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 01009
Number of page(s) 8
Section Environmental Engg. & Agro Chemistry
DOI https://doi.org/10.1051/e3sconf/202345501009
Published online 05 December 2023
  1. S. Bikkina, V. Kittu Manda, and U. V. Adinarayana Rao, “Medical oxygen supply during COVID-19: A study with specific reference to State of Andhra Pradesh, India,” Mater. Today Proc., Jan. (2021), doi: 10.1016/j.matpr.2021.01.196. [Google Scholar]
  2. S. T. Zhao, K. Wu, and X.-M. Yuan, “Optimal production-inventory policy for an integrated multi-stage supply chain with time-varying demand,” Eur. J. Oper. Res., vol. 255, no. 2, pp. 364–379, Dec. 2016, doi: 10.1016/j.ejor.2016.04.027. [CrossRef] [Google Scholar]
  3. G. Pérez Lechuga, “Optimal logistics strategy to distribute medicines in clinics and hospitals,” J. Math. Ind., vol. 8, no. 1, p. 2, Dec. (2018), doi: 10.1186/s13362-018-0044-5. [CrossRef] [Google Scholar]
  4. W. Jiang, E. Sha, Q. H.-M. Zhuge, and L. Wu, “Efficient assignment algorithms to minimize operation cost for supply chain networks in agile manufacturing,” Comput. Ind. Eng., vol. 108, pp. 225–239 Jun. (2017), doi: 10.1016/j.cie.2017.04.014. [CrossRef] [Google Scholar]
  5. J. Mula, D. Peidro, M. Díaz-Madroñero, and E. Vicens, “Mathematical programming models for supply chain production and transport planning,” Eur. J. Oper. Res., vol. 204, no. 3, pp. 377–390 Aug. (2010), doi: 10.1016/j.ejor.2009.09.008. [CrossRef] [Google Scholar]
  6. F. Wang, F. Liao, Y. Li, X. Yan, and X. Chen, “An ensemble learning based multi-objective evolutionary algorithm for the dynamic vehicle routing problem with time windows,” Comput. Ind. Eng., vol. 154, p. 107131 Apr. (2021), doi: 10.1016/j.cie.2021.107131. [CrossRef] [Google Scholar]
  7. T. S. Hale, J. M. Day, F. Huq, and N. A. Pujari, “A framework for an integrated distribution system optimisation model,” Int. J. Logist. Syst. Manag., vol. 4, no. 5, p. 506, (2008), doi: 10.1504/IJLSM.2008.017598. [Google Scholar]
  8. Y. Liu, Y. Yuan, J. Shen, and W. Gao, “Emergency response facility location in transportation networks: A literature review,” J. Traffic Transp. Eng. (English Ed., vol. 8, no. 2, pp. 153–169 Apr. (2021), doi: 10.1016/j.jtte.2021.03.001. [Google Scholar]
  9. I. Correia and T. Melo, “A multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment,” Comput. Ind. Eng., vol. 110, pp. 307–321 Aug. (2017), doi: 10.1016/j.cie.2017.06.003. [CrossRef] [Google Scholar]
  10. M. Kchaou Boujelben, C. Gicquel, and M. Minoux, “A MILP model and heuristic approach for facility location under multiple operational constraints,” Comput. Ind. Eng., vol. 98, pp. 446–461 Aug. (2016), doi: 10.1016/j.cie.2016.06.022. [CrossRef] [Google Scholar]
  11. 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), doi: 10.1016/j.ijpe.2018.07.019. [CrossRef] [Google Scholar]
  12. J. Blackhurst, M. J. Rungtusanatham, K. Scheibe, and S. Ambulkar, “Supply chain vulnerability assessment: A network based visualization and clustering analysis approach,” J. Purch. Supply Manag., vol. 24, no. 1, pp. 21–30 Jan. (2018), doi: 10.1016/j.pursup.2017.10.004. [CrossRef] [Google Scholar]
  13. M. Dillon, F. Oliveira, and B. Abbasi, “A two-stage stochastic programming model for inventory management in the blood supply chain,” Int. J. Prod. Econ., vol. 187, pp. 27–41 May (2017), doi: 10.1016/j.ijpe.2017.02.006. [CrossRef] [Google Scholar]
  14. P. Kelle, H. Schneider, and H. Yi, “Decision alternatives between expected cost minimization and worst case scenario in emergency supply - Second revision,” Int. J. Prod. Econ., vol. 157, pp. 250–260 Nov. (2014), doi: 10.1016/j.ijpe.2014.06.009. [CrossRef] [Google Scholar]
  15. L.-C. Kung and W.-H. Liao, “An approximation algorithm for a competitive facility location problem with network effects,” Eur. J. Oper. Res., vol. 267, no. 1, pp. 176–186 May (2018), doi: 10.1016/j.ejor.2017.11.037. [CrossRef] [Google Scholar]
  16. A. Chatzikontidou, P. Longinidis, P. Tsiakis, and M. C. Georgiadis, “Flexible supply chain network design under uncertainty,” Chem. Eng. Res. Des., vol. 128, pp. 290–305 Dec. (2017), doi: 10.1016/j.cherd.2017.10.013. [CrossRef] [Google Scholar]
  17. M. S. Pishvaee, M. Rabbani, and S. A. Torabi, “A robust optimization approach to closed-loop supply chain network design under uncertainty,” Appl. Math. Model., vol. 35, no. 2, pp. 637–649 Feb. (2011), doi: 10.1016/j.apm.2010.07.013. [CrossRef] [Google Scholar]
  18. M. S. Pishvaee and S. A. Torabi, “A possibilistic programming approach for closed-loop supply chain network design under uncertainty,” Fuzzy Sets Syst., vol. 161, no. 20, pp. 2668–2683 Oct. (2010), doi: 10.1016/j.fss.2010.04.010. [CrossRef] [Google Scholar]
  19. H. I. Calvete, C. Galé, J. A. Iranzo, and P. Toth, “A matheuristic for the two-stage fixed-charge transportation problem,” Comput. Oper. Res., vol. 95, pp. 113–122 Jul. (2018), doi: 10.1016/j.cor.2018.03.007. [CrossRef] [Google Scholar]
  20. J. Coenen, R.E.C.M. van der Heijden, and A.C.R. van Riel, “Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions,” J. Clean. Prod., vol. 201, pp. 1–13 Nov. (2018), doi: 10.1016/j.jclepro.2018.07.216. [CrossRef] [Google Scholar]
  21. S. Vimala and S. Prabha, “Fuzzy Transportation Problem through Monalisha’s Approximation Method,” Br. J. Math. Comput. Sci., vol. 17, no. 2, pp. 1–11 Jan. (2016), doi: 10.9734/BJMCS/2016/26097. [CrossRef] [Google Scholar]
  22. S. K. Singh and S. P. Yadav, “A novel approach for solving fully intuitionistic fuzzy transportation problem,” Int. J. Oper. Res., vol. 26, no. 4, p. 460, (2016), doi: 10.1504/IJOR.2016.077684. [CrossRef] [Google Scholar]
  23. H. Badri, S. M. T. Fatemi Ghomi, and T.-H. Hejazi, “A two-stage stochastic programming approach for value-based closed-loop supply chain network design,” Transp. Res. Part E Logist. Transp. Rev., vol. 105, pp. 1–17 Sep. (2017), doi: 10.1016/j.tre.2017.06.012. [CrossRef] [Google Scholar]
  24. R. M. Dom, A. Shuib, and W.N.A.W.A. Fatthi, “A mixed integer programming model for solving realtime truck-to-door assignment and scheduling problem at cross docking warehouse,” J. Ind. Manag. Optim., vol. 12, no. 2, pp. 431–447 Jun. (2015), doi: 10.3934/jimo.2016.12.431. [CrossRef] [Google Scholar]
  25. A. M. Caunhye, Y. Zhang, M. Li, and X. Nie, “A location-routing model for prepositioning and distributing emergency supplies,” Transp. Res. Part E Logist. Transp. Rev., vol. 90, pp. 161–176 Jun. (2016), doi: 10.1016/j.tre.2015.10.011. [CrossRef] [Google Scholar]

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