طراحی شبکه زنجیره تامین حلقه بسته در فضای عدم قطعیت

محورهای موضوعی : مدیریت صنعتی

Reza Yousefi Zenouz ¹ , Farzad Haghighi rad ² , sajad zakeritabar ³

1 - Information technology and operations management, Kharazmi University
2 - Faculty member,, Operations and Information technology management Kharazmi University
3 - Operations and information Technology Management

تاریخ دریافت : 1399/04/14 تاریخ پذیرش : 1399/11/04 تاریخ انتشار : 1399/12/06

کلید واژه: عدم قطعیت, شبکه زنجیره تامین حلقه بسته, بهینه سازی چندهدفه, بهینه سازی استوار,

چکیده مقاله :

تغییرات آب و هوا و اثرات مخرب زیست محیطی فعالیتهای اقصادی، زنجیره های تامین را بر آن داشته است که در بازارهای رقابتی جهت کسب مزیت رقابتی در کنار عملکرد مالی، به دنبال اجرای سیاستهای سبز و کاهش آسیب به محیط زیست باشند. یکی از روشهای دستیابی همزمان به اهداف اقتصادی و زیست محیطی، داشتن شبکه های زنجیره تامین حلقه بسته است که در آنها علاوه از جریان رو به جلو، لجستیک معکوس نیز در شبکه ادغام شده است. در این مقاله یک مدل برنامه ریزی عدد صحیح مختلط دو هدفه به منظور طراحی یک شبکه زنجیره تأمین حلقه بسته توسعه داده شده است. تابع هدف اول کمینه کردن هزینه‌های اقتصادی و تابع هدف دوم شامل حداقل کردن زمان تاخیر ارسال محصولات از تولیدکنندگان به توزیع کنندگان است. برای حل مدل از روش های ال پی-متریک و اپسیلون-محدودیت استفاده شده است. در نهایت مثال عددی برای ارزیابی و آنالیز حساسیت مدل ارائه شده است. در این مدل هزینه‌ها و تقاضا بعنوان پارامترهای غیر قطعی در نظرگرفته می‌شود. در راستای مواجهه با پارامترهای غیر قطعی و کاهش تاثیر آن بر روی جواب بهینه، یک مدل بهینه سازی استوار مطرح شده است. به منظور حل مدل ارائه شده در مقیاس بزرگ از الگوریتم بهینه سازی ازدحام ذرات چند هدفه (MOPSO) بهره‌گرفته شد. برای نشان دادن کارایی الگوریتم فراابتکاری پیشنهادی MOPSO، جواب های به دست آمده با جواب های روش حل دقیق مقایسه شده است. یافته های این تحقیق می تواند تصمیم گیرندگان را در طراحی زنجیره های تامین حلقه بسته یاری رساند.

چکیده انگلیسی:

Climate change and environmental impacts of economic activities, have forced supply chains to implement green policies and reduce environmental impacts and destruction to achieve competiete advantage. One approach to achive simultaneously to the economic and envitonmental objectives is to design closed loop supply chain networks (CLSCN) that integrate reverse logistics into their forward paths. In this paper, a bi-objective mixed integer linear programming model was developed for the CLSCN problem. The first objective is to minimize the cost function and the second objective function tries to minimize the time of transferring products from manufacturers to the distributors. Lp Metric and ε -constraint methods were utilized to solve the model. A numerical example was presented to show the applicability of the model and also sensitivity analysis was done. In this model two parameters of cost and demand are uncertain, in order to deal with uncertain parameters a robust optimization approach was utilizec. Multi objective particle swarm optimization (MOPSO) was used to solve the model in lare scales ad the solutions were compared with the solutions that obtained by exact methods. the findings of this research can help decision makers and executives to design efficient closed loop supply chains.

منابع و مأخذ:

1. Angeline, P. J. (1998). Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. International Conference on Evolutionary Programming (pp. 601-610). Berlin, Heidelberg.: Springer.


2. Cardoso, S. R., Barbosa-Póvoa, A. P., & Relvas, S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226(3), 436-451.


3. Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In B. C., & E. R., Das Summa Summarum des Management (pp. 265-275). Gabler.


4. Coello Coello, C. A., Aguirre, A. H., & Zitzler, E. (2007). Evolutionary multi-objective optimization. European Journal of Operational Research, 18(1), 1617-1619.


5. Coskun, S., Ozgur, L., Polat, O., & Gungor, A. (2016). A model proposal for green supply chain network design based on consumer segmentation. Journal of Cleaner Production, 110, 149-157.


6. Eberhart, R. C., & Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. International conference on evolutionary programming (pp. 611-616). Berlin, Heidelberg: Springer.


7. 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.


8. FAKHRZAD, M., & GOODARZIAN, F. (2019). The green Closed-Loop Supply Chain Network Design Considering Supply Centers Reliability Under Uncertainty. Journal of Industrial Engineering Research in Production Systems, 7(14), 179 - 197.


9. Hassanzadeh, A., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.

10.Hugos, M. H. (2018). Essentials of supply chain management. John Wiley & Sons.

11.Jayaraman, V., Guide, J. V., & Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of the Operational Research Society, 50, 497–508.

12.Jindal, A., Sangwan, K. S., & Saxena, S. (2015). Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty. Procedia Cirp, 29, 656-661.

13.Kara, S. S., & Onut, S. (2010). A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling. Expert Systems with Applications, 39(7), 6129-6137.

14.Kennedy, J., & Eberhart, R. (2001). Swarm Intelligence. San Francisco, CA.: Morgan Kaufmann Publishers, Inc.

15.Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of cleaner production, 196, 1314-1328.

16.Kim, S. H., & Nelson, B. L. (2005). Selecting the best system. Handbooks in operations research and management science, 13, 501-534.

17.Leung, S. C., Tsang, O., S., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multisite production planning problem in an uncertain environment. European Journal of Operational Research, 181, 224-832.

18.Mavrotas, G. (2009). Effective implementation of ε-constraint method in Multi-Objective Mathematical Programming problems". Applied Mathematics and Computation, 213, 455-465.

19.Mohammadi, A. S., Alem Tabriz, A., & Pishvaee, M. (2018). Designing Green Closed-loop Supply Chain Network with Financial Decisions under Uncertainty. Industrial Management Journal, 10(1), 61- 84.

20.Mulvey, J., & Ruszczynski, ,. A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations Research, 43(3), 477-.094.

21.Myers, R. H., & Montgomery, D. C. (1995). Response Surface Methodology: Process and Optimization Using designed experiment. John Wiley and sons Inc.

22.Özkır, V., & Başlıgil, H. (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment. Journal of Cleaner Production, 41, 114-125.

23.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.

24.Poli, R., & Broomhead, D. (2007). Exact analysis of the sampling distribution for the canonical particle swarm optimiser and its convergence during stagnation. Proceedings of the 9th annual conference on Genetic and evolutionary computation, (pp. 134-141).

25.Rabbani, M., Asgaari, E., Ghavamifar, A., & Farrokhi-Asl, H. (2019). Designing a Closed Loop Supply Chain Network Considering the. Computational Methods in Engineering, 37(2), 61-78.

26.Ruimin, M. A., Lifei, Y. A., Maozhu, J. I., Peiyu, R. E., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195-202.

27.Schultmann, F., Zumkeller, M., & Rentz, O. (2005). Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry. European Journal of Operational Research, 1-18.

28.Soleimani, H., & Kannan, G. (2015). hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.

29.Talaei, M., Farhang, M. B., Pishvaee, M., & Bozorgi, A. A. (2015). A Bi-Objective facility location modelfor a green clesed-loop supplychain network design. Quarterly journal of transportation research, 12(1), 65-77.

30.Wu, G. H., Chang, C. K., & Hsu, L. M. (2018). . Comparisons of interactive fuzzy programming approaches for closed-loop supply chain network design under uncertainty. Computers & Industrial Engineering, 125, 500-513.

31.Yu, C., & Li, H. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64, 385–397.

32.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, 51-164.

33.Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry. Journal of Cleaner Production, 133, 314-337.