Araştırma Makalesi
BibTex RIS Kaynak Göster

Sağlık kurumlarının ve eczanelerin taleplerine yönelik ecza deposu yer seçimi problemi

Yıl 2021, Cilt: 11 Sayı: 2, 452 - 465, 15.04.2021

Aslıhan Yıldız Selin Soner Kara Coşkun Özkan

https://doi.org/10.17714/gumusfenbil.676376
Cited By: 2

Öz

Sağlık sektöründe ecza ürünlerinin üreticiden son tüketiciye ivedi bir şekilde temin edilmesi son derece önemlidir. Sektörün tedarik ağının karmaşık yapıda olması, ecza ürünlerinin dağıtımı çalışmalarına araştırmacıların ilgisinin artmasını sağlamıştır. Çalışmada ecza ürünlerinin dağıtımını içeren tedarik ağında, farklı yapılardaki talep noktaları için ecza deposu yeri seçim problemi ele alınmıştır. Bu bağlamda problem, kapasiteli tesis yeri seçim problemi olarak ele alınmış ve probleme yönelik yeni bir matematiksel model önerilmiştir. Hazırlanan model için uygulama bölgesi olarak İstanbul/Silivri ilçesi seçilmiştir. Çalışmanın devamında önerilen matematiksel model kullanılarak minimum mesafeyle dağıtım yapılması için açılması gereken ecza depolarının yerleri belirlenmiştir. Modelin sonuçlarını analiz etmek ve doğruluğunu göstermek amacıyla problemde kullanılan parametre değerleri değiştirilerek duyarlılık analizi yapılmıştır. Sonuçlar duyarlılık analizi ile birlikte sunulmuştur.

Anahtar Kelimeler

Ecza deposu Karışık tam sayılı programlama Yer seçimi

Kaynakça

  • Arthur, J. L., Hachey, M., Sahr, K., Huso, M. and Kiester, A. R. (1997). Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis. Environmental and Ecological Statistics, 4(2), 153-165, https://doi.org/10.1023/A:1018570311399.
  • Bahadori-Chinibelagh, S., Fathollahi-Fard, A. M. and Hajiaghaei-Keshteli, M. (2019). Two constructive algorithms to address a multi-depot home healthcare routing problem. IETE Journal of Research, 1-7, https://doi.org/10.1080/03772063.2019.1642802.
  • Balinski, M. L. (1965). Integer programming: methods, uses, computations. Management Science, 12(3), 253-313, https://doi.org/10.1287/mnsc.12.3.253.
  • Chen, X., Yang, H. and Wang, X. (2019). Effects of price cap regulation on the pharmaceutical supply chain. Journal of Business Research, 97, 281-290, https://doi.org/10.1016/j.jbusres.2018.01.030.
  • Friemann, F. and Schönsleben, P. (2016). Reducing global supply chain risk exposure of pharmaceutical companies by further incorporating warehouse capacity planning into the strategic supply chain planning process. Journal of Pharmaceutical Innovation, 11(2), 162-176, https://doi.org/10.1007/s12247-016-9249-6.
  • Haial, A., Berrado, A. and Benabbou, L. (2019). Redesigning a transportation network: the case of a pharmaceutical supply chain. International Journal of Logistics Systems and Management, 35(1), 90-118, https://doi.org/10.1504/IJLSM.2020.10025514.
  • Haight, R. G., Revelle, C. S. and Snyder, S. A. (2000). An integer optimization approach to a probabilistic reserve site selection problem. Operations Research, 48(5), 697-708, https://doi.org/10.1287/opre.48.5.697.12411.
  • Jelokhani-Niaraki, M. and Malczewski, J. (2015). Decision complexity and consensus in web-based spatial decision making: a case study of site selection problem using gis and multicriteria analysis. Cities, 45, 60-70, https://doi.org/10.1016/j.cities.2015.03.007.
  • Ji, Y.i (2019). Optimal scheduling in home health care: pharmacy-hospital-patient's vehicle routing problem. In Proceedings of the 3rd International Conference on Computer Science and Application Engineering (pp. 1-6), https://doi.org/10.1145/3331453.3361310.
  • Koc, A., Turk, S. and Şahin, G. (2019). Multi-criteria of wind-solar site selection problem using a gis-ahp based approach with an application in Igdir province/Turkey. Environmental Science and Pollution Research, 26(31), 32298-32310, https://doi.org/10.1007/s11356-019-06260-1.
  • Li, X., He, J. and Liu, X. (2009). Intelligent gis for solving high‐dimensional site selection problems using ant colony optimization techniques. International Journal of Geographical Information Science, 23(4), 399-416, https://doi.org/10.1080/13658810801918491 .
  • Lin, M., Huang, C. and Xu, Z. (2020). MULTIMOORA based mcdm model for site selection of car sharing station under picture fuzzy environment. Sustainable Cities and Society, 53, 101873, https://doi.org/10.1016/j.scs.2019.101873.
  • Lubis, A. and Mawengkang, H. (2020). A capacitated heterogeneous vehicle routing problem for pharmaceutical products delivery. Systematic Reviews in Pharmacy, 11(4), 738-741.
  • Masoumi, A. H., Yu, M. and Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research Part E: Logistics and Transportation Review, 48(4), 762-780, https://doi.org/10.1016/j.tre.2012.01.001.
  • Myerson, A. S., Krumme, M., Nasr, M., Thomas, H. and Braatz, R. D. (2015). Control systems engineering in continuous pharmaceutical manufacturing. 2014 Continuous Manufacturing Symposium. Journal of Pharmaceutical Sciences, 104(3), 832-839, https://doi.org/10.1002/jps.24311.
  • Nematollahi, M., Hosseini-Motlagh, S. M. and Heydari, J. (2017). Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. Journal of Cleaner Production, 142, 3956-3969, https://doi.org/10.1016/j.jclepro.2016.10.062.
  • Oufella, S. and Hamdadou, D. (2018). A collaborative spatial decision support system applied to site selection problems. International Journal of Applied Management Science, 10(2), 127-156, https://doi.org/10.1504/IJAMS.2018.092078.
  • Papageorgiou, L. G., Rotstein, G. E. and Shah, N. (2001). Strategic supply chain optimization for the pharmaceutical industries. Industrial & Engineering Chemistry Research, 40(1), 275-286, https://doi.org/10.1021/ie990870t.
  • Pedroso, M. C. and Nakano, D. (2009). Knowledge and information flows in supply chains: a study on pharmaceutical companies. International Journal of Production Economics, 122(1), 376-384, https://doi.org/10.1016/j.ijpe.2009.06.012.
  • Redi, A. A. N. P., Maula, F. R., Kumari, F., Syaveyenda, N. U., Ruswandi, N., Khasanah, A. U. and Kurniawan, A. C. (2020). Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution. Jurnal Sistem dan Manajemen Industri, 4(1), 41-49, https://doi.org/10.30656/jsmi.v4i1.2215.
  • Rossetti, C. L., Handfield, R. and Dooley, K. J. (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physical Distribution & Logistics Management, 41(6), 601-622, https://doi.org/10.1108/09600031111147835.
  • Shah, N. (2004). Pharmaceutical supply chains: key issues and strategies for optimisation. Computers & Chemical Engineering, 28(6-7), 929-941, https://doi.org/10.1016/j.compchemeng.2003.09.022.
  • Uthayakumar, R. and Priyan, S. (2013). Pharmaceutical supply chain and inventory management strategies: optimization for a pharmaceutical company and a hospital. Operations Research for Health Care, 2(3), 52-64, https://doi.org/10.1016/j.orhc.2013.08.001.
  • Weraikat, D., Zanjani, M. K., and Lehoux, N., (2016). Coordinating a green reverse supply chain in pharmaceutical sector by negotiation. Computers & Industrial Engineering, 93, 67-77, https://doi.org/10.1016/j.cie.2015.12.026.
  • Wong, C. K., Fung, I. W. H. and Tam, C. M. (2010). Comparison of using mixed-integer programming and genetic algorithms for construction site facility layout planning. Journal Of Construction Engineering And Management, 136(10), 1116-1128, https://doi.org/10.1061/(ASCE)CO.1943-7862.0000214.
  • Wu, Y., Zhang, B., Wu, C., Zhang, T. and Liu, F. (2019). Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: a case in China. Renewable Energy, https://doi.org/10.1016/j.renene.2019.05.131.
  • Yap, J. Y. L., Ho, C. C. and Ting, C. Y. (2019). A systematic review of the applications of multi-criteria decision-making methods in site selection problems. Built Environment Project and Asset Management, https://doi.org/10.1108/BEPAM-05-2018-0078.
  • Yu, X., Li, C., Shi, Y. and Yu, M. (2010). Pharmaceutical supply chain in China: current issues and implications for health system reform. Health Policy, 97(1), 8-15, https://doi.org/10.1016/j.healthpol.2010.02.010.
  • Zahiri, B., Jula, P. and Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283, https://doi.org/10.1016/j.ins.2017.09.046.

Pharmaceutical warehouse site selection problem considering the demands of medical ınstitutions and pharmacies

Yıl 2021, Cilt: 11 Sayı: 2, 452 - 465, 15.04.2021

Aslıhan Yıldız Selin Soner Kara Coşkun Özkan

https://doi.org/10.17714/gumusfenbil.676376
Cited By: 2

Öz

It is extremely important that pharmaceutical products are effectively supplied from the producer to the end consumer in the health sector, The complexity of the supply network of the sector has increased the interest of researchers in the distribution of pharmaceutical products. In this study, site selection problem for pharmaceutical warehouse for the demand points of different structures is discussed in the supply network, which includes the distribution of pharmaceutical products, In this context, the problem is considered as the capacited site selection problem and a novel mathematical model is proposed for the problem. İstanbul / Silivri district is chosen as the application area for the proposed mode. After, the sites of the pharmaceutical warehouses that should be opened in order to distribute with minimum distance were determined using the proposed mathematical model. Sensitivity analysis is performed by changing the parameter values used in the problem in order to analyze the results of the model and to demonstrate its accuracy. The results are presented with sensitivity analysis.

Anahtar Kelimeler

Pharmaceutical warehouse Mixed integer programming Site selection

Kaynakça

  • Arthur, J. L., Hachey, M., Sahr, K., Huso, M. and Kiester, A. R. (1997). Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis. Environmental and Ecological Statistics, 4(2), 153-165, https://doi.org/10.1023/A:1018570311399.
  • Bahadori-Chinibelagh, S., Fathollahi-Fard, A. M. and Hajiaghaei-Keshteli, M. (2019). Two constructive algorithms to address a multi-depot home healthcare routing problem. IETE Journal of Research, 1-7, https://doi.org/10.1080/03772063.2019.1642802.
  • Balinski, M. L. (1965). Integer programming: methods, uses, computations. Management Science, 12(3), 253-313, https://doi.org/10.1287/mnsc.12.3.253.
  • Chen, X., Yang, H. and Wang, X. (2019). Effects of price cap regulation on the pharmaceutical supply chain. Journal of Business Research, 97, 281-290, https://doi.org/10.1016/j.jbusres.2018.01.030.
  • Friemann, F. and Schönsleben, P. (2016). Reducing global supply chain risk exposure of pharmaceutical companies by further incorporating warehouse capacity planning into the strategic supply chain planning process. Journal of Pharmaceutical Innovation, 11(2), 162-176, https://doi.org/10.1007/s12247-016-9249-6.
  • Haial, A., Berrado, A. and Benabbou, L. (2019). Redesigning a transportation network: the case of a pharmaceutical supply chain. International Journal of Logistics Systems and Management, 35(1), 90-118, https://doi.org/10.1504/IJLSM.2020.10025514.
  • Haight, R. G., Revelle, C. S. and Snyder, S. A. (2000). An integer optimization approach to a probabilistic reserve site selection problem. Operations Research, 48(5), 697-708, https://doi.org/10.1287/opre.48.5.697.12411.
  • Jelokhani-Niaraki, M. and Malczewski, J. (2015). Decision complexity and consensus in web-based spatial decision making: a case study of site selection problem using gis and multicriteria analysis. Cities, 45, 60-70, https://doi.org/10.1016/j.cities.2015.03.007.
  • Ji, Y.i (2019). Optimal scheduling in home health care: pharmacy-hospital-patient's vehicle routing problem. In Proceedings of the 3rd International Conference on Computer Science and Application Engineering (pp. 1-6), https://doi.org/10.1145/3331453.3361310.
  • Koc, A., Turk, S. and Şahin, G. (2019). Multi-criteria of wind-solar site selection problem using a gis-ahp based approach with an application in Igdir province/Turkey. Environmental Science and Pollution Research, 26(31), 32298-32310, https://doi.org/10.1007/s11356-019-06260-1.
  • Li, X., He, J. and Liu, X. (2009). Intelligent gis for solving high‐dimensional site selection problems using ant colony optimization techniques. International Journal of Geographical Information Science, 23(4), 399-416, https://doi.org/10.1080/13658810801918491 .
  • Lin, M., Huang, C. and Xu, Z. (2020). MULTIMOORA based mcdm model for site selection of car sharing station under picture fuzzy environment. Sustainable Cities and Society, 53, 101873, https://doi.org/10.1016/j.scs.2019.101873.
  • Lubis, A. and Mawengkang, H. (2020). A capacitated heterogeneous vehicle routing problem for pharmaceutical products delivery. Systematic Reviews in Pharmacy, 11(4), 738-741.
  • Masoumi, A. H., Yu, M. and Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research Part E: Logistics and Transportation Review, 48(4), 762-780, https://doi.org/10.1016/j.tre.2012.01.001.
  • Myerson, A. S., Krumme, M., Nasr, M., Thomas, H. and Braatz, R. D. (2015). Control systems engineering in continuous pharmaceutical manufacturing. 2014 Continuous Manufacturing Symposium. Journal of Pharmaceutical Sciences, 104(3), 832-839, https://doi.org/10.1002/jps.24311.
  • Nematollahi, M., Hosseini-Motlagh, S. M. and Heydari, J. (2017). Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. Journal of Cleaner Production, 142, 3956-3969, https://doi.org/10.1016/j.jclepro.2016.10.062.
  • Oufella, S. and Hamdadou, D. (2018). A collaborative spatial decision support system applied to site selection problems. International Journal of Applied Management Science, 10(2), 127-156, https://doi.org/10.1504/IJAMS.2018.092078.
  • Papageorgiou, L. G., Rotstein, G. E. and Shah, N. (2001). Strategic supply chain optimization for the pharmaceutical industries. Industrial & Engineering Chemistry Research, 40(1), 275-286, https://doi.org/10.1021/ie990870t.
  • Pedroso, M. C. and Nakano, D. (2009). Knowledge and information flows in supply chains: a study on pharmaceutical companies. International Journal of Production Economics, 122(1), 376-384, https://doi.org/10.1016/j.ijpe.2009.06.012.
  • Redi, A. A. N. P., Maula, F. R., Kumari, F., Syaveyenda, N. U., Ruswandi, N., Khasanah, A. U. and Kurniawan, A. C. (2020). Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution. Jurnal Sistem dan Manajemen Industri, 4(1), 41-49, https://doi.org/10.30656/jsmi.v4i1.2215.
  • Rossetti, C. L., Handfield, R. and Dooley, K. J. (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physical Distribution & Logistics Management, 41(6), 601-622, https://doi.org/10.1108/09600031111147835.
  • Shah, N. (2004). Pharmaceutical supply chains: key issues and strategies for optimisation. Computers & Chemical Engineering, 28(6-7), 929-941, https://doi.org/10.1016/j.compchemeng.2003.09.022.
  • Uthayakumar, R. and Priyan, S. (2013). Pharmaceutical supply chain and inventory management strategies: optimization for a pharmaceutical company and a hospital. Operations Research for Health Care, 2(3), 52-64, https://doi.org/10.1016/j.orhc.2013.08.001.
  • Weraikat, D., Zanjani, M. K., and Lehoux, N., (2016). Coordinating a green reverse supply chain in pharmaceutical sector by negotiation. Computers & Industrial Engineering, 93, 67-77, https://doi.org/10.1016/j.cie.2015.12.026.
  • Wong, C. K., Fung, I. W. H. and Tam, C. M. (2010). Comparison of using mixed-integer programming and genetic algorithms for construction site facility layout planning. Journal Of Construction Engineering And Management, 136(10), 1116-1128, https://doi.org/10.1061/(ASCE)CO.1943-7862.0000214.
  • Wu, Y., Zhang, B., Wu, C., Zhang, T. and Liu, F. (2019). Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: a case in China. Renewable Energy, https://doi.org/10.1016/j.renene.2019.05.131.
  • Yap, J. Y. L., Ho, C. C. and Ting, C. Y. (2019). A systematic review of the applications of multi-criteria decision-making methods in site selection problems. Built Environment Project and Asset Management, https://doi.org/10.1108/BEPAM-05-2018-0078.
  • Yu, X., Li, C., Shi, Y. and Yu, M. (2010). Pharmaceutical supply chain in China: current issues and implications for health system reform. Health Policy, 97(1), 8-15, https://doi.org/10.1016/j.healthpol.2010.02.010.
  • Zahiri, B., Jula, P. and Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283, https://doi.org/10.1016/j.ins.2017.09.046.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Aslıhan Yıldız 0000-0001-5288-7967

Selin Soner Kara 0000-0002-0894-0772

Coşkun Özkan 0000-0002-0318-8614

Yayımlanma Tarihi 15 Nisan 2021
Gönderilme Tarihi 17 Ocak 2020
Kabul Tarihi 8 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 11 Sayı: 2

Kaynak Göster

APA Yıldız, A., Soner Kara, S., & Özkan, C. (2021). Sağlık kurumlarının ve eczanelerin taleplerine yönelik ecza deposu yer seçimi problemi. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11(2), 452-465. https://doi.org/10.17714/gumusfenbil.676376

Cited By

A fuzzy approach for prioritization of pharmacies to improve mask distribution process during COVID-19 pandemic—a pilot study for İstanbul
Neural Computing and Applications
https://doi.org/10.1007/s00521-023-08749-w
SWARA, ARAS ve WASPAS yöntemleri ile yeni şube yeri seçimi: Lojistik sektöründe bir kargo firma örneği
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.35234/fumbd.1187200
 Kapak Resmi İndir