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Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach

Yıl 2026, Cilt: 9 Sayı: 2, 769 - 787, 15.03.2026
https://doi.org/10.34248/bsengineering.1808251
https://izlik.org/JA33DM27NG

Öz

The sustainability of the pharmaceutical supply chain after disasters is critically important to ensure the continuity of healthcare services and prevent loss of life during crises. Türkiye is geographically located in a region highly susceptible to various disasters such as earthquakes, floods, landslides, and epidemics. This study aims to comprehensively evaluate the resilience of pharmaceutical warehouses, which are key components of Türkiye’s pharmaceutical supply chain, against disasters. The research adopted both static and dynamic approaches by integrating Multi-Criteria Decision-Making (MCDM) methods with Long Short-Term Memory (LSTM)-based forecasting models. Thus, while conducting an up-to-date analysis of the resilience of pharmaceutical warehouses to current risks, the study also forecasted potential future environmental burdens. In the MCDM analysis, seventeen criteria were determined under five main categories: logistics challenges, inventory management, quality control, institutional coordination, and financial capacity. The importance weights of the criteria were calculated using the AHP, Fuzzy AHP, and CRADIS methods, while the risk preparedness levels of the warehouses were evaluated through the TOPSIS and LOPCOW methods. According to experts, the most critical factors were identified as “Incorrect Drug Distribution,” “Stolen or Counterfeit Medicines,” “Inter-Institutional Communication,” and “Transportation Difficulties.” In the dynamic analysis based on the Long Short-Term Memory (LSTM) model, one- and three-layer LSTM models were trained using data on the number of disasters that occurred between 2000 and 2024 and Türkiye’s healthcare expenditures. According to the LSTM model predictions obtained at the epoch with the lowest RMSE value, it is estimated that by 2030, at least 800,000 people will be affected by disasters, and healthcare expenditures will triple compared to current levels. These findings indicate that demographic and economic growth will create a significant gap between the existing infrastructure and the increasing future burden, suggesting that even low-risk warehouses may exhibit operational vulnerabilities. In conclusion, the proposed dual approach not only provides concrete forecasts for the future but also offers a quantitative assessment of the current infrastructure’s capacity to meet these emerging demands.

Etik Beyan

Ethics committee approval was not required for this study because of there was no study on animals or humans.

Kaynakça

  • Afsordegan, A., Sánchez, M., Agell, N., Zahedi, S., & Cremades, L. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives. International Journal of Environmental Science and Technology, 13(6), 1419–1432.
  • Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, Article 108193.
  • Alidoost, F., Bahrami, F., & Safari, H. (2020). Multi-objective pharmaceutical supply chain modeling in disaster (Case study: Earthquake crisis in Tehran). Journal of Industrial Management Perspective, 10(3), 99–123.
  • Alzober, W., & Yaakub, A. R. (2014). Integrating AHP application for project management. Applied Mechanics and Materials, 575, 895–899.
  • Atkinson, M. A., Bayazit, O., & Karpak, B. (2015). A case study using the analytic hierarchy process for IT outsourcing decision making. International Journal of Information Systems and Supply Chain Management, 8(1), 60–84.
  • Aytekin, A., Görçün, Ö. F., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2023). Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach. Kybernetes, 52(11), 5561–5592.
  • Bandhu, K. C., Litoriya, R., Lowanshi, P., Jindal, M., Chouhan, L., & Jain, S. (2023). Making drug supply chain secure, traceable and efficient: A blockchain and smart contract based implementation. Multimedia Tools and Applications, 82(15), 23541–23568.
  • Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.
  • BenAmor, W. D., Labella, A., Frikha, H. M., & López, L. M. (2022). Pharmaceutical supply chain risk assessment during COVID-19 epidemic. IFAC-PapersOnLine, 55(10), 2203–2208.
  • Bhat, S. (2023). An enhanced AHP group decision-making model employing neutrosophic trapezoidal numbers. Journal of Operations and Strategic Analysis, 1(2), 81–89.
  • Biswas, S., Kumar, D., Hajiaghaei-Keshteli, M., & Bera, U. K. (2024). An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye. International Journal of Disaster Risk Reduction, 102, Article 104287.
  • Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2, Article 100021.
  • Chen, L., & Pan, W. (2021). Review fuzzy multi-criteria decision-making in construction management using a network approach. Applied Soft Computing, 102, Article 107103.
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  • Chung, H.-Y., & Chang, K. H. (2022). Using the flexible analytic hierarchy process method to solve the emergency decision making of public health for COVID-19. International Journal of Industrial Engineering: Theory, Applications and Practice, 31(5).
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  • Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2022). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 15(1), 249–267.
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  • de Souza, G. M., Santos, E. A., Silva, C. E. S., & de Souza, D. G. B. (2022). Integrating fuzzy-MCDM methods to select project portfolios under uncertainty: The case of a pharmaceutical company. Brazilian Journal of Operations & Production Management, 19(3), 1–19.
  • Demir, G., Riaz, M., & Almalki, Y. (2023). Multi-criteria decision making in evaluation of open government data indicators: An application in G20 countries. AIMS Mathematics, 8(8), 18408–18434.
  • Dewi, R. K., Ananta, M. T., Fanani, L., Brata, K. C., & Priandani, N. D. (2018). The development of mobile culinary recommendation system based on group decision support system. International Journal of Interactive Mobile Technologies, 12(3), 209–216.
  • Dinçer, S., Gündüz, F., Atalay, E., Usta, G., & Göktaş, S. P. (2025). The role of community pharmacists in natural disasters: Experiences from the 2023 Türkiye earthquakes. Health Policy, Article 105333.
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Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach

Yıl 2026, Cilt: 9 Sayı: 2, 769 - 787, 15.03.2026
https://doi.org/10.34248/bsengineering.1808251
https://izlik.org/JA33DM27NG

Öz

The sustainability of the pharmaceutical supply chain after disasters is critically important to ensure the continuity of healthcare services and prevent loss of life during crises. Türkiye is geographically located in a region highly susceptible to various disasters such as earthquakes, floods, landslides, and epidemics. This study aims to comprehensively evaluate the resilience of pharmaceutical warehouses, which are key components of Türkiye’s pharmaceutical supply chain, against disasters. The research adopted both static and dynamic approaches by integrating Multi-Criteria Decision-Making (MCDM) methods with Long Short-Term Memory (LSTM)-based forecasting models. Thus, while conducting an up-to-date analysis of the resilience of pharmaceutical warehouses to current risks, the study also forecasted potential future environmental burdens. In the MCDM analysis, seventeen criteria were determined under five main categories: logistics challenges, inventory management, quality control, institutional coordination, and financial capacity. The importance weights of the criteria were calculated using the AHP, Fuzzy AHP, and CRADIS methods, while the risk preparedness levels of the warehouses were evaluated through the TOPSIS and LOPCOW methods. According to experts, the most critical factors were identified as “Incorrect Drug Distribution,” “Stolen or Counterfeit Medicines,” “Inter-Institutional Communication,” and “Transportation Difficulties.” In the dynamic analysis based on the Long Short-Term Memory (LSTM) model, one- and three-layer LSTM models were trained using data on the number of disasters that occurred between 2000 and 2024 and Türkiye’s healthcare expenditures. According to the LSTM model predictions obtained at the epoch with the lowest RMSE value, it is estimated that by 2030, at least 800,000 people will be affected by disasters, and healthcare expenditures will triple compared to current levels. These findings indicate that demographic and economic growth will create a significant gap between the existing infrastructure and the increasing future burden, suggesting that even low-risk warehouses may exhibit operational vulnerabilities. In conclusion, the proposed dual approach not only provides concrete forecasts for the future but also offers a quantitative assessment of the current infrastructure’s capacity to meet these emerging demands.

Etik Beyan

Ethics committee approval was not required for this study because of there was no study on animals or humans.

Kaynakça

  • Afsordegan, A., Sánchez, M., Agell, N., Zahedi, S., & Cremades, L. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives. International Journal of Environmental Science and Technology, 13(6), 1419–1432.
  • Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, Article 108193.
  • Alidoost, F., Bahrami, F., & Safari, H. (2020). Multi-objective pharmaceutical supply chain modeling in disaster (Case study: Earthquake crisis in Tehran). Journal of Industrial Management Perspective, 10(3), 99–123.
  • Alzober, W., & Yaakub, A. R. (2014). Integrating AHP application for project management. Applied Mechanics and Materials, 575, 895–899.
  • Atkinson, M. A., Bayazit, O., & Karpak, B. (2015). A case study using the analytic hierarchy process for IT outsourcing decision making. International Journal of Information Systems and Supply Chain Management, 8(1), 60–84.
  • Aytekin, A., Görçün, Ö. F., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2023). Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach. Kybernetes, 52(11), 5561–5592.
  • Bandhu, K. C., Litoriya, R., Lowanshi, P., Jindal, M., Chouhan, L., & Jain, S. (2023). Making drug supply chain secure, traceable and efficient: A blockchain and smart contract based implementation. Multimedia Tools and Applications, 82(15), 23541–23568.
  • Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.
  • BenAmor, W. D., Labella, A., Frikha, H. M., & López, L. M. (2022). Pharmaceutical supply chain risk assessment during COVID-19 epidemic. IFAC-PapersOnLine, 55(10), 2203–2208.
  • Bhat, S. (2023). An enhanced AHP group decision-making model employing neutrosophic trapezoidal numbers. Journal of Operations and Strategic Analysis, 1(2), 81–89.
  • Biswas, S., Kumar, D., Hajiaghaei-Keshteli, M., & Bera, U. K. (2024). An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye. International Journal of Disaster Risk Reduction, 102, Article 104287.
  • Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2, Article 100021.
  • Chen, L., & Pan, W. (2021). Review fuzzy multi-criteria decision-making in construction management using a network approach. Applied Soft Computing, 102, Article 107103.
  • Chopra, S., & Sodhi, M. (2004). Supply-chain breakdown. MIT Sloan Management Review, 46(1), 53–61.
  • Chung, H.-Y., & Chang, K. H. (2022). Using the flexible analytic hierarchy process method to solve the emergency decision making of public health for COVID-19. International Journal of Industrial Engineering: Theory, Applications and Practice, 31(5).
  • CRED. (2025). EM-DAT: The International Disaster Database. Université catholique de Louvain (UCLouvain), Institute of Health & Society (IRSS). https://www.emdat.be
  • da Silva, A. C. T., de Sousa, J. P., & Marques, C. M. (2022). Supply chain resiliency in the pharmaceutical industry–a simulation-based approach. In Proceedings of the 5th European International Conference on Industrial Engineering and Operations Management.
  • Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2022). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 15(1), 249–267.
  • de FSM Russo, R., & Camanho, R. (2015). Criteria in AHP: A systematic review of literature. Procedia Computer Science, 55, 1123–1132.
  • de Souza, G. M., Santos, E. A., Silva, C. E. S., & de Souza, D. G. B. (2022). Integrating fuzzy-MCDM methods to select project portfolios under uncertainty: The case of a pharmaceutical company. Brazilian Journal of Operations & Production Management, 19(3), 1–19.
  • Demir, G., Riaz, M., & Almalki, Y. (2023). Multi-criteria decision making in evaluation of open government data indicators: An application in G20 countries. AIMS Mathematics, 8(8), 18408–18434.
  • Dewi, R. K., Ananta, M. T., Fanani, L., Brata, K. C., & Priandani, N. D. (2018). The development of mobile culinary recommendation system based on group decision support system. International Journal of Interactive Mobile Technologies, 12(3), 209–216.
  • Dinçer, S., Gündüz, F., Atalay, E., Usta, G., & Göktaş, S. P. (2025). The role of community pharmacists in natural disasters: Experiences from the 2023 Türkiye earthquakes. Health Policy, Article 105333.
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  • El Mokrini, A., Kafa, N., Dafaoui, E., El Mhamedi, A., & Berrado, A. (2016). Evaluating outsourcing risks in the pharmaceutical supply chain: Case of a multi-criteria combined fuzzy AHP-PROMETHEE approach. IFAC-PapersOnLine, 49(28), 114–119.
  • Elleuch, H., Hachicha, W., & Chabchoub, H. (2014). A combined approach for supply chain risk management: Description and application to a real hospital pharmaceutical case study. Journal of Risk Research, 17(5), 641–663.
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  • Grošelj, P., & Stirn, L. Z. (2017). Soft consensus model for the group fuzzy AHP decision making. Croatian Operational Research Review, 207–220.
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  • Huynh, V. D. B., Nguyen, P., Nguyen, Q., & Nguyen, P. T. (2018). Application of fuzzy analytical hierarchy process based on geometric mean method to prioritize social capital network indicators. International Journal of Advanced Computer Science and Applications, 9(12), 182–186.
  • Ikhsan, M. N., Muhammad, A., & Mahessya, R. A. (2023). Customer Relationship Management to retain customers with the TOPSIS method. Journal of Computer Science and Information Technology, 112–118.
  • İmece, S., & Beyca, Ö. F. (2022). Demand forecasting with integration of time series and regression models in pharmaceutical industry. International Journal of Advanced Engineering and Pure Sciences, 34(3), 415–425.
  • Institute, T. S. (2025). Total healthcare expenditure, 2000–2024. https://www.tuik.gov.tr
  • Jaberidoost, M., Nikfar, S., Abdollahiasl, A., & Dinarvand, R. (2013). Pharmaceutical supply chain risks: A systematic review. DARU Journal of Pharmaceutical Sciences, 21(1), Article 69.
  • Jaberidoost, M., Olfat, L., Hosseini, A., Kebriaeezadeh, A., Abdollahi, M., Alaeddini, M., & Dinarvand, R. (2015). Pharmaceutical supply chain risk assessment in Iran using analytic hierarchy process (AHP) and simple additive weighting (SAW) methods. Journal of Pharmaceutical Policy and Practice, 8(1), Article 9.
  • Just, M., & Kozera, A. (2018). Application of the positional POT-TOPSIS method to the assessment of financial self-sufficiency of local administrative units. In Proceedings of Economics and Finance Conferences (No. 6910173). International Institute of Social and Economic Sciences.
  • Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda for future research. International Journal of Logistics: research and applications, 6(4), 197-210.
  • Khan, O., & Burnes, B. (2007). Risk and supply chain management: Creating a research agenda. The International Journal of Logistics Management, 18(2), 197–216.
  • Khan, S. A., Gupta, H., Gunasekaran, A., Mubarik, M. S., & Lawal, J. (2023). A hybrid multi‐criteria decision‐making approach to evaluate interrelationships and impacts of supply chain performance factors on pharmaceutical industry. Journal of Multi-Criteria Decision Analysis, 30(1-2), 62–90.
  • Kocaoğlu, B., & Küçük, A. (2019). Evaluation of the performance of companies operating in the pharmaceutical sector for Reverse Logistics applications with TOPSIS and MOORA Methods. Journal of Transport & Logistics, 4(1), 11–30.
  • Kordi, M., & Brandt, S. A. (2012). Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis. Computers, Environment and Urban Systems, 36(1), 43–53.
  • Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Systems with Applications, 161, Article 113738.
  • Lukić, R. (2023). Research of the economic positioning of the Western Balkan countries using the LOPCOW and EDAS methods. Journal of Engineering Management and Competitiveness, 13(2), 106–116.
  • Malau, A., & Hafizh, M. (2023). Utilization of IT Business Management for marketing development with the analytical hierarchy process method. Journal of Computer Science and Information Technology, 125–131.
  • Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133–155.
  • Martha, J., & Subbakrishna, S. (2002). Targeting a just-in-case supply chain for the inevitable next disaster. Supply Chain Management Review, 6(5), 18–23.
  • Massam, B. H. (1988). Multi-criteria decision making (MCDM) techniques in planning. Progress in Planning, 30, 1–84.
  • Meixner, O., Haas, R., & Pöchtrager, S. (2016, August). AHP group decision making and clustering. International Symposium on the Analytic Hierarchy Process (ISAHP). http://www.isahp.org/uploads/paper_mo_hr_isahp_2016rev_
  • Modibbo, U. M., Hassan, M., Ahmed, A., & Ali, I. (2022). Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS. Management Decision, 60(3), 806–836.
  • Narayana, S. A., Pati, R. K., & Vrat, P. (2014). Managerial research on the pharmaceutical supply chain–A critical review and some insights for future directions. Journal of Purchasing and Supply Management, 20(1), 18–40.
  • Nguyen, X., Le, T., Nguyen, A., Pham, T., & Tran, T. (2021). Supply chain risk, integration, risk resilience and firm performance in global supply chain: Evidence from Vietnam pharmaceutical industry. Uncertain Supply Chain Management, 9(4), 779–796.
  • Okeagu, C. N., Reed, D. S., Sun, L., Colontonio, M. M., Rezayev, A., Ghaffar, Y. A., & Fox, C. J. (2021). Principles of supply chain management in the time of crisis. Best Practice & Research Clinical Anaesthesiology, 35(3), 369–376.
  • Oran, İ. B., Ayboğa, M. H., Erol, M., & Yildiz, G. (2022). The necessity of transition from Industry 4.0 to Industry 5.0: SWOT analysis of Türkiye’s SCM strategy. Journal of Organizational Behavior Research, 7(2), 1–17.
  • Pedroso, M. C., & Nakano, D. (2009). Knowledge and information flows in supply chains: A study on pharmaceutical companies. International Journal of Production Economics, 122(1), 376–384.
  • Perçin, S. (2008). Use of fuzzy AHP for evaluating the benefits of information‐sharing decisions in a supply chain. Journal of Enterprise Information Management, 21(3), 263–284.
  • Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7467–7482.
  • Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS model. Axioms, 11(9), Article 427.
  • Qiu, K., Chen, J., Ashraf, S., & Shahid, T. (2024). Strategic decision support system with probabilistic linguistic term sets: Extended CRADIS approach for supply chain risk management in sports industry. IEEE Access, 13, 32853–32862.
  • Rajabi, F., Jahangiri, M., Bagherifard, F., Banaee, S., & Farhadi, P. (2020). Strategies for controlling violence against health care workers: Application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment. Journal of Nursing Management, 28(4), 777–786.
  • Sabbagh, P., Pourmohamad, R., Elveny, M., Beheshti, M., Davarpanah, A., Metwally, A. S. M., & Mohammed, A. S. (2021). RETRACTED: Evaluation and classification risks of implementing blockchain in the drug supply chain with a new hybrid sorting method. Sustainability, 13(20), Article 11466.
  • Sampat, M. A., Kumar, R., Pushpangatha Kurup, R., Chiu, K., Saucedo, V. M., & Zavala, V. M. (2021). Multisite supply planning for drug products under uncertainty. AIChE Journal, 67(1), Article e17069.
  • Sangshetti, J. N., Deshpande, M., Zaheer, Z., Shinde, D. B., & Arote, R. (2017). Quality by design approach: Regulatory need. Arabian Journal of Chemistry, 10, S3412–S3425.
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  • Taş, I., & Satoglu, S. I. (2023). Demand forecasting in pharmaceutical industry under Covid-19 pandemic conditions by machine learning and time series analysis. In Proceedings of the International Conference on Intelligent and Fuzzy Systems.
  • Tirivangani, T., Alpo, B., Kibuule, D., Gaeseb, J., & Adenuga, B. A. (2021). Impact of COVID-19 pandemic on pharmaceutical systems and supply chain–a phenomenological study. Exploratory Research in Clinical and Social Pharmacy, 2, Article 100037.
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  • Uludağ, A. S., & Doğan, H. (2016). Çok kriterli karar verme yöntemlerinin karşılaştırılmasına odaklı bir hizmet kalitesi uygulaması. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 17–48.
  • Vishwakarma, V., Prakash, C., & Barua, M. K. (2016). A fuzzy-based multi criteria decision making approach for supply chain risk assessment in Indian pharmaceutical industry. International Journal of Logistics Systems and Management, 25(2), 245–265.
  • Xu, P., Guo, M., Qian, H., & Zhang, Q. (2019). Geothermal water quality assessment based on entropy weighted TOPSIS method in Xi'an, China. In Proceedings of the 2nd International Conference on Sustainable Energy, Environment and Information Engineering.
  • Yapici Pehlivan, N., Şahin, A., Zavadskas, E. K., & Turskis, Z. (2018). A comparative study of integrated FMCDM methods for evaluation of organizational strategy development. Journal of Business Economics and Management, 19(2), 360–381.
  • Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270.
  • Yue, D., Wu, X., & Bai, J. (2008). RFID application framework for pharmaceutical supply chain. IEEE International Conference on Service Operations and Logistics, and Informatics.
  • Yue, Z. (2014). TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 277, 141–153.
  • Zadeh, L. A. (1990). The birth and evolution of fuzzy logic. International Journal of General Systems, 17(2-3), 95–105.
  • Zamani, R., & Yousefi, P. (2013). Optimal decision making method for multi criteria problems. International Journal of Machine Learning and Computing, 3(4), 380–384.
Toplam 90 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hesaplamalı İstatistik, Çok Ölçütlü Karar Verme, Endüstri Mühendisliği, Üretim ve Hizmet Sistemleri
Bölüm Araştırma Makalesi
Yazarlar

Menşure Zühal Barak 0000-0002-2039-0785

Gizem Gül Koç 0000-0002-0058-0207

Gönderilme Tarihi 21 Ekim 2025
Kabul Tarihi 17 Şubat 2026
Yayımlanma Tarihi 15 Mart 2026
DOI https://doi.org/10.34248/bsengineering.1808251
IZ https://izlik.org/JA33DM27NG
Yayımlandığı Sayı Yıl 2026 Cilt: 9 Sayı: 2

Kaynak Göster

APA Barak, M. Z., & Koç, G. G. (2026). Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach. Black Sea Journal of Engineering and Science, 9(2), 769-787. https://doi.org/10.34248/bsengineering.1808251
AMA 1.Barak MZ, Koç GG. Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach. BSJ Eng. Sci. 2026;9(2):769-787. doi:10.34248/bsengineering.1808251
Chicago Barak, Menşure Zühal, ve Gizem Gül Koç. 2026. “Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach”. Black Sea Journal of Engineering and Science 9 (2): 769-87. https://doi.org/10.34248/bsengineering.1808251.
EndNote Barak MZ, Koç GG (01 Mart 2026) Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach. Black Sea Journal of Engineering and Science 9 2 769–787.
IEEE [1]M. Z. Barak ve G. G. Koç, “Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach”, BSJ Eng. Sci., c. 9, sy 2, ss. 769–787, Mar. 2026, doi: 10.34248/bsengineering.1808251.
ISNAD Barak, Menşure Zühal - Koç, Gizem Gül. “Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach”. Black Sea Journal of Engineering and Science 9/2 (01 Mart 2026): 769-787. https://doi.org/10.34248/bsengineering.1808251.
JAMA 1.Barak MZ, Koç GG. Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach. BSJ Eng. Sci. 2026;9:769–787.
MLA Barak, Menşure Zühal, ve Gizem Gül Koç. “Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach”. Black Sea Journal of Engineering and Science, c. 9, sy 2, Mart 2026, ss. 769-87, doi:10.34248/bsengineering.1808251.
Vancouver 1.Menşure Zühal Barak, Gizem Gül Koç. Disaster Resilience Assessment of Pharmaceutical Warehouses in Türkiye: A Multi-Criteria and Long Short-Term Memory-Based Approach. BSJ Eng. Sci. 01 Mart 2026;9(2):769-87. doi:10.34248/bsengineering.1808251

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