Refrences:
1.Merkuryev, Y., Merkuryeva, G., Piera, M., and Guasch, A., Supply Chain Dynamics, Springer, London (2009).
2. Vieira, G.E. Ideas for modeling and simulation of supply chains with Arena", Proceedings of the 2004 Winter Simulation Conference, 2, pp. 1418-1427 (2004).
3. Vieira, G.E. and Junior, O.C. A conceptual model for the creation of supply chain simulation models", Proceedings of the 2005 Winter Simulation Conference, pp. 2619-2627 (2005).
4. Tabrizi, M.M., Karimi, B., and Mirhassani, S. A novel two-stage stochastic model for supply chain network design under uncertainty", Scientia Iranica, Transactions E, Industrial Engineering, 23(6), p. 3046 (2016). 5. Mahmoodi, A., Haji, A., and Haji, R. A twoechelon inventory model with perishable items and lost sales", Scientia Iranica, Transactions E, Industrial Engineering, 23(5), p. 2277 (2016). 6. Badri, H., Fatemi Ghomi, S.M.T., and Hejazi, T.-H. A two-stage stochastic programming model for valuebased supply chain network design", Scientia Iranica, Transactions E, Industrial Engineering, 23(1), p. 348 (2016). 7. Rooeinfar, R., Azimi, P., and Pourvaziri, H. Multiechelon supply chain network modelling and optimization via simulation and metaheuristic algorithms", Scientia Iranica, Transactions E, Industrial Engineering, 23(1), p. 330 (2016). 8. Tsai, S.C. and Chen, S.T. A simulation-based multiobjective optimization framework: A case study on inventory management", Omega, 70, pp. 148-159 (2017). 9. Ye, W. and You, F. A computationally e_cient simulation-based optimization method with regionwise surrogate modeling for stochastic inventory management of supply chains with general network structures", Computers & Chemical Engineering, 87, pp. 164-179 (2016). 10. Keyvanshokooh, E., Ryan, S.M., and Kabir, E. Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition", European Journal of Operational Research, 249(1), pp. 76-92 (2016). 11. Chu, Y., You, F., Wassick, J.M., and Agarwal, A. Simulation-based optimization framework for multiechelon inventory systems under uncertainty", Computers & Chemical Engineering, 73, pp. 1-16 (2015). 12. Shi, W., Liu, Z., Shang, J., and Cui, Y. Multicriteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain", European Journal of Operational Research, 229(3), pp. 695-706 (2013). 13. Hussain, M., Drake, P.R., and Lee, D.M. Quantifying the impact of a supply chain's design parameters on the bullwhip e_ect", International Journal of Physicial Distribution & Logistics Management, 42(10), pp. 947- 968 (2012). 14. Yang, T., Wen, Y.-F., and Wang, F.-F. Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method", International Journal of Production Economics, 134(2), pp. 458-466 (2011). 15. Kumar, S., Chandra, C., and Seppanen, M.S. Demonstrating supply chain parameter optimization through beer game simulation", Information- Knowledge-Systems Management, 6(4), pp. 291-322 (2007). 16. Shang, J.S., Li, S., and Tadikamalla, P. Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization", International Journal of Production Research , 42(18), pp. 3823-3849 (2004). 17. Shi, W., Shang, J., Liu, Z., and Zuo, X. Optimal design of the auto parts supply chain for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology", European Journal of Operational Research, 236(2), pp. 664-676 (2014). 18. Nair, V.N., Abraham, B., MacKay, J., et al. Taguchi's parameter design: a panel discussion", Technometrics, 34(2), pp. 127-161 (1992). 19. Robinson, T.J., Borror, C.M., and Myers, R.H. Robust parameter design: a review", Quality and Reliability Engineering International, 20(1), pp. 81-101 (2004). 20. Montgomery, D.C., Design and Analysis of Experiments, John Wiley & Sons (2008). 21. Box, G.E.P. and Wilson, K.B. On the experimental attainment of optimum conditions", Journal of the Royal Statistical Society: Series B, 13(1), pp. 1-38 (1951). L.N. Tang et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 2971{2987 2987 22. Myers, R.H. and Montgomery, D.C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley Series in Probability and Statistics, New York (1995). 23. Dellino, G., Kleijnen, J.P., and Meloni, C. Robust optimization in simulation: Taguchi and response surface methodology", International Journal of Production Economics, 125(1), pp. 52-59 (2010). 24. Tong, L.I., Su, C.T., and Wang, C.H. The optimization of multi-response problems in the Taguchi method", International Journal of Quality & Reliability Management, 14(4), pp. 367-380 (1997). 25. Shukla, S.K., Tiwari, M., Wan, H.-D., and Shankar, R. Optimization of the supply chain network: Simulation, Taguchi, and psychoclonal algorithm embedded approach", Computers & Industrial Engineering, 58(1), pp. 29-39 (2010). 26. Tiwari, M.K., Raghavendra, N., Agrawal, S., and Goyal, S. A hybrid Taguchi-Immune approach to optimize an integrated supply chain design problem with multiple shipping", European Journal of Operational Research, 203(1), pp. 95-106 (2010). 27. Li, Y.C. and Liao, H.C. The optimal parameter design for a blood supply chain system by the Taguchi method", International Journal of Innovative Computing, Information and Control, 8(11), pp. 7697-7712 (2012). 28. Kelton, D. and Sturrock, D., Simulation with Arena, 6th Ed., McGraw-Hill, New York (2015). 29. Soleimani, H. and Kannan, G. A hybrid particle swarm optimization and genetic algorithm for closedloop supply chain network design in large-scale networks", Applied Mathematical Modelling, 39(14), pp. 3990-4012 (2015). 30. Osorio, A.F., Brailsford, S.C., Smith, H.K., Forero-Matiz, S.P., and Camacho-Rodriguez, B.A. Simulation-optimization model for production planning in the blood supply chain", Health Care Management Science, 20(4), pp. 548-564 (2017). 31. Liu, Y.S., Tang, L.N., Ma, Y.Z., and Yang, T. TFT-LCD module cell layout design using simulation and fuzzy multiple attribute group decision-making approach", Applied Soft Computing, 68, pp. 873-888 (2018).
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