gummfd Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1300-1884 1304-4915 Gazi Üniversitesi 10.17341/gazimmfd.1110485 Engineering Mühendislik Kaos durumu altında hava kargo şirketi seçimi: Bütünleşik Bayesian BWM ve WASPAS çerçevesi https://orcid.org/0000-0002-1522-1768 Boz Esra KTO KARATAY UNIVERSITY https://orcid.org/0000-0002-3355-8882 Çizmecioğlu Sinan KTO KARATAY ÜNİVERSİTESİ https://orcid.org/0000-0002-6796-0052 Çalık Ahmet KTO KARATAY ÜNİVERSİTESİ 01 06 2023 38 3 1586 1600 04 30 2022 07 22 2022 Copyright © 1986, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 1986 Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Karar problemlerinin sonuçları ve problemin sonuçlarını etkileyen faktörler, herhangi bir kaos durumunun bulunup bulunmamasına göre değişiklik gösterebilmektedir. Kaos durumları altında, karar alıcıların tercihleri için farklı kriterler eklenebilmekle ve kriterlerin önem düzeyleri değişebilmektedir. COVID-19 pandemisi her alanda olduğu gibi havacılık sektörünü de etkilemiş olmasına rağmen hava kargo taşımacılığı bu dönemde güçlü bir performans göstermektedir. Bu noktadan hareketle, bu çalışmada kaos durumlarının hava kargo şirketi seçimine yansıması incelenmektedir. Karar vericilerin, karar problemlerini sonuçlandırmasında etkili bir çözüm yöntemi olan Çok Kriterli Karar Verme (ÇKKV) yöntemleri ile yeni bir karar verme çerçevesi önerilmektedir. Yeni önerilen yöntemlerin daha hassas yanıt vermesinden dolayı, kriter ağırlıklarının belirlenmesinde yeni yöntemlerden olan Bayesian BWM (En İyi-En Kötü) yöntemi kullanılırken, hava kargo şirketlerinin sıralanmasında ise WASPAS yöntemi kullanılmaktadır. Böylece bu iki yöntem bütünleştirilmekte ve aynı zamanda sıralama sonuçları TOPSIS ve COPRAS yöntemi ile kıyaslanarak sonuçlar analiz edilmektedir. Buna göre, kaos ortamında hava kargo şirketi seçimi için en önemli kriter ekonomik kriterler olarak görünmektedir.

 WASPAS B-BWM Hava kargo firma seçimi ÇKKV Agrebi, M., & Abed, M. (2021). Decision-making from multiple uncertain experts: case of distribution center location selection. Soft Computing, 25(6), 4525–4544. Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69–78. Akcan, S., & Güldeş, M. (2019). Integrated multicriteria decision-making methods to solve supplier selection problem: a case study in a hospital. Journal of Healthcare Engineering, 2019. Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing Journal, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023 Atayah, O. F., Dhiaf, M. M., Najaf, K., & Frederico, G. F. (2021). Impact of COVID-19 on financial performance of logistics firms: evidence from G-20 countries. Journal of Global Operations and Strategic Sourcing. Awasthi, A., Adetiloye, T., & Crainic, T. G. (2016). Collaboration partner selection for city logistics planning under municipal freight regulations. Applied Mathematical Modelling, 40(1), 510–525. Awasthi, A., & Chauhan, S. S. (2012). A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Applied Mathematical Modelling, 36(2), 573–584. Awasthi, A., Chauhan, S. S., & Goyal, S. K. (2011). A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53(1–2), 98–109. Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195(October 2017), 106–117. https://doi.org/10.1016/j.ijpe.2017.10.013 Azadnia, A. H., Saman, M. Z. M., & Wong, K. Y. (2015). Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process. International Journal of Production Research, 53(2), 383–408. https://doi.org/10.1080/00207543.2014.935827 Bahadori, M., Hosseini, S. M., Teymourzadeh, E., Ravangard, R., Raadabadi, M., & Alimohammadzadeh, K. (2020). A supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR. International Journal of Healthcare Management, 13(4), 286–294. Bai, C., & Sarkis, J. (2019). Integrating and extending data and decision tools for sustainable third-party reverse logistics provider selection. Computers & Operations Research, 110, 188–207. Bansal, A., & Kumar, P. (2013). 3PL selection using hybrid model of AHP-PROMETHEE. International Journal of Services and Operations Management, 14(3), 373–397. Bjelobrk, N., Nabavi, M., & Poulikakos, D. (2011). Acoustic levitator for contactless transport and mixing of droplets in air. The Journal of the Acoustical Society of America, 130(4), 2370. Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal. Burney, S. A., & Ali, S. M. (2019). Fuzzy multi-criteria based decision support system for supplier selection in textile industry. IJCSNS, 19(1), 239. Chaharsooghi, S. K., & Ashrafi, M. (2014). Sustainable supplier performance evaluation and selection with neofuzzy TOPSIS method. International Scholarly Research Notices, 2014. Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1–20. Chakraborty, S., Zavadskas, E. K., & Antucheviciene, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 5–22. Chan, F. T. S., Kumar, N., Tiwari, M. K., Lau, H. C. W., & Choy, K. L. (2008). Global supplier selection: A fuzzy-AHP approach. International Journal of Production Research, 46(14), 3825–3857. https://doi.org/10.1080/00207540600787200 Chen, T., Wang, Y.-C., & Wu, H.-C. (2021). Analyzing the impact of vaccine availability on alternative supplier selection amid the COVID-19 pandemic: a cFGM-FTOPSIS-FWI approach. Healthcare, 9(1), 71. Chen, Y.-J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Sciences, 181(9), 1651–1670. Chen, Z.-S., Zhang, X., Govindan, K., Wang, X.-J., & Chin, K.-S. (2021). Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach. Expert Systems with Applications, 166, 114051. Choi, T.-M. (2021). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. In Transportation Research Part E: Logistics and Transportation Review (Vol. 145, p. 102190). Elsevier. Choy, K. L., Li, C.-L., So, S. C. K., Lau, H., Kwok, S. K., & Leung, D. (2007). Managing uncertainty in logistics service supply chain. International Journal of Risk Assessment and Management, 7(1), 19–43. Delgado, F., Sirhan, C., Katscher, M., & Larrain, H. (2020). Recovering from demand disruptions on an air cargo network. Journal of Air Transport Management, 85, 101799. Durak, M. Ş., & Yılmaz, A. K. (2016). Airline selection criteria at air cargo transportation industry. Transport & Logistics, 16(40), 10–18. Ecer, F. (2018). Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634. Ecer, F. (2020). Çok kriterli karar verme geçmişten günümüze kapsamlı bir yaklaşım. Ankara: Seçkin Yayınevi. Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54(2), 269–287. Ejem, E. A., Uka, C. M., Dike, D. N., Ikeogu, C. C., Igboanusi, C. C., & Chukwu, O. E. (2021). Evaluation and selection of Nigerian third-party logistics service providers using multi-criteria decision models. LOGI–Scientific Journal on Transport and Logistics, 12(1), 135–146. Falsini, D., Fondi, F., & Schiraldi, M. M. (2012). A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration. International Journal of Production Research, 50(17), 4822–4829. Göl, H., & Çatay, B. (2007). Third‐party logistics provider selection: insights from a Turkish automotive company. Supply Chain Management: An International Journal. Govindan, K., Kadziński, M., Ehling, R., & Miebs, G. (2019). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega, 85, 1–15. Govindan, K., & Murugesan, P. (2011). Selection of third‐party reverse logistics provider using fuzzy extent analysis. Benchmarking: An International Journal. Gul, M., & Yucesan, M. (2022). Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model. Socio-Economic Planning Sciences, 80, 101173. Hasan, M. M., Jiang, D., Ullah, A. M. M. S., & Noor-E-Alam, M. (2020). Resilient supplier selection in logistics 4.0 with heterogeneous information. Expert Systems with Applications, 139, 112799. Ho, W., He, T., Lee, C. K. M., & Emrouznejad, A. (2012). Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach. Expert Systems with Applications, 39(12), 10841–10850. Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24. Hsu, C.-C., Liou, J. J. H., & Chuang, Y.-C. (2013). Integrating DANP and modified grey relation theory for the selection of an outsourcing provider. Expert Systems with Applications, 40(6), 2297–2304. Hsu, W.-C. J., Lo, H.-W., & Yang, C.-C. (2021). The formulation of epidemic prevention work of covid-19 for colleges and universities: priorities and recommendations. Sustainability, 13(4), 2081. Huang, C.-N., Liou, J. J. H., Lo, H.-W., & Chang, F.-J. (2021). Building an assessment model for measuring airport resilience. Journal of Air Transport Management, 95, 102101. Hudnurkar, M., & Ambekar, S. S. (2019). Framework for measurement of supplier satisfaction. International Journal of Productivity and Performance Management. Jayant, A., Gupta, P., Garg, S. K., & Khan, M. (2014). TOPSIS-AHP based approach for selection of reverse logistics service provider: a case study of mobile phone industry. Procedia Engineering, 97, 2147–2156. Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. International Journal of Production Economics, 195(December 2014), 391–418. https://doi.org/10.1016/j.ijpe.2017.02.020 Kannan, D., Govindan, K., & Rajendran, S. (2015). Fuzzy axiomatic design approach based green supplier selection: a case study from Singapore. Journal of Cleaner Production, 96, 194–208. Kannan, G. (2009). Fuzzy approach for the selection of third party reverse logistics provider. Asia Pacific Journal of Marketing and Logistics. Kasarda, J. D., & Green, J. D. (2005). Air cargo as an economic development engine: A note on opportunities and constraints. Journal of Air Transport Management, 11(6), 459–462. Kumar, D. T., Palaniappan, M., Kannan, D., & Shankar, K. M. (2014). Analyzing the CSR issues behind the supplier selection process using ISM approach. Resources, Conservation and Recycling, 92, 268–278. Kunadhamraks, P., & Hanaoka, S. (2008). Evaluating the logistics performance of intermodal transportation in Thailand. Asia Pacific Journal of Marketing and Logistics. Kusi-Sarpong, S., Gupta, H., Khan, S. A., Chiappetta Jabbour, C. J., Rehman, S. T., & Kusi-Sarpong, H. (2021). Sustainable supplier selection based on industry 4.0 initiatives within the context of circular economy implementation in supply chain operations. Production Planning & Control, 1–21. Lange, A. (2019). Does cargo matter? The impact of air cargo operations on departure on-time performance for combination carriers. Transportation Research Part A: Policy and Practice, 119, 214–223. Lee, N. S., Mazur, P. G., Bittner, M., & Schoder, D. (2021, January). An intelligent decision-support system for air cargo palletizing. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 1405). Lee, H.-H., Yang, T.-T., Chen, C.-B., & Chen, Y.-L. (2011). A fuzzy hierarchy integral analytic expert decision process in evaluating foreign investment entry mode selection for Taiwanese bio-tech firms. Expert Systems with Applications, 38(4), 3304–3322. https://doi.org/10.1016/J.ESWA.2010.08.116 Li, L., & Zabinsky, Z. B. (2011). Incorporating uncertainty into a supplier selection problem. International Journal of Production Economics, 134(2), 344–356. Li, T. (2020). A SWOT analysis of China’s air cargo sector in the context of COVID-19 pandemic. Journal of Air Transport Management, 88, 101875. Li, Y.-L., Ying, C.-S., Chin, K.-S., Yang, H.-T., & Xu, J. (2018). Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production, 195, 573–584. Liu, H. T., & Wang, W. K. (2009). An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert systems with applications, 36(3), 4387-4398. Liao, H., Wu, D., Huang, Y., Ren, P., Xu, Z., & Verma, M. (2018). Green logistic provider selection with a hesitant fuzzy linguistic thermodynamic method integrating cumulative prospect theory and PROMETHEE. Sustainability, 10(4), 1291. Linder, C., & Seidenstricker, S. (2018). How does a component from a supplier with high reputation for product innovation improve the perception of a final offering? A process perspective. European Management Journal, 36(2), 288–299. Liou, J. J. H., & Chuang, Y.-T. (2010). Developing a hybrid multi-criteria model for selection of outsourcing providers. Expert Systems with Applications, 37(5), 3755–3761. Liu, Y., Zhou, P., Li, L., & Zhu, F. (2020). An interactive decision-making method for third-party logistics provider selection under hybrid multi-criteria. Symmetry, 12(5), 729. Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698. https://doi.org/10.1016/j.jclepro.2016.09.078 Ma, X., Li, N., Tao, X., Xu, H., Peng, F., Che, Y., & Guo, S. (2019). The optimal selection of electrochemical energy storage using Bayesian BWM and TOPSIS method. 2019 6th International Conference on Information Science and Control Engineering (ICISCE), 610–614. Malighetti, P., Martini, G., Redondi, R., & Scotti, D. (2019). Air transport networks of global integrators in the more liberalized Asian air cargo industry. Transport Policy, 80, 12–23. Manello, A., & Calabrese, G. (2019). The influence of reputation on supplier selection: An empirical study of the European automotive industry. Journal of Purchasing and Supply Management, 25(1), 69–77. Mardani, A., Zavadskas, E. K., Khalifah, Z., Jusoh, A., & Nor, K. M. D. (2016). Multiple criteria decision-making techniques in transportation systems: A systematic review of the state of the art literature. Transport, 31(3), 359–385. Mitra, S., & Leon, S. M. (2014). Discrete choice model for air-cargo mode selection. The International Journal of Logistics Management. Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075. Özbek, A., & Eren, T. (2013). Multiple criteria decision making methods for selecting third party logistics firms: A literatur review. Sigma, 31, 178–202. Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383–407. Peng, J. (2012). Selection of logistics outsourcing service suppliers based on AHP. Energy Procedia, 17, 595–601. Peng, X., Deng, D., Cheng, S., Wen, J., Li, Z., & Niu, L. (2015). Key technologies of electric power big data and its application prospects in smart grid. Proceedings of the CSEE, 35(3), 503–511. Percin, S. (2009). Evaluation of third‐party logistics (3PL) providers by using a two‐phase AHP and TOPSIS methodology. Benchmarking: An International Journal. Perçin, S., & Min, H. (2013). A hybrid quality function deployment and fuzzy decision-making methodology for the optimal selection of third-party logistics service providers. International Journal of Logistics Research and Applications, 16(5), 380–397. Qureshi, M. N., Kumar, D., & Kumar, P. (2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pacific Journal of Marketing and Logistics. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. Rouyendegh, B. D., Yildizbasi, A., & Üstünyer, P. (2020). Intuitionistic fuzzy TOPSIS method for green supplier selection problem. Soft Computing, 24(3), 2215–2228. Roy, J., Pamučar, D., & Kar, S. (2020). Evaluation and selection of third party logistics provider under sustainability perspectives: an interval valued fuzzy-rough approach. Annals of Operations Research, 293(2), 669–714. Sahu, N. K., Sahu, A. K., & Sahu, A. K. (2015). Appraisement and benchmarking of third-party logistic service provider by exploration of risk-based approach. Cogent Business & Management, 2(1), 1121637. Sasikumar, P., & Haq, A. N. (2011). Integration of closed loop distribution supply chain network and 3PRLP selection for the case of battery recycling. International Journal of Production Research, 49(11), 3363–3385. Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2020). Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic. International Journal of Logistics Research and Applications, 1–21. Sharma, S. K., & Kumar, V. (2015). Optimal selection of third-party logistics service providers using quality function deployment and Taguchi loss function. Benchmarking: An International Journal. Singh, R. K., Gunasekaran, A., & Kumar, P. (2018). Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research, 267(1), 531–553. Subramoniam, R., Huisingh, D., Chinnam, R. B., & Subramoniam, S. (2013). Remanufacturing Decision-Making Framework (RDMF): research validation using the analytical hierarchical process. Journal of Cleaner Production, 40, 212–220. Tadić, S., Zečević, S., & Krstić, M. (2014). A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications, 41(18), 8112–8128. Torkayesh, S. E., Iranizad, A., Torkayesh, A. E., & Basit, M. N. (2020). Application of BWM-WASPAS model for digital supplier selection problem: A case study in online retail shopping. Journal of Industrial Engineering and Decision Making, 1(1), 12–23. Vahidi, F., Torabi, S. A., & Ramezankhani, M. J. (2018). Sustainable supplier selection and order allocation under operational and disruption risks. Journal of Cleaner Production, 174, 1351–1365. https://doi.org/10.1016/j.jclepro.2017.11.012 Vazifehdan, M. N., & Darestani, S. A. (2019). Green logistics outsourcing employing multi criteria decision making and quality function deployment in the petrochemical industry. The Asian Journal of Shipping and Logistics, 35(4), 243–254. Wang, C.-N., Nguyen, N.-A.-T., Dang, T.-T., & Lu, C.-M. (2021). A compromised decision-making approach to third-party logistics selection in sustainable supply chain using fuzzy AHP and fuzzy VIKOR methods. Mathematics, 9(8), 886. Wang, T.-Y., & Yang, Y.-H. (2009). A fuzzy model for supplier selection in quantity discount environments. Expert Systems with Applications, 36(10), 12179–12187. Xu, L., Kumar, D. T., Shankar, K. M., Kannan, D., & Chen, G. (2013). Analyzing criteria and sub-criteria for the corporate social responsibility-based supplier selection process using AHP. The International Journal of Advanced Manufacturing Technology, 68(1), 907–916. Yamaguchi, K. (2008). International trade and air cargo: Analysis of US export and air transport policy. Transportation Research Part E: Logistics and Transportation Review, 44(4), 653–663. Yang, Y. H., Hui, Y. Van, Leung, L. C., & Chen, G. (2010). An analytic network process approach to the selection of logistics service providers for air cargo. Journal of the Operational Research Society, 61(9), 1365–1376. Yayla, A. Y., Oztekin, A., Gumus, A. T., & Gunasekaran, A. (2015). A hybrid data analytic methodology for 3PL transportation provider evaluation using fuzzy multi-criteria decision making. International Journal of Production Research, 53(20), 6097–6113. Zamiela, C., Hossain, N. U. I., & Jaradat, R. (2021). Enablers of resilience in the healthcare supply chain: A case study of US healthcare industry during COVID-19 pandemic. Research in Transportation Economics, 101174. Zarbakhshnia, N., Soleimani, H., & Ghaderi, H. (2018). Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Applied Soft Computing, 65, 307–319. Zarbakhshnia, N., Wu, Y., Govindan, K., & Soleimani, H. (2020). A novel hybrid multiple attribute decision-making approach for outsourcing sustainable reverse logistics. Journal of Cleaner Production, 242, 118461. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika Ir Elektrotechnika, 122(6), 3–6. Zhang, A., & Zhang, Y. (2002). Issues on liberalization of air cargo services in international aviation. Journal of Air Transport Management, 8(5), 275–287.