse sequencing in the steel supply chain as a generalized job shop model,” Int. J. Prod. Econ., vol. 104, no. 2, pp. 482–501, Dec. (2006) [CrossRef] [Google Scholar]
  25. A. Ghodratnama, R. Tavakkoli-Moghaddam, and A. Azaron, “Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub locationallocation problem: A supply chain overview,” Appl. Soft Comput., vol. 37, pp. 255276, Dec. (2015) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.