طراحی شبکه زنجیره تأمین یکپارچه خون تحت شرایط عدم‌قطعیت با درنظرگرفتن انتقالات جانبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، پردیس دانشکده‌های فنی، دانشگاه تهران.

2 دانشیار، پردیس دانشکده‌های فنی، دانشگاه تهران.

چکیده

یکی از کلیدی­‌ترین بخش‌­های یک سیستم سلامت، زنجیره تأمین خون است که سهم زیادی از هزینه‌­های این سیستم را به خود اختصاص داده است؛ بنابراین هرگونه پیشرفتی در عملکرد زنجیره تأمین خون می‌­تواند به‌طور چشمگیری به بهبود کارایی و صرفه­‌جویی در هزینه­‌های سیستم­‌های سلامت بیانجامد. در این پژوهش، یک مدل دوهدفه برای طراحی شبکه زنجیره تأمین خون با هدف کاهش هزینه تأسیس تسهیلات ثابت و موقت، هزینه‌های انتقال فرآورده‌های خونی و حداقل­‌کردن حداکثر میزان کمبود ارائه ‌شده است. با توجه به عدم‌قطعیت‌های موجود در عرضه و تقاضا، برای مقابله با کمبود و افزایش سطح پاسخگویی، انتقالات جانبی بین بیمارستان‌ها در نظر گرفته ‌شده است. مدل غیرقطعی به کمک روش فازی خیمنز به مدل قطعی تبدیل ‌شده و در ادامه مدل دو­هدفه با استفاده از روش ترابی و حسینی، به مدل تک‌­هدفه تبدیل ‌شده است. نتایج محاسباتی حاصل از مدل نشان می‌دهد در مدل فازی به‌­دلیل وجود α-cut، مدل انعطاف‌پذیرتر شده؛ درحالی‌که در شرایط قطعیت، به‌­دلیل قطعی‌­بودن مقادیر پارامترها، اجازه انعطاف‌پذیری به مقادیر پارامترهای مدل داده نمی‌شود. مدل فازی علاوه بر نزدیک‌بودن به محیط واقعی، سبب می‌شود مدیران با توجه به عدم‌قطعیت موجود بر اساس میزان درجه مطلوبیت لازم اقدام به تصمیم‌گیری کنند. 

کلیدواژه‌ها

عنوان مقاله [English]

Integrate Blood Supply Chain Network Design with Considering Lateral Transshipment under Uncertainty

نویسندگان [English]

  • Mansour Doodman 1
  • Ali Bozorgi Amiri 2
1 M.Sc., College of Engineering, University of Tehran.
2 Associate Professor, College of Engineering, University of Tehran.
چکیده [English]

One of the most critical sections in a healthcare system, is blood supply chain that has owned significant portion of the costs of this system. So, any improvement in the blood supply chain performance can impressively lead to efficiency improvement and saving in the healthcare systems’ costs. In this research, a bi-objective model is presented for blood supply chain network design with aiming at decreasing main and temporary facilities opening cost, transportation costs of blood-derived products and minimizing the maximum shortage. Due to uncertainty in supply and demand, for dealing with shortage and increasing of responsiveness, lateral transshipment among hospitals is considered. Uncertain model is converted to deterministic model using Jiménez fuzzy and then the bi-objective model is transformed to a single objective model using Torabi-Hassini’s method. Computational results obtained from the model shows that in fuzzy model, because of -cut, the model is more flexible while in the certainty situation, because of certainty in parameters, model’s parameters value are not allowed to be flexible. Fuzzy model in addition to closeness to real environment, causes that managers make decisions based on uncertainty based on desirability. Also, model fuzzy has not significant impact on computational complexity and solving time.

کلیدواژه‌ها [English]

  • Blood Supply Chain Management
  • Location-Allocation
  • Uncertainty
  • Possibilistic Programming
  • Lateral Transshipment

مراجع

1. Arvan, M., Tavakkoli-Moghaddam, R., & Abdollahi, M. (2015). Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 3(1), 57-68.
2. Barati, M. (2017). The Impact of Supply Chain Relationships Management on Competitiveness in Iranian Small and Medium-sized Enterprises in Automotive Parts Industry. Journal of Industrial Management Perspective, 7(2), 169-188 (in Persian).
3. Beliën, J., and Force, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
4. Cheraghi, S., & Hosseini-Motlagh, S.M. (2017). Optimal Blood Transportation in Disaster Relief Considering Facility Disruption and Route Reliability under Uncertainty. International Journal of Transportation Engineereing, 4(3), 225-254.
5. Cetin, E., & Sarul, L. S. (2009). A blood bank location model: A multi objective approach. European Journal of Pure and Applied Mathematics, 2(1), 112-124.
6. Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Amin, S. H. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236-250.
7. Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2015). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 24, 479–502.
8. Fontaine, M.J., Chung, Y.T., & Rogers, W.M. (2009). Improving platelet supply chains through collaborations between blood centers and transfusion services. Tansfusion, 49(10), 2040-2047.
9. Fereiduni, M., and Shahanaghi, K. (2016). A robust optimization model for blood supply chain in emergency situations. International Journal of Industrial Engineering Computations, 7(4), 535-554.
10. Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141.
11. Ghandforoush, P., & Sen, T.K. (2010). A DSS to manage platelet production supply chain for regional blood centers. Decision Support Systems, 50(1), 32-42.
12. Hosseinifard, Z., and Abbasi, B. (2016). The inventory centralization impacts on sustainability of the blood supply chain. Computers & Operations Research, in press.
13. 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.
14. Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143.
15. Haijema, R., Wal, J.V.D., and van Dijk, N.M. (2007). Blood platelet production: Optimization by dynamic programming and simulation. Computers & Operations Research, 34(3), 760-779.
16. Hemmelmayr, V., Doerner, K.F., & Hartl, R.F. (2010). Vendor managed inventory for environments with stochastic product usage. European Journal of Operational Research, 202(3), 686-695.
17. Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers & Operations Research, 89, 206-212.
18. Jacobs, D. A., Silan, M. N., & Clemson, B. A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 40-50.
19. Jiménez, M., Arenas, M., Bilbao, A., & Rodriguez, M. V. (2007). Linear programming with fuzzy parameters, An interactive method resolution. European Journal of Operational Research, 177, 1599–1609.
20. Kaveh, A., & Ghobadi, M. (2017). A Multistage Algorithm for Blood Banking Supply Chain Allocation Problem. International Journal of Civil Engineering, 15(1), 103-112.
21. Mohammadian, Z., & Jabbarzadeh, A. (2016). A location-allocation model of blood supply chain under uncertainty considering disruption and transshipment between hospitals. International Conference on New Researches in Engineering Sciences.
22. Mohammadian-Behbahani, Z., Jabbarzadeh, A., & Pishvaee, M. S. (2019). A robust optimization model for sustainable blood supply chain network design under uncertainty. International Journal of Industrial and Systems Engineering, 31(4), 475-494.
23. Nagurney, A. Masoumi, A. H. & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 9, 205-231.
24. Osorio, A.F., Brailsford, S., and 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.
25. Rais, A., & Viana, A. (2011). Operations Research in Healthcare: a survey. International transactions in operational research, 18(1), 1-31.
26. Shahbandarzadeh, H., Paykam, A. (2015). Employment of a Weighted Fuzzy Multi-Objective Programming Model to Determine the Amount of Optimum Purchasing from Suppliers. Journal of Industrial Management Perspective, 5(2), 129-152 (in Persian).
27. Shaikh, R., Shambaiati, H. (2016). Locating Facilities in Uncertainty Conditions based on D Number Theory. Journal of Industrial Management Perspective, 5(4), 143-166 (in Persian).
28. Şahin, G., Süral, H., & Meral, S. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services. Computers & Operations Research, 34(3), 692-704.
29. Şahin, G., Sural, H., & Meral, S. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services. Computers & Operations Research, 34(3), 692-704.
30. Sha, Y., & Huang, J. (2012). The Multi-period Location-allocation Problem of Engineering Emergency Blood Supply Systems. Systems Engineering Procedia, 5, 21-28.
31. Syam, S.S., & Cote, M. (2010). A location–allocation model for service providers with application to not-for-profit health care organizations, Omega, 38(3-4), 157-166.
32. Shen, Z.J.M., Coullard, C., & Daskin, M.S. (2003). A Joint Location-Inventory Model. Transportation Science, 37(1), 40-55.
33. Torabi, S. A., & Hassini. E. (2008). An Interactive Possibilistic Programming Approach for Multiple Objective Supply Chain Master Planning An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
34. Van Zyl G.J.J. (1964). Inventory control for perishable commodities. Dissertation, University of North of colifornia.
35. Zahiri, B., & Pishvaee, M.S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 55(7), 2013-2033.
36. Zahiri, B., Torabi, S.A., Mousazadeh, M., & Mansouri, S.A. (2015). Blood collection management: Methodology and application. Applied Mathematical Modelling, 39(23-24), 7680-7696.
37. Zhou, Z., & Leung, L. (2011). Inventory Management of Platelets in Hospitals: Optimal Inventory Policy for Perishable Products with Regular and Optional Expedited Replenishments. Manuf. & Serv. Oper. Manag, 1(4), 420-438.
38. Zahiri, B., Torabi, S. A., Mohammadi, M., & Aghabegloo, M. (2018). A multi-stage stochastic programming approach for blood supply chain planning. Computers & Industrial Engineering, 122, 1-14.
39. Zahraee, S.M., Rouhani, J.M., & Firouzi, A. (2015). A Shahpanah Efficiency Improvement of Blood Supply Chain System Using Taguchi Method and Dynamic Simulation. Procedia Manufacturing, 2, 1-5.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار