طراحی پایای شبکه زنجیره تامین حلقه بسته تحت عدم قطعیت: مطالعه موردی یک تولید‌کننده باتری‌‌ اسیدی

نویسندگان

1 دانشگاه تربیت مدرس

2 دانشگاه علم و صنعت ایران

چکیده

امروزه، نگرانی در مورد وقوع ناگهانی اختلالات و ضررهای بزرگ ناشی از آن‌، بسیاری از محققین را به طراحی پایای شبکه‌های زنجیره تامین سوق داده است. همچنین، عدم قطعیت ذاتی پارامترهای ورودی، مساله‌ای مهم است که به واسطه اثرات نامطلوب آن بر تصمیمات استراتژیک، تاکتیکی و عملیاتی، باید با دقت فراوان در طراحی شبکه‌های زنجیره تامین مد نظر قرار گیرد. بنابراین، این مقاله، یک مدل جدید برای طراحی پایای شبکه زنجیره تامین حلقه بسته ارائه می‌نماید که در آن یک رویکرد جدید پایایی معرفی گردیده است. مدل پیشنهادی نه تنها مجموع هزینه‌ها را کمینه می‌نماید، بلکه به صورت اثربخش، شبکه‌ای مقاوم تحت اختلالات مختلف را ایجاد می‌نماید. برای مقابله با عدم قطعیت پارامترها از یک روش کارای برنامه‌ریزی امکانی بهره گرفته شده است. در نهایت، از یک مطالعه موردی صنعتی واقعی به منظور نمایش اثربخشی و کاربردی بودن مدل بهینه سازی فازی توسعه داده شده، استفاده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

A reliable closed-loop supply chain network design under uncertainty: A case study of a lead-acid battery manufacturer

نویسندگان [English]

  • Mohamadreza Fazli khalaf 1
  • Seyed kamal Chaharsooghi 1
  • Mir saman Pishvaee 2
چکیده [English]

Nowadays, the concern about strike of disruptions and its consequent huge losses has motivated many researchers to design reliable supply chain networks. As well, inherent uncertainty of input data is a momentous issue which should be considered carefully in the design of supply chain networks due to its adverse effect on quality of strategic, tactical and operational decisions. Therefore, this paper presents a novel model for designing a reliable closed-loop supply chain network in which a new reliability method is introduced. The propounded model not only minimizes the total cost, also efficiently finds a robust network under different kind of disruptions. To cope with imprecision of parameters, an effective possibilistic programming approach is employed. Finally, a real industrial case is used to demonstrate the effectiveness and applicability of the developed fuzzy optimization model.

کلیدواژه‌ها [English]

  • Supply chain network design
  • Reliability
  • Closed-loop supply chain
  • Fuzzy mathematical programming
 

[1] H.L. Lee, Aligning supply chain strategies with product uncertainties, California Management Review, 44 (1) (2005) 105–119.

[2] A.C.S. Amaro, A.P.F.D. Barbosa-Póvoa, The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure, Computers and Chemical Engineering 33 (2009) 2144–2158.

[3] M.R. Maxwell, V.B. Gargeya, Global supply chain design: A literature review and critique, Transportation 41 (2005) 531–550.

[4] W. Dullaert, O. Braysy, M. Goetschalckx, B. Raa, Supply chain (re)design: support for managerial and policy decisions, European Journal of Transport and Infrastructure Research 7 (2) (2007) 73–91.

[5] B. Vahdani, R. Tavakkoli-Moghaddam, F. Jolai, Arman Baboli, Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model, Engineering Optimization (2012) 1–21.

[6] L. Meade, J. Sarkis, A. Presley, The theory and practice of reverse logistics, International Journal of Logistics Systems and Management 3 (2007) 56–84.

[7] R. Cruz-Rivera, J. Ertel, Reverse logistics network design for the collection of end-of-life vehicles in Mexico. European Journal of Operational Research, 196 (1) (2009) 930–939.

[8] H. Baumgarten, B. Christian, F. Annerous, S.D. Thomas, Supply chain management and reverse logistics-integration of reverse logistics processes into supply chain management approaches, in: Proceedings of the Electronics and the Environment, on IEEE International Symposium, IEEE Computer Society, Washington, 2003, pp. 79–83.

[9] F. Schultmann, Z. Moritz, R. Otto, Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry, Eur. J. Oper. Res. 171 (2006) 1033–1050.

[10] Ramezani M, Bashiri M, Tavakkoli-Moghaddam R (2012) A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Math Model. doi:10.1016/j.apm.2012.02.032

[11] B. Vahdani , R. Tavakkoli-Moghaddam, F. Jolai., Reliable design of a logistics network under uncertainty: A fuzzy possibilistic-queuing model, Appl. Math. Modell. (2012).

[12] B. Vahdani, R. Tavakkoli-Moghaddam, M. Modarres, A. Baboli, Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model, Transportation Research Part E 48 (2012) 1152–1168.

[13] M.S. Pishvaee, S.A. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty, Fuzzy Sets Syst. 161 (2010) 2668–2683.

[14] M.S. Pishvaee, J. Razmi, Environmental supply chain network design using multi-objective fuzzy mathematical programming, Appl. Math. Modell. (2011), doi:10.1016/j.apm.2011.10.007.

[15] M. Fleischmann, P. Beullens, J.M. Bloemhof-ruwaard, L. Wassenhove, The impact of product recovery on logistics network design, Productions and Operations Management 10 (2001) 156–173.

[16] W. Klibi, A. Martel, A. Guitouni, The design of robust value-creating supply chain networks: a critical review, European Journal of Operational Research, 203 (2010) 283–293.

[17] C. Ho, Evaluating the impact of operating environments on MRP system nervousness, International Journal of Production Research 27 (1989) 1115–1135.

[18] P. Peng, L.V. Snyder, A. Lim, Z. Liu, Reliable logistics networks design with facility disruptions. Transportation Research Part B 45 (2011) 1190–1211.

[19] D. Clark, Y. Takahashi, Quake disrupts key supply chains, The Wall Street Journal Asia, March 12 (2011).

[20] L.V. Snyder, M.S. Daskin, Reliability models for facility location: the expected failure cost case. Transportation Science 39(3) (2005) 400–416.

[21] M. Lim, M. Daskin, A. Bassamboo, S. Chopra, A facility reliability problem: formulation, properties and algorithm, Naval Research Logistics, 57(1) (2010) 58–70.

[22] N. Azad, G.K.D. Saharidis, H. Davoudpour, H. Malekly. Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Operations Research. Doi: 10.1007/s10479-012-1146-x.

[23] G. Kannan, P. Sasikumar, K. Devika, A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling, Applied Mathematical Modelling 34 (2010) 655–670.

[24] UNEP and the Secretariat of the Basel Convention, Technical Guidelines for the Environmentally Sound Management of Waste Lead-acid Batteries, Basel Convention series, SBC No. 2003/9.

[25] G.T. Tsoulfas, C.P. Pappis, S. Minner, An environmental analysis of the reverse supply chain of SLI batteries, Resources, Conservation and Recycling 36 (2002) 135–154.

[26] S.E. Daniel, C.P. Pappis, T.G. Voutsinas, Applying life cycle inventory to reverse supplychains: a case study of lead recovery from batteries, Resources, Conservation and Recycling 37 (2003) 251_/281.

[27] M.I.G. Salema, A.P. Barbosa-Povoa, A.Q. Novais, An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty, European Journal of Operational Research 179 (2007) 1063–1077.

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

[29] M. El-Sayed, N. Afia, A. El-Kharbotly, A stochastic model for forward_reverse logistics network design under risk, Comput. Ind. Eng. 58 (2010) 423–431.

[30] L. Liu, G.H. HUANG, G.A FULLER, Y. LIU, G.M. ZENG, A fuzzy-stochastic robust optimization model for regional air quality management under uncertainty. Engineering Optimization, 35 (2) (2003) 177–199.

[31] R.E. Bellman, L.A. Zadeh, Decision making in a fuzzy environment, Management Science 17 (1970) 141–164.

[32] M. Inuiguchi, J. Ramik, Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem, Fuzzy Sets Syst. 111 (2000) 3–28.

[33] D. Dubois, H. Fargier, P. Fortemps, Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge, Eur. J. Oper. Res. 147 (2003) 231–252.

[34] M.S. Pishvaee, J. Razmi, S.A. Torabi, Robust possibilistic programming for socially responsible supply chain network design: A new approach, Fuzzy Sets and Systems 206 (2012) 1–20.

[35] M. Jimenez, M. Arenas, A. Bilbao, M.V. Rodriguez, Linear programming with fuzzy parameters: an interactive method resolution, European Journal of Operational Research 177 (2007) 1599–1609.

[36] S. Heilpern, The expected valued of a fuzzy number. Fuzzy Sets and Systems 47 (1992) 81–86.[37] M. Jimenez, Ranking fuzzy numbers through the comparison of its expected intervals, Int. J. Uncertain. Fuzziness Knowl. Based Syst. 4 (4) (1996) 379–388.