ارائه مدل دومرحله‌ای احتمالی استوار برای طراحی زنجیره تأمین خون تاب‌آور با درنظرگرفتن اختلال زلزله و بیماری واگیردار

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران.

2 استاد، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تربیت مدرس، تهران، ایران.

3 استاد، گروه مدیریت صنعتی و فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران.

چکیده

هدف: در دنیای آشفته امروزی، زنجیره‌های تأمین با اختلال‌های متعددی مواجه‌اند که سبب قطع یا کاهش جریان می‌‌شوند یکی از راه‌های مقابله با اختلال‌ها، استراتژی‌های تاب‌آوری است. در این مقاله، با در نظر گرفتن دو اختلال در زنجیره تأمین چندسطحی خون و مدنظر قراردادن تأثیر آن‌ها، مدل احتمالی مبتنی بر سناریو دومرحله‌ای استوار ارائه شده است.
روش: پس از مرور مقاله‌های مختلف، شکاف تحقیقاتی بررسی و مدل‌سازی ریاضی صورت پذیرفت. برای مقابله با عدم قطعیت، برنامه‌ریزی احتمالی دومرحله‌ای استوار استفاده شد. در پایان نیز با استفاده از روش ترابی و هسینی، به حل مدل پرداخته شد.
یافته‌ها: مدل ارائه‌شده در موردی واقعی، یعنی زنجیره تأمین خون شهر تهران، در مدت زمان مناسبی با نرم‌افزار گمز حل شد و اثر راهبردهای تاب‌آوری گوناگون در سناریوهای مختلف مشخص شد و با بررسی جواب‌های مدل، صحت مدل به تأیید رسید.
نتیجه‌گیری: در این مقاله نشان داده شد که می‌توان با استفاده از راهبردهای تاب‌آوری افزونگی، بهبود انعطاف‌پذیری و گسترش مسئولیت اجتماعی، زنجیره تأمین خون را تاب‌آور ساخت و کمبود را در هنگام مواجهه با اختلال‌ها کاهش داد.

کلیدواژه‌ها


عنوان مقاله [English]

Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases

نویسندگان [English]

  • Ali Sibevei 1
  • Adel Azar 2
  • Mostafa Zandieh 3
1 PhD Candidate, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
2 Prof., Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
3 Prof., Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
چکیده [English]

Objective: In today's turbulent world, supply chains face a variety of disruptions that cause disruption or reduction of flow in them. One way to deal with supply chain disruptions is through resilience strategies. In this paper, a two-stage scenario-based model was developed considering two disruptions in the multilevel blood supply chain as well as their effects.
Methods: First, by examining different articles, the research gap was investigated and then the mathematical modeling was done. Also, to deal with uncertainty, two-stage stochastic programming was used. Finally, in order to face the multi-objective nature of the model, the model was solved by Torabi and Hosseini method.
Results: The proposed model was solved using the Torabi and Hosseini method in the real case, i.e. the blood supply chain of Tehran, in a suitable period of time by GAMS software.
Conclusion: The achieved results of the present study proved that adopting strategies such as redundancy, flexibility, and expanding social responsibility makes it is possible to make the blood supply chain resilient and reduce the shortage when faced with disruptions.

کلیدواژه‌ها [English]

  • Resilient supply chain
  • Blood supply chain
  • Disruption
  • Robust mathematical modeling
ابراهیمی، مهران؛ صفری، حسین؛ صادقی مقدم، محمدرضا (1396). ارائه مدل تداوم زنجیره تأمین بر اساس رویکرد طراحی آگزیوماتیک. مدیریت صنعتی، 9 (4)، 563-586.
امیری، مقصود؛ حسینی دهشیری، سید جلال الدین؛ یوسفی هنومرور، احمد (1397). تعیین ترکیب بهینه استراتژی‌های لارج با بهره‌گیری از تحلیل SWOT، تکنیک‌های تصمیم‌گیری چند معیاره و تئوری بازی. مدیریت صنعتی، 10 (2)، 221-246.
ایزدیار، مهدی؛ طلوعی اشلقی، عباس؛ سید حسینی، سید محمد (1399). مدل ارزیابی عملکرد پایـداری شـیوه‌هـای مـدیریت زنجیره تأمین لارج در زنجیره تأمین خودروسازی با استفاده از پویایی سیستم. مدیریت صنعتی، 12 (1)، 111-142.
بیگی، سکینه؛ یعقوبی، حسن؛ کریمی، حسین ( 1399 ). تخصیص بهینه نقاط اسکان اضطراری، بیمارستان‌ها و درمانگاه‌ها به ناحیه‌های شهری پس از وقوع زلزله (مطالعه موردی: شهر بجنورد). مدیریت صنعتی، 12 (1)، 82-110.
پایدار، محمد مهدی؛ حبیبی، میترا (1394). طراحی مدل چندهدفه چندسطحی زنجیره تأمین خون در شرایط بحران. مدیریت زنجیره تأمین، 50 (17)، 82-95.
حسن زاده، محمدرضا؛ ملکی، محمدحسن؛ جهانگیرنیا، حسین؛ غلامی جمکرانی، رضا (1399). شناسایی و اولویت‌بندی عوامل مؤثر تاب‌آوری بازار سرمایه ایران. مدیریت صنعتی، 12 (1)، 172-205.
زنده‌دل، محمد؛ بزرگی امیری، علی؛ عمرانی، هاشم (1393). ارائه مدل مکان‌یابی پایگاه‌های اهدای خون با درنظر گرفتن اختلال در محل استقرار. نشریه تخصصی مهندسی صنایع، 48، 33-43.
سیدی، سید حسین؛ خاتمی فیروزآبادی، سید محمدی علی؛ امیری، مقصود؛ تقوی فرد، سید محمد تقی (1398). مکـان‌یـابی و تخصیص بهینه نقاط انتقال، بیمارستان و مراکز امدادی برای تشکیل زنجیره امدادرسانی در بحران، بـا فـرض غربـالگری مجروحـان. مدیریت صنعتی، 11 (1)، 1-20.
عقیانی، مونا؛ جبارزاده، آرمین؛ سجادی، سید جعفر (1394). ارائه یک مدل بهینه‌سازی استوار جهت طراحی شبکه زنجیره تأمین خون در شرایط بحران با در نظرگرفتن قابلیت اطمینان. نشریه مهندسی و مدیریت کیفیت، 5(2)، 85-96.
 
References
 
Abolghasemi, H., Radfar, M. H., Tabatabaee, M., Hosseini-Divkolayee, N. S., & Burkle Jr, F. M. (2008). Revisiting blood transfusion preparedness: Experience from the Bam Earthquake. Prehosp Disaster Med, 23(5), 391-4.
Aghezzaf, E. H., Sitompul, C., & Najid, N. M. (2010). Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research, 37(5), 880-889.
Aghyani, M., Jabbarzadeh, A., Sadjadi, J. (2015). A robust optimization model for designing blood supply chain in crisis considering reliability. Journal of Quality Management & Engineering, 5 (2). 85-96.
Amiri, M., Hosseini Dehshiri, S., Yousefi Hanoomarvar, A. (2018). Determining the Optimal Combination of LARG Supply Chain Strategies Using SWOT Analysis, Multi-criteria Decision-making Techniques and Game Theory. Industrial Management Journal, 10(2), 221-246. (in Persian)
Armaghan, N., & Pazani, N. Y. (2019). A Model For Designing A Blood Supply Chain Network To Earthquake Disasters (Case Study: Tehran City). International Journal for Quality Research, 13(3), 605-624.
Beigi, S., Yaghoubi, H., Karimi, H. (2020). Optimal Allocation of City Districts to Emergency Resettlement Sites, Hospitals, and Clinics after the Earthquake (Case Study: Bojnord City). Industrial Management Journal, 12(1), 82-110. (in Persian)
Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
Bozorgi, A., Kazemi, N., Baderi, Z. (2015). A Reliable multi-objective location-allocation model for blood supply systems under disruptions. Advances in Mathematical Modeling, 4(2), 1-25. (in Persian)
Caram‐Deelder, C., Kreuger, A. L., Jacobse, J., van der Bom, J. G., & Middelburg, R. A. (2016). Effect of platelet storage time on platelet measurements: a systematic review and meta‐analyses. Vox sanguinis, 111(4), 374-382.
Chen, S., & Wang, C. (2019). Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters. Discrete Dynamics in Nature and Society, 2019(1), 1-28.
Cheraghi, S., & Hosseini-Motlagh, S. M. (2018). Responsive and reliable injured-oriented blood supply chain for disaster relief: a real case study. Annals of Operations Research, 1-39.
Cheraghi, S., Hosseini-Motlagh, S. M., & Ghatreh Samani, M. (2017). Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering, 10(special issue on healthcare), 55-80.
Chopra, S., & Sodhi, M. S. (2004). Supply-chain breakdown. MIT Sloan management review, 46(1), 53-61.
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1-13.
Derikvand, H., Hajimolana, S. M., & Jabarzadeh, A. (2019). A fuzzy stochastic bi-objective model for blood provision in disastrous time. Journal of Industrial and Systems Engineering, 12(2), 223-245.
Donadoni, M., Roden, S., Scholten, K., Stevenson, M., Caniato, F., van Donk, D. P., & Wieland, A. (2019). The Future of Resilient Supply Chains. In Revisiting Supply Chain Risk (pp. 169-186). Springer, Cham.
Ebrahimi, M., Safari, H., Sadeghi Moghaddam, M. (2017). A Mathematical Model for Power Generation Expansion Planning with Considering Distributed Generation Units and Decreasing Carbon Dioxide. Industrial Management Journal, 9(4), 563-586.
Ellis, S. C., Henry, R. M., & Shockley, J. (2010). Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment. Journal of operations management, 28(1), 34-46.
Ensafian, H., Yaghoubi, S., & Yazdi, M. M. (2017). Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty. Computers & Chemical Engineering, 106, 355-372.
Fahimnia, B., & Jabbarzadeh, A. (2016). Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research Part E: Logistics and Transportation Review, 91, 306-324.
Fazli-Khalaf, M., Fathollahzadeh, K., Mollaei, A., Naderi, B., & Mohammadi, M. (2019). A robust possibilistic programming model for water allocation problem. RAIRO-Operations Research, 53(1), 323-338.
Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research, 1-31.
Ganguly, A., Chatterjee, D., & Rao, H. (2018). The role of resiliency in managing supply chains disruptions. In Supply Chain Risk Management (pp. 237-251). Springer, Singapore.
Gholami-Zanjani, S. M., Jabalameli, M. S., Klibi, W., & Pishvaee, M. S. (2021). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research, 59(1), 301-324.
Gorissen, B. L. (2015). Robust fractional programming. Journal of Optimization Theory and Applications, 166(2), 508-528.
Haeri, A., Hosseini‐Motlagh, S. M., Ghatreh Samani, M. R., & Rezaei, M. (2020). A mixed resilient‐efficient approach toward blood supply chain network design. International Transactions in Operational Research, 27(4), 1962-2001.
Hasanzadeh, M., Maleki, M., Jahangirnia, H., Gholami Jamkarani, R. (2020). Identifying and Prioritizing the Factors Affecting the Resilience of the Iranian Capital Market. Industrial Management Journal, 12(1), 172-205. (in Persian)
Hosseini-Motlagh, S. M., Cheraghi, S., & Ghatreh Samani, M. (2016). A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research, 27(4), 425-444.
Hosseini-Motlagh, S. M., Samani, M. R. G., & Homaei, S. (2020). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1085-1104.
Huang, Y., Pardalos, P. M., & Zheng, Q. P. (2017). Electrical power unit commitment: deterministic and two-stage stochastic programming models and algorithms. Springer.
Inagaki, M., & Kuroda, K. (2007). Supply chain management in Japan. Supply and Demand Chain Executive, 8(3), 68.
Izadyar, M., Toloie-Eshlaghy, A., Seyed Hosseini, S. (2020). A Model of Sustainability Performance Assessment of LARG Supply Chain Management Practices in Automotive Supply Chain Using System Dynamics. Industrial Management Journal, 12(1), 111-142. (in Persian)
Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225-244.
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 178-191.
Jabbarzadeh, A., Oghyani, M., Sadjadi, J. (2015). Provide a robust optimization model for designing the blood supply chain network in crisis situations with regard to reliability. Quality Engineering and Management Journal, 5 (2), 85-96. (in Persian)
Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133.
Kamyabniya, A., Lotfi, M. M., Cai, H., Hosseininasab, H., Yaghoubi, S., & Yih, Y. (2019). A two-phase coordinated logistics planning approach to platelets provision in humanitarian relief operations. IISE Transactions, 51(1), 1-21.
Khalilpourazari, S., & Khamseh, A. A. (2017). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Annals of Operations Research, 1-39.
Ma, H. L., & Wong, W. H. C. (2018). A fuzzy-based House of Risk assessment method for manufacturers in global supply chains. Industrial Management & Data Systems, 118(7), 1463-1476.
Melnyk,S.A.,Closs,D.J.,Griffis,S.E.,Zobel,C.W.,Macdonald,J.R.,2014.Undestanding supplychainresilience.SupplyChainManag.Rev.18(1), 34–41.
Morris, P. (2019). Responding to disruptions in the pharmaceutical supply chain. Clinical Pharmacist, 11(2).
Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680-708.
Neise, F. (2008). Risk management in stochastic integer programming. Vieweg+ Teubner.
Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. International journal of production economics, 118(1), 168-174.
Or, I., & Pierskalla, W. P. (1979). A transportation location-allocation model for regional blood banking. AIIE transactions, 11(2), 86-95.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2015). A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making. International Journal of Production Research, 53(24), 7191-7212.
Paydar, M. M., Habibi, M. (2016). Designing a multi-objective multi-level model of blood supply chain in crisis. Supply Chain Management, 17(50), 82-95. (in Persian)
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.
Rahmani, D. (2019). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research, 283(1), 613-641.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283(1), 1413-1462.
Samani, M. R. G., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450-471.
Sangari, M. S., & Dashtpeyma, M. (2019). An integrated framework of supply chain resilience enablers: a hybrid ISM-FANP approach. International Journal of Business Excellence, 18(2), 242-268.‏
Seyyedi, S., Khatami, M., Amiri, M., Taghavi Fard, M. (2019). Positioning and Optimized Allocation of Transfer Points, Hospitals and Emergency Services Centers to Organize a Crisis Relief Chain, Assuming Screening of Injuries. Industrial Management Journal, 11(1), 1-20. (in Persian)
Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 21-28.
Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan management review, 47(1), 41.
Shekarian, M. (2018). A Literature Review on the Impact of Antecedents of Supply Chain Resilience on Mitigating Supply Chain Disruptions. In Conference: Annual Meeting of the Decision Sciences Institute Proceedings At: Chicago, IL, USA. International Journal of Production Research, 1, 56-65.
Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., ... & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375-390.
Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89-109.
Sodhi, M. S., & Tang, C. S. (2012). Supply Chain Management. In Managing supply chain risk (pp. 3-12). Springer, Boston, MA.
Thomas, B., Anania, K., DeCicco, A., & Hamm, J. A. (2019). Toward Resiliency in the Joint Blood Supply Chain. Rand health quarterly, 8(3).
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.
Verma, M., Dahiya, K., Malik, D., Sehgal, P. K., Devi, R., Soni, A., & Ghalaut, V. G. (2015). Effect of blood storage on complete biochemistry. J Blood Disord Transfus, 6(6), 1-4.
Wagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of purchasing and supply management, 12(6), 301-312.
Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.
Waters, D. (2007). Supply chain risk management: vulnerability and resilience in logistics. Kogan Page Publishers.
Xiao, R., Yu, T., & Gong, X. (2012). Modeling and simulation of ant colony's labor division with constraints for task allocation of resilient supply chains. International Journal on Artificial Intelligence Tools, 21(03), 1240014.
Yaghoubi, S., Hosseini-Motlagh, S. M., Cheraghi, S., & Larimi, N. G. (2019). Designing a robust demand-differentiated platelet supply chain network under disruption and uncertainty. Journal of Ambient Intelligence and Humanized Computing, 1-28.
Zendehdel., M, Bozorgi-Amiri., A., & Omrani., H. (2014). A location Model for Blood Donation Camps with Consideration of Disruption. Journal of Industrial Engineering, 48(1), 33-43. (in Persian)