توسعه یک رویکرد برنامه‌ریزی فازی استوار برای طراحی زنجیره تأمین حلقه‌بسته

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، پردیس فارابی دانشگاه تهران.

2 استاد، دانشگاه تربیت مدرس.

3 استاد، پردیس فارابی دانشگاه تهران.

چکیده

طی یک دهه گذشته با توجه به افزایش اهمیت رقابت‌پذیری اقتصادی و نگرانی‌های زیست‌محیطی در زمینه محصولات فرسوده، موضوع زنجیره تأمین حلقه‌بسته موردتوجه پژوهشگران قرار گرفته است. پژوهش حاضر درصدد توسعه یک رویکرد برنامه‌ریزی تصادفی فازی استوار با استفاده از مفاهیم برنامه‌ریزی با محدودیت‌های اعتبار و میانگین انحراف مطلق برای طراحی شبکه زنجیره تأمین حلقه‌بسته تحت شرایط عدم‌قطعیت ترکیبی است. در مدل پیشنهادی فرض می‌شود که هزینه ثابت احداث مراکز تولیدی به‌صورت غیرخطی و تابعی از سطح ظرفیت است. این مدل با استفاده از یک تکنیک خطی‌سازی به یک مدل خطی معادل تبدیل می‌شود. در این مدل دو منبع عدم‌قطعیت برای برخی پارامترها وجود دارد. نخستین منبع از تصادفی‌بودن پارامترها ناشی می‌شود که با سناریوهای آتی بیان می‌شود. دومین منبع از عدم‌قطعیت شناختی در پارامترهای هر سناریو ناشی می‌شود؛ به‌طوری‌که می‌توان آن‌ها را با یک توزیع امکانی مشخص کرد. عملکرد مدل پیشنهادی برحسب انحراف استاندار و هزینه با مدل‌های استوار دیگر مقایسه شد. نتایج نشان می‌دهد که مدل پیشنهادی قادر است با صرف یک هزینه پایین استواری مدل را بهبود بخشد.

کلیدواژه‌ها


1. Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129-138.

2. Beale, E. M. L., & Tomlin, J. A. (1970). Special facilities in a general mathematical programming system for non-convex problems using ordered sets of variables in Lawrence, J (ed.) Proceedings of the Fifth Intenational Conference on Operations Research 447454.

3. Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199-215.

4. Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2016). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega.

5. Carlsson, C., & Fullér, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy sets and systems, 122(2), 315-326.

6. Chouinard, M., D’Amours, S., & Aït-Kadi, D. (2008). A stochastic programming approach for designing supply loops. International Journal of Production Economics, 113(2), 657-677.

7. Cruz-Rivera, R., & Ertel, J. (2009). Reverse logistics network design for the collection of end-of-life vehicles in Mexico. European Journal of Operational Research, 196(3), 930-939.

8. Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy Sets and Systems, 3(1), 37-48.

9. Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3), 594-615.

10. El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423-431.

11. Govindan, K., & Fattahi, M. (2015). Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain. International Journal of Production Economics.

12. Hwang, C. M. (2000). A theorem of renewal process for fuzzy random variables and its application. Fuzzy Sets and Systems, 116(2), 237-244.

13. Hasani, A., & Hosseini, S.M.H., (2014). A Comprehensive Robust Bi-objective Model and a Memetic Solution Algorithm for Designing Reverse Supply. Journal of Indusrial Mangement Perspective, 16, 31-54 (In Persion).

14. Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630-2647.

15. Horri, M.S., & Anjomshoa, A. (2016). Multi-objective mathematical model for supplier selection and order allocation under multi-Item condition. Journal of Industrial Management Perspective, 6(21), 41-51 (In Persion).

16. Inuiguchi, M., Ichihashi, H., & Tanaka, H. (1990). Fuzzy programming: a survey of recent developments. In Stochastic versus fuzzy approaches to multiobjective mathematical programming under uncertainty (pp. 45-68). Springer Netherlands.

17. Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.

18. Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346-366.

19. Kasperski, A., & Kulej, M. (2009). Choosing robust solutions in discrete optimization problems with fuzzy costs. Fuzzy Sets and Systems, 160(5), 667-682.

20. Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3), 644-658.

21. Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European Journal of Operational Research, 249(1), 76-92.

22. Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.

23. Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. Fuzzy Systems, IEEE Transactions on, 10(4), 445-450.

24. Liu, B. Uncertainty Theory: An Introduction to its Axiomatic Foundations. 2004.

25. Luhandjula, M. K. (2004). Optimisation under hybrid uncertainty. Fuzzy Sets and Systems, 146(2), 187-203.

26. Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 181(1), 224-238.

27. Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of operations management, 25(6), 1075-1082.

28. Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61-71.

29. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.

30. Min, H., & Ko, H. J. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176-192.

31. Mirakhorli, A. (2014). Fuzzy multi-objective optimization for closed loop logistics network design in bread-producing industries. The International Journal of Advanced Manufacturing Technology, 70(1-4), 349-362.

32. Mohammadi, M., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2014). Sustainable hub location under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 62, 89-115.

33. Peidro, D., Mula, J., Poler, R., & Verdegay, J. L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657.

34. Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37(4), 668-683.

35. Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.

36. Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.

37. Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.

38. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.

39. Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40(1), 407-418.

40. Rabieh, M., Azar, A., Modarres, M., & Fetanat, M., (2011). Mathematical Modeling for Multi Objective Robust Sourcing Problem: An Approach in Reduction of Supply Chain Risk (Case study: IKCO Supply Chain). Journal of Indusrial Mangement Perspective, 1, 57-77. (In Persion)

41. Rabieh, M., & Fadaei, A., (2015). Fuzzy Robust Mathematical Model for Project Portfolio Selection and its Solving through Multi Objective Differential Evolutionary Algorithm. Journal of Indusrial Mangement Perspective, 19, 65-90 (In Persion).

42. Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5-8), 825-843.

43. Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2010). Simultaneous design and planning of supply chains with reverse flows: a generic modelling framework. European Journal of Operational Research, 203(2), 336-349.

44. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488.

45. Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214.

46. Tabrizi, B. H., & Razmi, J. (2013). Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks. Journal of Manufacturing Systems, 32(2), 295-307.

47. Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2015). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 1-30.

48. Üster, H., Easwaran, G., Akçali, E., & Cetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model. Naval Research Logistics (NRL), 54(8), 890-907.

49. Wang, R. C., & Liang, T. F. (2005). Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328-341.

50. Wang, H. F., & Hsu, H. W. (2010). A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & operations research, 37(2), 376-389.

51. Winkler, H. (2011). Closed-loop production systems—A sustainable supply chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243-246.

52. Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64(1), 385-397.

53. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.

54. Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean–variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787-2801.

55. Zhang, W. G., & Xiao, W. L. (2009). On weighted lower and upper possibilistic means and variances of fuzzy numbers and its application in decision. Knowledge and information systems, 18(3), 311-330.

56. Zhang, P., & Zhang, W. G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74-91.

57. Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151-164.

58. Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.