طراحی شبکه زنجیره تأمین چنددوره‌ای و چند‌محصولی با در‌نظر‌گرفتن اختلال در تسهیلات و مسیرهای ارتباطی (موردمطالعه: طرح اشتراک نشریات)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشگاه علم و صنعت ایران.

2 دانشیار، دانشگاه علم و صنعت ایران.

چکیده

یکی از مهم‌ترین چالش‌های مسئله‌ طراحی شبکه زنجیره تأمین، وقوع اختلالات احتمالی و آسیب‌های ناشی از آن است. در این پژوهش طراحی شبکه زنجیره تأمین با در‌نظر‌گرفتن حداقل میزان دریافت به‌عنوان رضایت مشتری مدنظر بوده است. برای مقابله با اختلال از سه روش تأسیس تسهیلات جدید، استفاده از قرارداد دوجانبه و تسهیلات موجود در بازار خدمات لحظه‌ای استفاده‌ شده است. بدین منظور یک مدل برنامه‌ریزی خطی مختلط عدد صحیح فرموله شده و طرح اشتراک نشریات به­عنوان مورد مطالعه بررسی شد. یافته‌ها نشان می‌دهد که در شرایط بروز اختلال سه حالت متفاوت رخ می‌دهد: نخست اینکه هزینه اختلال کم باشد که در این شرایط به‌صرفه خواهد بود از بین بازار لحظه‌ای و پذیرش کمبود یکی انتخاب شود؛ دوم اینکه هزینه‌ اختلال و کمبود بالا بوده، اما کمتر از میزان بودجه برای تأسیس تسهیلات باشد که در این حالت از قراردادهای دوجانبه استفاده می‌شود؛ و در حالت سوم  هزینه‌های اختلال و کمبود آن‌قدر بالاست که تأسیس تسهیلات، به‌صرفه خواهد بود. باید توجه داشت با بالا­رفتن تقاضا و یا افزایش احتمال وقوع اختلال، فاصله هزینه‌ای بین استفاده از تسهیل پشتیبان موجود در قرارداد دوجانبه و یا خرید خدمت از بازار لحظه‌ای با تأسیس تسهیل جدید کاهش ‌یافته و سپس تأسیس تسهیلات جدید به‌صرفه‌تر خواهد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Design of Multi-Periodical and Multi-Product Supply Chain Network with Regard to Disruption of Facilities and Communication Paths (Case Study: Subscription Plan for Publications)

نویسندگان [English]

  • Ali Asghar Emadabadi 1
  • Ebrahim Teimoury 2
  • Mir Saman Pishvaee 2
1 Ph.D Student, Iran University of Science and Technology.
2 Associate Professor, Iran University of Science and Technology.
چکیده [English]

One of the most important challenges of designing a supply chain network is possible disruptions. This study is intended to design a supply chain network considering the minimum amount of receiving as a customer satisfaction index. To overcome disruptions, the three main methods applied include establishing new facilities, using bilateral agreements, and using the existing facilities of instantaneous services market. To do so, a complex integer linear programming model is established and examined as a case study of a subscription plan for publications. In the case of disruptions, three possibilities will happen. Firstly, if the cost of disruption is low, it will be cost-effective to choose whether an instantaneous market or the adoption of shortage. Secondly, if the cost of disruption and shortage is high but less than the budget allocated to facilities, the bilateral agreements will be used. Finally, if the cost of disruptions and shortage is too high, the establishment of a new facility will be cost-effective. It should be noticed that by increasing for demand or in the probability of disruption, the cost gap between the use of the existing background facility and/or buying the service of the instantaneous market would be narrowed by establishing the facility.

کلیدواژه‌ها [English]

  • Supply Chain Network Design
  • Location-Allocation
  • Customer Satisfaction
  • Disruption
  • Subscription Plan for Publications

مراجع

  1. Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369-384.
  2. Aqlan, F., & Lam, S. S. (2016). Supply chain optimization under risk and uncertainty: A case study for high-end server manufacturing. Computers and Industrial Engineering, 93, 78–87
  3. https://doi.org/10.1016/j.cie.2015.12.025
  4. Azad, N., & Davoudpour, H. (2013). Designing a stochastic distribution network model under risk. Int J Adv Manuf Technol, 64, 23–40.
  5. Benaïcha, S., & Hadj-Alouane, A. B. (2013). Super facilities versus chaining in mitigating disruptions impacts. Computers and Industrial Engineering, 65(3), 351–359.
  6. Bozorgi Amiri, A., Mansoori, S., Pishvaee, M. (2017). 'Multi-objective Relief Chain Network Design for Earthquake Response under Uncertainties', Journal of Industrial Management Perspective, 7(1), 9-36 (in Persian).
  7. Cui, J., Zhao, M., Li, X., Parsafard, M., & An, S. (2016). Reliable design of an integrated supply chain with expedited shipments under disruption risks. Transportation Research Part E: Logistics and Transportation Review, 95, 143–163.
  8. Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 119, 338-352.
  9. Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125-138.
  10. Drezner, Z. (1987). Heuristic Solution Methods for Two Location Problems with Unreliable Facilities. He Journal of the Operational Research Society, 38(6), 509–514.
  11. Dubey, R., Gunasekaran, A., & Childe, S. J. (2015). The design of a responsive sustainable supply chain network under uncertainty. The International Journal of Advanced Manufacturing Technology, 80(1-4), 427-445.
  12. Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive Supply Chain Network Design: An Overview of Classifications, Models, Solution Techniques and Applications. Omega, 45, 92-118.
  13. Feizollahi, S., Soltanpanah, H., Farughi, H., Rahimzadeh, A. (2019). 'Development of Multi Objective Multi Period Closed-Loop Supply Chain Network Model Considering Uncertain Demand and Capacity', Journal of Industrial Management Perspective, 8(4), 61-95 (in Persian).
  14. Ghavamifar,A.(2015)” A Reliable Competitive Supply Chain Network Design under Risk of Disruption and Uncertainty Case Study: Isaco Company,”MS’s Thesis in Industrial Engineering, Industrial Engineering Department, Iran University of Science and Technology,(in Persian).
  15. Goh, M., Lim, J. Y. S., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182, 164–173.
  16. Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108-141.
  17. Hajimirzajan , A., Pirayesh, M. & Dehghanian, F., (2014). Developing a Supply Chain Planning Model for Perishable Crops. Production and Operations Management, 10(1), pp. 35-60,(in Persian).
  18. Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
  19. Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649-4669.
  20. Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International Journal of Computer Integrated Manufacturing, 28(6), 664-678.
  21. Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 100725.
  22. Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers and Industrial Engineering, 116, 178–191.
  23. Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., & Azad, N. (2012). Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering.
  24. Jouzdani, J., Sadjadi, S. J., & Fathian, M. (2013). Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Applied Mathematical Modelling, 37(18-19), 8467-8483.
  25.         Kadambala, D. K., Subramanian, N., Tiwari, M. K., Abdulrahman, M., & Liu, C. (2017). Closed loop supply chain networks: Designs for energy and time value efficiency. International Journal of Production Economics, 183, 382-393.
  26. Kamalahmadi, M., & Parast, M. M. (2017). An assessment of supply chain disruption mitigation strategies. International Journal of Production Economics, 184, 210–230.
  27. 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.
  28. Kirschen, D.S. and Strbac, G., (2018). Fundamentals of power system economics. John Wiley & Sons
  29. Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412.
  30. Mohammed, A., & Wang, Q. (2017). Developing a meat supply chain network design using a multi-objective possibilistic programming approach. British Food Journal, 119(3), 690-706.
  31. Mohammed, A., & Wang, Q. (2017). The fuzzy multi-objective distribution planner for a green meat supply chain. International Journal of Production Economics, 184, 47-58.
  32. Mohammaditabar, D., Ghodsypour, S. H., & Hafezalkotob, A. (2016). A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs. International Journal of Production Economics, 181, 87-97.
  33. Nikjoo, N., Javadian, N. (2019). 'A Multi-Objective Robust Optimization Logistics Model in Times of Crisis under Uncertainty', Journal of Industrial Management Perspective, 8(4), 121-147 (in Persian).
  34. Ouyang, Y., & Li, X. (2010). The bullwhip effect in supply chain networks. European Journal of Operational Research, 201(3), 799–810.
  35. Ouyang, Y., & Li, X. (2010). The bullwhip effect in supply chain networks. European Journal of Operational Research, 201(3), 799-810.
  36. Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, 292, 57-74.
  37. Peng, P., Snyder, L. V, Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B, 45(8), 1190–1211. 
  38. Rajagopal, V., Prasanna Venkatesan, S., & Goh, M. (2017). Decision-making models for supply chain risk mitigation: A review. Computers and Industrial Engineering, 113, 646–682. 
  39. Rezaei, S. R., Hejazi, S. R. & Rasti-Barzoki, M., (2018). An ant colony optimization for an Integrated Production and Distribution Scheduling Model in Supply Chains: Minimizing Total Weighted Tardiness and Delivery Cost. Production and Operations Management, 8(2), pp. 60,(in Persian).
  40. Rostami, B., Kämmerling, N., Buchheim, C., & Clausen, U. (2018). Computers and Operations Research Reliable single allocation hub location problem under hub breakdowns. Computers and Operations Research, 96, 15–29.
  41. Schmitt, A. J., Sun, S. A., Snyder, L. V., & Shen, Z. J. M. (2015). Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega (United Kingdom), 52, 201–212.
  42. Snyder, L. V., & Daskin, M. S. (2005). Reliability Models for Facility Location: The Expected Failure Cost Case. Transportation Science, 39(3), 400–416.
  43. Stefansdottir, B., & Grunow, M. (2018). Selecting new product designs and processing technologies under uncertainty: Two-stage stochastic model and application to a food supply chain. International Journal of Production Economics, 201, 89-101.
  44. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review, 48(6), 1152-1168.
  45. Valipour, P., Rashidi, A. S., Javanshir, H. & Moaboodi, M., (2010). The Impact of Supply Chain Management on Customer Satisfaction in Textile Industry. Textile Science and Technology, 5(1), pp. 13-23,(in Persian).
  46. Venegas, B. B., & Ventura, J. A. (2018). A two-stage supply chain coordination mechanism considering price sensitive demand and quantity discounts. European Journal of Operational Research, 264(2), 524-533.
  47. Wong, A., Tjosvold, D., & Zhang, P. (2005). Supply chain relationships for customer satisfaction in China: Interdependence and cooperative goals. Asia Pacific Journal of Management, 22(2), 179-199.
  48. Yahyaei, M., & Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: an integration of shelter and supply facility location. Annals of Operations Research, 1–20.
  49. Yongheng, J., Rodriguez, M. A., Harjunkoski, I., & Grossmann, I. E. (2014). Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm. Computers & Chemical Engineering, 62, 211-224.
  50. Zahiri, B., Tavakkoli-Moghaddam, R., Mohammadi, M., & Jula, P. (2014). Multi-objective design of an organ transplant network under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 72, 101-124.

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