Abstract
In many practical supply chain network design (SCND) problems, the critical parameters such as customer demands, transportation costs and resource capacities are quite uncertain. The significance of uncertainty motivates us to develop a new mean-risk fuzzy optimization method for SCND problem, in which the standard semivariance is suggested to gauge the risk resulted from fuzzy uncertainty. To demonstrate the advantages of the proposed optimization method, we define a new concept about the value of fuzzy solution for our SCND problem. When the transportation costs and the demands of customers have continuous possibility distributions, we approximate the continuous fuzzy vector by a sequence of discrete fuzzy vectors. On the basis of the approximation scheme, we obtain an approximating optimization model, which is a nonlinear mixed-integer programming problem. Furthermore, we design a hybrid memetic algorithm (MA) to solve the approximating optimization problem. The designed hybrid MA incorporates the reduced variable neighborhood search to act as the local search procedure. Finally, we conduct some numerical experiments via an application example to demonstrate the effectiveness of the designed hybrid MA.
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References
Aikens, C. H. (1985). Facility location models for distribution planning. European Journal of Operational Research, 22(3), 263–279.
Alonso-Ayuso, A., Escudero, L. F., Garin, A., Ortuno, M. T., & Perez, G. (2003). An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming. Journal of Global Optimization, 26, 97–124.
Bachlaus, M., Pandey, M. K., Mahajan, C., Shankar, R., & Tiwari, M. K. (2008). Designing an integrated multi-echelon agile supply chain network: A hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19(6), 747–761.
Bidhandi, H. M., Yusuff, R. M., Ahmad, M. M. H. M., & Bakar, M. R. A. (2009). Development of a new approach for deterministic supply chain network design. European Journal of Operational Research, 198(1), 121–128.
Cheung, R. K. M., & Powell, W. B. (1996). Models and algorithms for distribution problems with uncertain demands. Transportation Science, 30(1), 43–59.
Cho, D. W., & Lee, Y. H. (2012). Bullwhip effect measure in a seasonal supply chain. Journal of Intelligent Manufacturing, 23(6), 2295–2305.
Cohen, M. A., & Lee, H. L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81–104.
Geoffrion, A. M., & Graves, G. W. (1974). Multi-commodity distribution system design by Benders decomposition. Management Science, 20(5), 822–844.
Geoffrion, A. M., & Powers, R. F. (1995). Twenty years of strategic distribution system design: An evolutionary perspective. Interfaces, 25(5), 105–127.
Georgiadis, M. C., Tsiakis, P., Longinidis, P., & Sofioglou, M. K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254–272.
Giannoccaro, I., Pontrandolfo, P., & Scozzi, B. (2003). A fuzzy echelon approach for inventory management in supply chains. European Journal of Operational Research, 149(1), 185–196.
Jolai, F., Amalnick, M. S., Alinaghian, M., Shakhsi-Niaei, M., & Omrani, H. (2011). A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines. Journal of Intelligent Manufacturing, 22(2), 247–261.
Kubat, C., & Yuce, B. (2012). A hybrid intelligent approach for supply chain management system. Journal of Intelligent Manufacturing, 23(4), 1237–1244.
Lee, H. Y., Kang, A. H. I., & Yang, C. Y. (2012). A fuzzy ANP model for supplier selection as applied to IC packaging. Journal of Intelligent Manufacturing, 23(5), 1477–1488.
Liu, B., & Liu, Y. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445–450.
Liu, Y. (2005). Fuzzy programming with recourse. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 13(4), 381–413.
Liu, Y. (2006). Convergent results about the use of fuzzy simulation in fuzzy optimization problems. IEEE Transactions on Fuzzy Systems, 14(2), 295–304.
Mizgiera, K. J., Wagnera, S. M., & Holystb, J. A. (2012). Modeling defaults of companies in multi-stage supply chain networks. International Journal of Production Economics, 135(1), 14–23.
Petrovic, D., Roy, R., & Petrovic, R. (1999). Supply chain modelling using fuzzy sets. International Journal of Production Economics, 59(1–3), 443–453.
Pishvaee, M. S., Farahani, R. Z., & Dullaret, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse loistics network design. Computers & Operations Research, 37(6), 1100–1112.
Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581–598.
Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96–115.
Sun, G., Liu, Y., & Lan, Y. (2011). Fuzzy two-stage material procurement planning problem. Journal of Intelligent Manufacturing, 22(2), 319–331.
Urselmann, M., Barkmann, S., Sand, G., & Engell, S. (2011). A memetic algorithm for global optimization in chemical process synthesis problems. IEEE Transanctions on Evolutionary Computation, 15(5), 659–683.
Wang, J., & Shu, Y. (2005). Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 150(1), 107–127.
Wong, J. T. (2012). DSS for 3PL provider selection in global supply chain: Combining the multi-objective optimization model with experts’ opinions. Journal of Intelligent Manufacturing, 23(3), 599–614.
Yang, G., Liu, Y., & Yang, K. (2011). Modeling supply chain network design problem with joint service level constraint. Advances in Intelligent and Soft Computing, 123, 311–318.
Yeh, W. C. (2006). An efficient memetic algorithm for the multi-stage supply chain network problem. The International Journal of Advanced Manufacturing Technology, 29(7–8), 803–813.
Zhou, C., Zhao, R., & Tang, W. (2008). Two-echelon supply chain games in a fuzzy environment. Computers & Industrial Engineering, 55(2), 390–405.
Acknowledgments
The authors wish to thank Editors and anonymous referees, whose valuable comments led to an improved version of this paper. This work was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Hebei Province (A2011201007), and the Training Foundation of Hebei Province Talent Engineering.
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Yang, G., Liu, Y. Designing fuzzy supply chain network problem by mean-risk optimization method. J Intell Manuf 26, 447–458 (2015). https://doi.org/10.1007/s10845-013-0801-7
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DOI: https://doi.org/10.1007/s10845-013-0801-7