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Kan Tedarik Zinciri Ağ Tasarımı Problemine Bulanık Karma Tamsayılı Programlama Modeli Önerisi

Year 2021, Volume 25, Issue 2, 851 - 868, 24.05.2021

Abstract

Bu çalışma, kan tedarik zinciri ağ tasarımı problemine yeni bir bulanık karma tam sayılı programlama modeli önerisi sunmaktadır. Önerilen konum-tahsis modeli kan tedarik zincirlerinde bulunan sabit birimler ile mobil birimleri birlikte değerlendirerek sistemi optimize etmektedir. Kan tedarik zincirlerinde en önemli belirsizlik kaynağı arz ve talep miktarlarıdır. Bu belirsizliğin üstesinden gelebilmek amacıyla model Verdegay Yaklaşımından faydalanılarak bulanık model olarak tanımlanmıştır. Gerçek hayat uygulaması olarak Kızılay’ın bir kısım Doğu Anadolu ve bir kısım Güney Doğu Anadolu bölgelerini kapsayan tedarik zincirleri incelenmiştir. Bulanık modelden elde edilen parametrik çözümler ile karar vericilere faydalı tavsiyelerde ve değerlendirmelerde bulunulmuştur.

References

  • Beliën, J. and Forcé, H., (2012). “Supply chain management of blood products: A literature review”, European Journal of Operational Research, 217: 1-16.
  • Bruno, G., Diglio, A., Piccolo, C., Cannavacciuolo, L., (2019). “Territorial reorganization of regional blood management systems: Evidences from an Italian case study”, Omega, 89: 54-70.
  • 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.
  • Dillon, M., Oliveira, F., Abbasi, B., (2017). “A two-stage stochastic programming model for inventory management in the blood supply chain”, International Journal of Production Economics, 187: 27-41.
  • Ensafian, H. and Yaghoubi, S., (2017). “Robust optimization model for integrated procurement production and distribution in platelet supply chain”, Transportation Research Part E, 103: 32-55.
  • 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 and Chemical Engineering, 106: 355-372.
  • 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.
  • Hamdan, B., and Diabat, A., (2020b). “Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation”, Transportation Research Part E, 134: 101764.
  • Hamdan, B., and Diabat, A., (2020a). “Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management”, Computers and Operations Research, 119: 104941.
  • Hamdan, B., and Diabat, A., (2019). “A two-stage multi-echelon stochastic blood supply chain problem”, Computers and Operations Research, 101: 130-143.
  • Heidari-Fathian, H. and Pasandideh, S.H.R., (2018). “Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation”, Computers & Industrial Engineering, 122: 95-105.
  • Jafarkhan, F. and Yaghoubi, S., (2018). “An efficient solution method for the flexible and robust inventory-routing of red blood cells”, Computers & Industrial Engineering, 117: 191-206.
  • Karadağ, İ., Keskin, M.E., Yiğit, V., (2021). “Re-design of a blood supply chain organization with mobile units”, Soft Computing, 25, 6311-6327, https://doi.org/10.1007/s00500-021-05618-3
  • Lai, Y.-J., and Hwang, C.-L., (1992). “Fuzzy Mathematical Programming: Methods and Applications”, Berlin, Springer-Verlag.
  • 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.
  • Osorio AF, Brailsford SC, Smith HK, (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.
  • Pirabán, A., Guerrero, W.J., Labadie, N., (2019). “Survey on blood supply chain management: Models and methods”, Computers and Operations Research, 112: 104756.
  • Ramezanian, R. and Behboodi, Z., (2017). “Blood supply chain network design under uncertainties in supply and demand considering social aspects”, Transportation Research Part E, 104: 69-82.
  • Samani, M.R.G., and Hosseini-Motlagh, S.M., (2019). “An enhanced procedure for managing blood supply chain under disruptions and uncertainties”, Annals of Operations Research, 283(1-2): 1413-1462.
  • Şahin, G., Süral, H., Meral, S., (2007). “Locational analysis for regionalization of Turkish Red Crescent blood services”, Computers and Operations Research, 34: 692– 704.
  • Verdegay, J. L., (1984). “A Dual Approach to Solve the Fuzzy Linear Programming Problem”, Fuzzy Sets and Systems, 14, 131-141.
  • Zahiri B, Torabi SA, Mohammadi M, Aghabegloo M, (2018). “A multistage stochastic programming approach for blood supply chain planning”, Computers & Industrial Engineering, 122, 1–14.
  • Zahiri, B. and Pishvaee, M.S., (2017). “Blood supply chain network design considering blood group compatibility under uncertainty”, International Journal of Production Research, 55(7): 2013-2033.
  • Zahiri, B., Torabi, S.A., Mousazadeh, M., Mansouri, S.A., (2015). “Blood collection management: Methodology and application”, Applied Mathematical Modelling, 39: 7680 –7696.
  • Zimmermann, H.-J., (1976), “Description and optimization of fuzzy systems”, International Journal of General Systems, 2, 209-215.
  • Zimmermann, H.-J. (1978). “Fuzzy programming and linear programming with several objective functions”, Fuzzy Sets and Systems, 1, 45-55.

Fuzzy Mixed-Integer Programming Model Suggestion for The Blood Supply Chain Network Design Problem

Year 2021, Volume 25, Issue 2, 851 - 868, 24.05.2021

Abstract

This paper presents a novel fuzzy mixed-integer programming model to the blood supply chain network design problem. The proposed location-allocation model optimizes the system by evaluating the fixed units in the blood supply chains and mobile units together. The most important source of uncertainty in blood supply chains is the amount of supply and demand. In order to overcome this uncertainty, the model is defined as a fuzzy model by using Verdegay Approach. As a real-life application, the supply chain of the Red Crescent covering a part of Eastern Anatolia and a part of South-Eastern Anatolia regions is examined. With the parametric solutions obtained from fuzzy model, useful recommendations and evaluations are made to the decision-makers.

References

  • Beliën, J. and Forcé, H., (2012). “Supply chain management of blood products: A literature review”, European Journal of Operational Research, 217: 1-16.
  • Bruno, G., Diglio, A., Piccolo, C., Cannavacciuolo, L., (2019). “Territorial reorganization of regional blood management systems: Evidences from an Italian case study”, Omega, 89: 54-70.
  • 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.
  • Dillon, M., Oliveira, F., Abbasi, B., (2017). “A two-stage stochastic programming model for inventory management in the blood supply chain”, International Journal of Production Economics, 187: 27-41.
  • Ensafian, H. and Yaghoubi, S., (2017). “Robust optimization model for integrated procurement production and distribution in platelet supply chain”, Transportation Research Part E, 103: 32-55.
  • 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 and Chemical Engineering, 106: 355-372.
  • 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.
  • Hamdan, B., and Diabat, A., (2020b). “Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation”, Transportation Research Part E, 134: 101764.
  • Hamdan, B., and Diabat, A., (2020a). “Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management”, Computers and Operations Research, 119: 104941.
  • Hamdan, B., and Diabat, A., (2019). “A two-stage multi-echelon stochastic blood supply chain problem”, Computers and Operations Research, 101: 130-143.
  • Heidari-Fathian, H. and Pasandideh, S.H.R., (2018). “Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation”, Computers & Industrial Engineering, 122: 95-105.
  • Jafarkhan, F. and Yaghoubi, S., (2018). “An efficient solution method for the flexible and robust inventory-routing of red blood cells”, Computers & Industrial Engineering, 117: 191-206.
  • Karadağ, İ., Keskin, M.E., Yiğit, V., (2021). “Re-design of a blood supply chain organization with mobile units”, Soft Computing, 25, 6311-6327, https://doi.org/10.1007/s00500-021-05618-3
  • Lai, Y.-J., and Hwang, C.-L., (1992). “Fuzzy Mathematical Programming: Methods and Applications”, Berlin, Springer-Verlag.
  • 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.
  • Osorio AF, Brailsford SC, Smith HK, (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.
  • Pirabán, A., Guerrero, W.J., Labadie, N., (2019). “Survey on blood supply chain management: Models and methods”, Computers and Operations Research, 112: 104756.
  • Ramezanian, R. and Behboodi, Z., (2017). “Blood supply chain network design under uncertainties in supply and demand considering social aspects”, Transportation Research Part E, 104: 69-82.
  • Samani, M.R.G., and Hosseini-Motlagh, S.M., (2019). “An enhanced procedure for managing blood supply chain under disruptions and uncertainties”, Annals of Operations Research, 283(1-2): 1413-1462.
  • Şahin, G., Süral, H., Meral, S., (2007). “Locational analysis for regionalization of Turkish Red Crescent blood services”, Computers and Operations Research, 34: 692– 704.
  • Verdegay, J. L., (1984). “A Dual Approach to Solve the Fuzzy Linear Programming Problem”, Fuzzy Sets and Systems, 14, 131-141.
  • Zahiri B, Torabi SA, Mohammadi M, Aghabegloo M, (2018). “A multistage stochastic programming approach for blood supply chain planning”, Computers & Industrial Engineering, 122, 1–14.
  • Zahiri, B. and Pishvaee, M.S., (2017). “Blood supply chain network design considering blood group compatibility under uncertainty”, International Journal of Production Research, 55(7): 2013-2033.
  • Zahiri, B., Torabi, S.A., Mousazadeh, M., Mansouri, S.A., (2015). “Blood collection management: Methodology and application”, Applied Mathematical Modelling, 39: 7680 –7696.
  • Zimmermann, H.-J., (1976), “Description and optimization of fuzzy systems”, International Journal of General Systems, 2, 209-215.
  • Zimmermann, H.-J. (1978). “Fuzzy programming and linear programming with several objective functions”, Fuzzy Sets and Systems, 1, 45-55.

Details

Primary Language Turkish
Subjects Social
Journal Section Makaleler
Authors

Vecihi YİĞİT (Primary Author)
Atatürk Üniversitesi
0000-0003-0625-3140
Türkiye


İlker KARADAĞ
ATATÜRK ÜNİVERSİTESİ
0000-0002-7048-8529
Türkiye


Doğan DURNA
ATATÜRK ÜNİVERSİTESİ, DİŞ HEKİMLİĞİ FAKÜLTESİ
0000-0001-5341-6024
Türkiye

Supporting Institution Atatürk Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi
Project Number FOA-2020-8742
Thanks Bu çalışma Atatürk Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimince Desteklenmiştir. Proje numarası: FOA-2020-8742 (This work was supported by Research Fund of the Ataturk University. Project Number: FOA-2020-8742)
Publication Date May 24, 2021
Published in Issue Year 2021, Volume 25, Issue 2

Cite

APA Yiğit, V. , Karadağ, İ. & Durna, D. (2021). Kan Tedarik Zinciri Ağ Tasarımı Problemine Bulanık Karma Tamsayılı Programlama Modeli Önerisi . Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi , 25 (2) , 851-868 . Retrieved from https://dergipark.org.tr/en/pub/ataunisosbil/issue/62432/938670

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