زنجیره‌تأمین حلقه بسته پایدار و تاب‌آور دارو با تأمین‌کنندۀ پشتیبان تحت شرایط بیماری کرونا (کووید-19)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه مهندسی صنایع، پردیس فنی و مهندسی، دانشگاه یزد، ایران

2 کارشناسی ارشد، مهندسی صنایع، دانشکده اقتصاد، دانشگاه خوارزمی تهران، ایران

3 دانشیار گروه مدیریت صنعتی، دانشکده اقتصاد، دانشگاه یزد، ایران

چکیده

صنعت داروسازی در ایران دچار مشکلاتی مانند توزیع و زمان‌بندی نامناسب دارو است که موجب به‌موقع نرسیدن دارو به بیماران و یا از طرف دیگر حجم عظیم داروهای تاریخ‌گذشته شده است. همچنین توجه به مسائل زیست‌محیطی و اجتماعی در کنار مسائل اقتصادی رویکرد جدی برای رسیدن به توسعه پایدار است. در این مقاله، برای توزیع دارو در سطح کشور باتوجه به میزان تقاضا، توابع هدف اقتصادی، زیست‌محیطی و اجتماعی درنظر گرفته شده است. هدف، طراحی مدلی نوین برای شبکه توزیع دارو منطبق با شرایط وجود یک اپیدمی (بیماری‌های همه‌گیر مانند کرونا) و بررسی اثر این بیماری بر زنجیره‌تأمین دارو است. همچنین، با اضافه‌شدن مرکز تأمین‌کنندۀ پشتیبان سعی شده تا علاوه‌بر مدیریت نمودن تأثیرهای منفی بیماری کرونا، آثار آن نیز به‌صورت جامع بررسی و تحلیل شود. برای به‌دست‌آوردن جبهۀ جواب (مرز پارتو) روش اپسیلون‌محدودیت بهبود‌یافتۀ افزودۀ 2 (AUGMECON2) به‌کار رفته است. بااستفاده از مدل پیشنهادی، مدیران زنجیره‌تأمین نه‌تنها قادر به تصمیم‌گیری‌های تاکتیکی (میزان جریان محصول در شبکه) با بیشترین سود، بیشترین تاثیر مثبت اجتماعی، کاهش ایجاد گازهای گلخانه‌ای و کاهش میزان سرایت بیماری می‌شوند؛ بلکه می‌توانند با ایجاد تاب‌آوری و پایداری در شبکۀ‌ خود برای آینده، در شرایط بحرانی مانند وجود ویروس کرونا، مشکلات جدی شبکه زنجیره‌تأمین خود را تا حد زیادی کاهش داده یا از بین ببرند. نتایج نشان‌داد که رویکرد پیشنهادی برای شرایط بیماری کرونا از کارایی بسیار مناسبی برخوردار بوده و می‌تواند آثار مخرب و زیان‌بار این بیماری مهلک و خانمان سوز را تا اندازۀ قابل‌توجهی کاهش دهد

کلیدواژه‌ها


عنوان مقاله [English]

Sustainable and Resilient Closed-Loop Drug Supply Chain with Backup Suppliers under Coronavirus Disease Pandemic (COVID-19)

نویسندگان [English]

  • Davood Shishebori 1
  • Omid Abdolazimi 2
  • Davood Andalib Ardakani 3
1 Associate Professor, Department of Industrial Engineering, Technical and Engineering Campus, Yazd University, Iran
2 Master's degree, Industrial Engineering, Faculty of Economics, Kharazmi University, Tehran, Iran
3 Associate Professor, Department of Industrial Management, Faculty of Economics, Yazd University, Iran
چکیده [English]

The pharmaceutical industry in Iran suffers from problems such as improper distribution and scheduling of drugs that have delayed the delivery of drugs to patients or on the other hand a huge volume of expired drugs. Also, paying attention to environmental and social issues along with economic ones is a serious approach to achieving sustainable development. Therefore, in this paper, economic, environmental, and social objective functions are considered for drug distribution in Iran according to the amount of demand. The purpose is to design a new model for the drug distribution network following the conditions of an epidemic (COVID-19) and to investigate its effect on the drug supply chain. Besides, in this paper, by adding backup suppliers, in addition to neutralizing the negative effects of Coronavirus, its effects are also examined and analyzed. To obtain the Pareto front, the improved version of augmented Ɛ-constraint (AUGMECON2) has been utilized. Using the proposed model, supply chain managers are able to make tactical decisions (product flow rate in the network) with the most profit, the most positive social impact, reduce greenhouse gasesو and reduce disease transmission. They can also greatly reduce or eliminate the serious problems of their supply chain network by creating resilience and sustainability in their network for the future, in critical situations such as the COVID-19 pandemic. The results showed that the suggested model has suitable efficiency for Coronavirus conditions, and can significantly reduce the destructive and harmful effects of this deadly epidemic

کلیدواژه‌ها [English]

  • Closed-loop Drug Supply Chain
  • Sustainability and Resilience
  • Coronavirus Desiease
  • Environmental and Social Issues. Backup Supplier
  • Choi, T. M., Wen, X., Sun, X., & Chung, S. H. (2019). The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transportation Research Part E: Logistics and Transportation Review, 127, 178-191.
  • Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
  • Ivanov, D., Dolgui, A., & Sokolov, B. (Eds.). (2019). Handbook of ripple effects in the supply chain (Vol. 276). New York: Springer.
  • Dolgui, A., Ivanov, D., & Rozhkov, M. (2020). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 58(5), 1285-1301.
  • Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics, 107693.
  • Araz, O. M., Choi, T. M., Olson, D., & Salman, F. S. (2020). Data analytics for operational risk management. Decision Sciences.
  • شاوردی، مرضیه. (1395). طراحی و مدیریت زنجیره عرضه. موسسه انتشارات علمی دانشگاه صنعتی شریف، تهران، چاپ دوم، شهریور 1395.
  • Aday, S., & Aday, M. S. (2020). Impact of COVID-19 on the food supply chain. Food Quality and Safety, 4(4), 167-180.
  • Guan, D., Wang, D., Hallegatte, S., Davis, S. J., Huo, J., Li, S., ... & Gong, P. (2020). Global supply-chain effects of COVID-19 control measures. Nature human behaviour, 4(6), 577-587.
  • Abdolazimi, O., Esfandarani, M. S., Salehi, M., & Shishebori, D. (2020a). Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production, 121566.
  • Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Van Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
  • 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.
  • Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194.
  • 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.
  • Amin, S. H., & 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.
  • Papen, P., & Amin, S. H. (2019). Network configuration of a bottled water closed-loop supply chain with green supplier selection. Journal of Remanufacturing, 9(2), 109-127.
  • Govindan, K., & Popiuc, M. N. (2014). Reverse supply chain coordination by revenue sharing contract: A case for the personal computers industry. European Journal of Operational Research, 233(2), 326-336.
  • Cardoso, S. R., Barbosa-Póvoa, A. P. F., & 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.
  • Chakraborty, T., Chauhan, S. S., & Ouhimmou, M. (2020). Mitigating supply disruption with a backup supplier under uncertain demand: competition vs. cooperation. International Journal of Production Research, 58(12), 3618-3649.
  • Leonard, D. 2005. “The Only Lifeline Was the Wal-Mart.” Fortune Magazine, October 3. http://archive.fortune.com/magazines/fortune/ fortune_archive/2005/10/03/8356743/index.htm/.
  • Reuters Staff. 2016. “Toyota Resumes Production at Japan Plants After Steel Shortage.” Reuters, February 14. https://www.reuters.com/ article/autos-toyota-production-idUSL3N15U0J3/.
  • Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics (NRL), 54(5), 544-555.
  • Chen, K., & Yang, L. (2014). Random yield and coordination mechanisms of a supply chain with emergency backup sourcing. International Journal of Production Research, 52(16), 4747-4767.
  • Giri, B. C., & Dey, S. (2020). Game theoretic models for a closed-loop supply chain with stochastic demand and backup supplier under dual channel recycling. Decision Making: Applications in Management and Engineering, 3(1), 108-125.
  • Zeng, N., Zeng, D., Liu, A., & Jin, L. (2020). Drop-Shipping and Backup-Sourcing Strategies Under the Risk of Supply Disruption. IEEE Access, 8, 169496-169515.
  • Janatyan, N., Zandieh, M., Alem Tabriz, A., & Rabieh, M. (2019). Optimizing Sustainable Pharmaceutical Distribution Network Model with Evolutionary Multi-objective Algorithms (Case Study: Darupakhsh Company). Journal of Production and Operations Management, 10(1), 133-153.
  • Ahmadi, A., Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2018). Or applications in pharmaceutical supply chain management. In Operations research applications in health care management (pp. 461-491). Springer, Cham.
  • سلیمی زاویه، سید قاسم. (1399). راهبردهای پاسخ به بحران در زمان بحران کرونا ویروس (کووید 19) در بخش تولید و صنعت. فصل‌نامه توسعه تکنولوژی صنعتی، شماره 39، بهار 1399، صفحه 76-63.
  • Wenzel, M., Stanske, S., & Lieberman, M. B. (2020). Strategic responses to crisis. Strategic Management Journal, 41(7/18).
  • Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2019). Disaster relief operations: past, present and future. Annals of Operations Research, 283(1-2), 1-8.
  • Farahani, R. Z., Lotfi, M. M., Baghaian, A., Ruiz, R., & Rezapour, S. (2020). Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations. European Journal of Operational Research.
  • Johanis, D. (2007). How Toronto Pearson International Airport applied lessons from SARS to develop a pandemic response plan. Journal of Business Continuity & Emergency Planning, 1(4), 356-368.
  • Chou, J., Kuo, N. F., & Peng, S. L. (2004). Potential impacts of the SARS outbreak on Taiwan's economy. Asian Economic Papers, 3(1), 84-99.
  • Calnan, M., Gadsby, E. W., Kondé, M. K., Diallo, A., & Rossman, J. S. (2018). The response to and impact of the Ebola epidemic: towards an agenda for interdisciplinary research. International journal of health policy and management, 7(5), 402.
  • Bild, 2020. https://www.bild.de/news/inland/news-inland/coronavirus-rki-erklaert-ganz-italien-zum-sperrgebiet-weltweit-nehmen-faelle-zu-69089326.bild.html, accessed on March 10, 2020.
  • Apple, 2020. Investor update on quarterly guidance [February 17, 2020], accessed on March 11, 2020.
  • Retaildive, 2020. https://www.retaildive.com/news/the-impact-of-the-coronavirus-on-retail/573522/, accessed on March 10, 2020.
  • (2020). WHO announces COVID-19 outbreak a pandemic. https://www.euro.who.int/en/health-topics/health emergencies/coronavirus-covid-19/news/news/2020/3/who announces-covid-19-outbreak-a-pandemic. Accessed December 1, 2020.
  • Nunes, L. J. R., Causer, T. P., & Ciolkosz, D. (2020). Biomass for energy: A review on supply chain management models. Renewable and Sustainable Energy Reviews, 120, 109658.
  • Vafaei, A., Yaghoubi, S., Tajik, J., & Barzinpour, F. (2020). Designing a sustainable multi-channel supply chain distribution network: A case study. Journal of Cleaner Production, 251, 119628.
  • Abdolazimi, O., & Khakestari, M. (2020). Determine the optimal number of item groups in the werehouse based on ABC analysis within the framework of a supply chain network.
  • Abdolazimi, O., Esfandarani, M. S., & Shishebori, D. (2021). Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Computing and Applications, 33(12), 6641-6656.
  • Rasi, R. E., & Sohanian, M. (2020). A multi-objective optimization model for sustainable supply chain network with using genetic algorithm. Journal of Modelling in Management.
  • Micheli, G. J., Cagno, E., Mustillo, G., & Trianni, A. (2020). Green supply chain management drivers, practices and performance: A comprehensive study on the moderators. Journal of Cleaner Production, 259, 121024.
  • Gao, J., Xiao, Z., Wei, H., & Zhou, G. (2020). Dual-channel green supply chain management with eco-label policy: A perspective of two types of green products. Computers & Industrial Engineering, 146, 106613.
  • Pourmehdi, M., Paydar, M. M., & Asadi-Gangraj, E. (2020). Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology. Journal of Cleaner Production, 277, 123298.
  • Venkatesh, V. G., Kang, K., Wang, B., Zhong, R. Y., & Zhang, A. (2020). System architecture for blockchain based transparency of supply chain social sustainability. Robotics and Computer-Integrated Manufacturing, 63, 101896.
  • Govindan, K., Shaw, M., & Majumdar, A. (2020). Social sustainability tensions in multi-tier supply chain: A systematic literature review towards conceptual framework development. Journal of Cleaner Production, 123075.
  • شاوردی، مرضیه. (1399). بازیابی زنجیره‌تأمین در عصر کروناویروس – برنامه‌ریزی برای حال و آینده. سلسله گزارش‌های میز رصد کرونا- گزارش(8)، بهار 1399.
  • Anvari, S., & Turkay, M. (2017). The facility location problem from the perspective of triple bottom line accounting of sustainability. International Journal of Production Research, 55(21), 6266-6287.
  • Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020). A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains. Advances in Industrial Engineering, 54(1), 75-98.
  • Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
  • Florios, K., & Mavrotas, G. (2014). Generation of the exact pareto set in multi-objective traveling salesman and set covering problems. Applied Mathematics and Computation, 237, 1-19.
  • Nikas, A., Fountoulakis, A., Forouli, A., & Doukas, H. (2020). A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Operational Research, 1-42.
  • Shafiee, M., Zare Mehrjerdi, Y., & Keshavarz, M. (2021). Integrating lean, resilient, and sustainable practices in supply chain network: mathematical modelling and the AUGMECON2 approach. International Journal of Systems Science: Operations & Logistics, 1-21.
  • فخرزاد، محمدباقر، لطفی، رضا. (1396). مدل سبز مدیریت موجودی توسط فروشنده با مجاز بودن کمبود در زنجیره تأمین دوسطحی با رویکرد‌های حل اپسیلون محدودیت و .NSGA-II  نشریه پژوهش های مهندسی صنایع در سیستم های تولید. دوره 5، شماره 11، پاییز و زمستان 1396، صفحه 193-209.
  • Abdolazimi, O., Esfandarani, M. S., Salehi, M., Shishebori, D., & Shakhsi-Niaei, M. (2021). Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study. The International Journal of Logistics Management.
  • رحیمی شیخ، حبیب اله، شریفی، مانی، شهریاری، محمدرضا. (1396). طراحی مدل زنجیره تأمین تاب آور (مورد مطالعه: سازمان بهزیستی کشور). چشم اندازی مدریت صنعتی. شماره 27، پاییز 1396، صفحه 127-150.
  • Pettit, T. J., Fiksel, J., & Croxton, K. L. (2008). Can you measure your supply chain resilience. Supply Chain and Logistics Journal, 10(1), 21-22.
  • Glickman, T. S., & White, S. C. (2006). Security, visibility and resilience: the keys to mitigating supply chain vulnerabilities. International Journal of Logistics Systems and Management, 2(2), 107-119.
  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The international journal of logistics management.
  • جعفرنژاد، احمد، محسنی، مریم. (1394). ارائه چارچوبی برای بهبود عملکرد زنجیره تأمین تاب آور. فصلنامه علمی-ترویجی مدیدیت زنجیره تأمین. سال 17، شماره 48، تابستان 1394، صفحه 38-51.