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ÜÇÜNCÜ TARAF LOJİSTİK SAĞLAYICI SEÇİMİNDE PFAHP-GTOPSIS YÖNTEMLERİNİN UYGULANMASI

Yıl 2024, Cilt: 14 Sayı: 1, 393 - 413, 25.03.2024
https://doi.org/10.30783/nevsosbilen.1435092

Öz

Küresel pazarlardaki rekabet, daha kaliteli ürün kullanımı ve artan müşteri talepleri nedeniyle tedarik zincirlerini güncellemek için temel becerilerine odaklanarak riskten korunmanın maliyet yaratması ve verimliliği artırması nedeniyle şirketler artık dış kaynak kullanma seçeneğini değerlendiriyor. Bu şirketler lojistikle ilgili birçok görevi Üçüncü Taraf Lojistik Sağlayıcılarına (3TLS) devretmeden önce hangi şirketle iş birliği yapacaklarını dikkatlice seçmeli ve belirlemelidirler. Ancak 3TLS seçim problemlerinde belirsizliklerin ve insan etkisinin varlığı, bulanık veya ilgili küme teorilerinin kullanılmasına yol açmaktadır. Çok Kriterli Karar Verme (ÇKKV) yöntemlerinin bulanık sayılar ve gri sayılarla birleştirilmesiyle, öznel yargıların belirsizliğini giderecek pratik araçlar oluşturulabilir. Bu perspektiften bakıldığında, 3PLP değerlendirme ve seçimine ışık tutacak bütünleşmiş bir ÇKKV modeli önerilmiştir. Önerilen model, Pisagor bulanık sayıları ve gri sayılardan oluşan entegre bir çerçeveden oluşmaktadır ve ilgili model gıda endüstrisindeki bir şirkette müşteri siparişlerini teslim etmek için kullanılan 3TLS'ye uygulanmıştır. Değerlendirme kriterleri ağırlıkları, Pisagor Bulanık Analitik Hiyerarşi Süreci (PBAHS) yöntemi kullanılarak hesaplanır ve 3PLP'ler, en iyi 3TLS'yi bulmak için Gri İdeal Çözüme Benzerliğe Göre Sipariş Tercihi Tekniği (GTOPSIS) yöntemleri kullanılarak sıralanır. Analizler ve bulgular, maliyet, hizmet kalitesi ve zamanında teslimatın en büyük etkiye sahip üç kriter olduğu sonucuna varmıştır.

Kaynakça

  • Aguezzoul, A., & Pires, S. (2016). 3PL performance evaluation and selection: a MCDM method. Supply Chain Forum: An International Journal, 17(2), 87–94.
  • Akman, G., & Baynal, K. (2014). Logistics service provider selection through an integrated fuzzy multicriteria decision making approach. Journal of Industrial Engineering, 2014.
  • Ali, S. S., Kaur, R., & Dubey, R. (2014). Analysis of 3PL sustainable relationship framework. International Journal of Services and Operations Management, 17(4), 404. https://doi.org/10.1504/IJSOM.2014.060000
  • Alkharabsheh, A., Moslem, S., Oubahman, L., & Duleba, S. (2021). An integrated approach of multi-criteria decision-making and grey theory for evaluating urban public transportation systems. Sustainability, 13(5), 2740.
  • Alwan, S. Y., Hu, Y., Al Asbahi, A. A. M. H., Al Harazi, Y. K., & Al Harazi, A. K. (2023). Sustainable and resilient e-commerce under COVID-19 pandemic: a hybrid grey decision-making approach. Environmental Science and Pollution Research, 1–21.
  • Arunagiri, R., Pandian, P., Krishnasamy, V., Ramasamy, R., & Sivaprakasam, R. (2023). Selection of browsers for smartphones: a fuzzy hybrid approach and machine learning technique. Knowledge and Information Systems, 1–26.
  • Aydın, S. (2021). A fuzzy MCDM method based on new Fermatean fuzzy theories. International Journal of Information Technology & Decision Making, 20(03), 881–902.
  • Badi, I., Alosta, A., Elmansouri, O., Abdulshahed, A., & Elsharief, S. (2023). An application of a novel grey-CODAS method to the selection of hub airport in North Africa. Decision Making: Applications in Management and Engineering, 6(1), 18–33.
  • Bayazit, O., & Karpak, B. (2013). Selection of a third party logistics service provider for an aerospace company: An analytical decision aiding approach. International Journal of Logistics Systems and Management, 15(4), 382–404. https://doi.org/10.1504/IJLSM.2013.054898
  • Bianchini, A. (2018). 3PL provider selection by AHP and TOPSIS methodology. Benchmarking: An International Journal, 25(1), 235–252. https://doi.org/10.1108/BIJ-08-2016-0125
  • Biswas, S., & Pamucar, D. (2023). A modified EDAS model for comparison of mobile wallet service providers in India. Financial Innovation, 9(1), 1–31.
  • Bulgurcu, B., & Nakiboglu, G. (2018). An extent analysis of 3PL provider selection criteria: A case on Turkey cement sector. Cogent Business & Management, 5(1), 1469183. https://doi.org/10.1080/23311975.2018.1469183
  • Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record, 2677(2), 1392–1400.
  • Cebi, S., Gündoğdu, F. K., & Kahraman, C. (2023). Consideration of reciprocal judgments through Decomposed Fuzzy Analytical Hierarchy Process: A case study in the pharmaceutical industry. Applied Soft Computing, 110000.
  • Çelikbilek, Y., & Tüysüz, F. (2016). An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy, 115, 1246–1258.
  • Chang, K.-H. (2023). Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues. Systems, 11(2), 74.
  • Chen, Y. M., Goan, M.-J., & Huang, P.-N. (2011). Selection process in logistics outsourcing – a view from third party logistics provider. Production Planning & Control, 22(3), 308–324. https://doi.org/10.1080/09537287.2010.498611
  • Daim, T. U., Udbye, A., & Balasubramanian, A. (2013). Use of analytic hierarchy process (AHP) for selection of 3PL providers. Journal of Manufacturing Technology Management, 24(1), 28–51. https://doi.org/10.1108/17410381311287472
  • Dey, A., LaGuardia, P., & Srinivasan, M. (2011). Building sustainability in logistics operations: a research agenda. Management Research Review, 34(11), 1237–1259. https://doi.org/10.1108/01409171111178774
  • 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.
  • 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.
  • Erdebilli, B., Gecer, E., Yılmaz, İ., Aksoy, T., Hacıoglu, U., Dinçer, H., & Yüksel, S. (2023). Q-ROF fuzzy TOPSIS and VIKOR methods for the selection of sustainable private health insurance policies. Sustainability, 15(12), 9229.
  • Erkayman, B., Gundogar, E., & Yılmaz, A. (2012). An integrated fuzzy approach for strategic alliance partner selection in third-party logistics. The Scientific World Journal, 2012.
  • 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.
  • Fan, J., Guan, R., & Wu, M. (2020). Z-MABAC method for the selection of third-party logistics suppliers in fuzzy environment. Ieee Access, 8, 199111–199119.
  • Gardas, B. B., D. Raut, R., & Narkhede, B. . (2019). Analysing the 3PL service provider’s evaluation criteria through a sustainable approach. International Journal of Productivity and Performance Management, 68(5), 958–980. https://doi.org/10.1108/IJPPM-04-2018-0154
  • Ghosh, P. (2023). Turkey Earthquake: Where Did It Hit and Why Was It so Deadly. BBC News, 6.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems.
  • Gürcan, Ö. F., Yazıcı, İ., Beyca, Ö. F., Arslan, Ç. Y., & Eldemir, F. (2016). Third party logistics (3PL) provider selection with AHP application. Procedia-Social and Behavioral Sciences, 235, 226–234.
  • 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.
  • Huo, H., & Wei, Z. (2008a). Grey multi-hierarchical evaluation of third party logistics providers in the environment of supply chain. 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, 1–4.
  • Huo, H., & Wei, Z. (2008b). Selection of third party logistics providers based on modified grey multi-hierarchical evaluation method. 2008 Chinese Control and Decision Conference, 2363–2368.
  • Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124–136.
  • Ji-Feng, D., & Chien-Chang, C. (2011). Middle managers selection for third-party logistics service providers. International Journal of Physical Sciences, 6(3), 610–619.
  • Jovčić, S., & Průša, P. (2021). A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection. Mathematics, 9(21), 2729.
  • Jovčić, S., Průša, P., Dobrodolac, M., & Švadlenka, L. (2019). A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach. Sustainability, 11(15), 4236. https://doi.org/10.3390/su11154236
  • Jung, H. (2017). Evaluation of Third Party Logistics Providers Considering Social Sustainability. Sustainability, 9(5), 777. https://doi.org/10.3390/su9050777
  • Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66–78.
  • Khuman, A. S., Yang, Y., & John, R. (2014). A commentary on some of the intrinsic differences between grey systems and fuzzy systems. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2032–2037.
  • Konstantinou, T., & Gkritza, K. (2023). Examining the barriers to electric truck adoption as a system: A Grey-DEMATEL approach. Transportation Research Interdisciplinary Perspectives, 17, 100746.
  • Kuo, M.-S., & Liang, G.-S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476–485.
  • Lakshmi Narayana, S., & Gopalan, V. (2023). Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model. Journal of Natural Fibers, 20(1), 2167033.
  • 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.
  • 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.
  • Luyen, L. A., & Thanh, N. Van. (2022). Logistics Service Provider Evaluation and Selection: Hybrid SERVQUAL–FAHP–TOPSIS Model. Processes, 10(5), 1024.
  • Mohammadkhani, A., & Mousavi, S. M. (2023). A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry. Expert Systems with Applications, 216, 119396.
  • Narkhede, B. E., Raut, R., Gardas, B., Luong, H. T., & Jha, M. (2017). Selection and evaluation of third party logistics service provider (3PLSP) by using an interpretive ranking process (IRP). Benchmarking: An International Journal, 24(6), 1597–1648. https://doi.org/10.1108/BIJ-04-2016-0055
  • Nel, J., De Goede, E., & Niemann, W. (2018). Supply chain disruptions: Insights from South African third-party logistics service providers and clients. Journal of Transport and Supply Chain Management, 12(1), 1–12.
  • Nguyen, N.-A.-T., Wang, C.-N., Dang, L.-T.-H., Dang, L.-T.-T., & Dang, T.-T. (2022). Selection of Cold Chain Logistics Service Providers Based on a Grey AHP and Grey COPRAS Framework: A Case Study in Vietnam. Axioms, 11(4), 154.
  • Özcan, E., & Ahıskalı, M. (2020). 3PL service provider selection with a goal programming model supported with multicriteria decision making approaches. Gazi University Journal of Science, 33(2), 413–427. https://doi.org/10.35378/gujs.552070
  • Oztaysi, B. (2014). A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems. Knowledge-Based Systems, 70, 44–54. https://doi.org/10.1016/J.KNOSYS.2014.02.010
  • 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.
  • 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. (2009). Evaluation of third‐party logistics (3PL) providers by using a two‐phase AHP and TOPSIS methodology. Benchmarking: An International Journal, 16(5), 588–604. https://doi.org/10.1108/14635770910987823
  • Pinar, A., & Boran, F. E. (2022). 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method. In q-Rung Orthopair Fuzzy Sets (pp. 285–301). Springer.
  • Pishdar, M., Danesh Shakib, M., Antucheviciene, J., & Vilkonis, A. (2021). Interval type-2 fuzzy super SBM network DEA for assessing sustainability performance of third-party logistics service providers considering circular economy strategies in the era of industry 4.0. Sustainability, 13(11), 6497.
  • Rahman, S., Ahsan, K., Yang, L., & Odgers, J. (2019). An Investigation into critical challenges for multinational third-party logistics providers operating in China. Journal of Business Research, 103, 607–619. https://doi.org/https://doi.org/10.1016/j.jbusres.2017.09.053
  • Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., & Lenny Koh, S. C. (2012). Generic balanced scorecard framework for third party logistics service provider. International Journal of Production Economics, 140(1), 269–282. https://doi.org/https://doi.org/10.1016/j.ijpe.2012.01.040
  • Raji, S. A., Akintuyi, A. O., Wunude, E. O., & Fashoto, B. (2023). Coupling MCDM-Based ensemble and AHP for the sustainable management of erosion risk in a tropical Sub-Saharan basin.
  • Raut, R. D., Gardas, B. B., Pushkar, S., & Narkhede, B. E. (2019). Third-party logistics service providers selection and evaluation: A hybrid AHP-DEA-COPRAS-G group decision-making approach. International Journal of Procurement Management, 12(6), 632–651. https://doi.org/10.1504/IJPM.2019.102936
  • Raut, R., Kharat, M., Kamble, S., & Kumar, C. S. (2018). Sustainable evaluation and selection of potential third-party logistics (3PL) providers: An integrated MCDM approach. Benchmarking: An International Journal, 25(1), 76–97. https://doi.org/10.1108/BIJ-05-2016-0065
  • 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, A. K., Sahu, A. K., & Sahu, N. K. (2017). Appraisements of material handling system in context of fiscal and environment extent: a comparative grey statistical analysis. The International Journal of Logistics Management.
  • Senturk, S., Erginel, N., & Yazırlı, Y. (2017). Interval Type-2 Fuzzy Analytic Network Process for Modelling a Third-party Logistics (3PL) Company. 28, 311–333.
  • 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, S. P., Adhikari, A., Majumdar, A., & Bisi, A. (2022). Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making-text mining-based investigation. Transportation Research Part E: Logistics and Transportation Review, 157, 102558.
  • Skender, H. P. (2023). An Analysis Of The Logistics Market And Third-Party Logistics Providers. Business Logistics in Modern Management, 23, 63–78.
  • So, S., Kim, J., Cheong, K., & Cho, G. (2006). Evaluating the service quality of third-party logistics service providers using the analytic hierarchy process. Journal of Information Systems and Technology Management, 3(3), 261–270. https://doi.org/10.1590/S1807-17752006000300001
  • SoonHu, S. (2010). A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process. African Journal of Business Management, 4(3), 339–349.
  • Sorooshian, S. (2023). Formulation of a Grey Sequence and an Optimization Solution to Present Multi-Layer Family Networks. Mathematics, 11(1), 144.
  • Tuljak-Suban, D., & Bajec, P. (2020). Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study. Symmetry, 12(5), 766. https://doi.org/10.3390/sym12050766
  • Ulutas, A. (2021). A grey hybrid model to select the optimal third-party logistics provider. South African Journal of Industrial Engineering, 32(1), 171–181.
  • Vafaeipour, M., Zolfani, S. H., Varzandeh, M. H. M., Derakhti, A., & Eshkalag, M. K. (2014). Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach. Energy Conversion and Management, 86, 653–663.
  • 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, M., Jie, F., & Abareshi, A. (2018). Improving logistics performance for one belt one road: a conceptual framework for supply chain risk management in Chinese third-party logistics providers. International Journal of Agile Systems and Management, 11(4), 364–380.
  • Wiangkam, N., Jamrus, T., & Sureeyatanapas, P. (2022). The decision-making for selecting cold chain logistics providers in the food industry. Engineering and Applied Science Research, 49(6), 811–818.
  • Yadav, S., Garg, D., & Luthra, S. (2020). Selection of third-party logistics services for internet of things-based agriculture supply chain management. International Journal of Logistics Systems and Management, 35(2), 204–230. https://doi.org/10.1504/IJLSM.2020.104780
  • Yang, Y., & John, R. (2012). Grey sets and greyness. Information Sciences, 185(1), 249–264.
  • Yang, Y., Liu, S., & John, R. (2013). Uncertainty representation of grey numbers and grey sets. IEEE Transactions on Cybernetics, 44(9), 1508–1517.
  • 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.
  • Yazdani, M., Zarate, P., Coulibaly, A., & Zavadskas, E. K. (2017). A group decision making support system in logistics and supply chain management. Expert Systems with Applications, 88, 376–392. https://doi.org/https://doi.org/10.1016/j.eswa.2017.07.014
  • Ying, Z., & Ru-Chao, Z. (2010). Study on the third party logistics service providers’ performance evaluation based on the weighted entropy and analysis process of grey relation. 2010 International Conference on Management Science & Engineering 17th Annual Conference Proceedings, 582–587.
  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.
  • Zadeh, L. A., Klir, G. J., & Yuan, B. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (Vol. 6). World scientific.
  • Zhang, X. (2016). Multicriteria Pythagorean fuzzy decision analysis: A hierarchical QUALIFLEX approach with the closeness index-based ranking methods. Information Sciences, 330, 104–124.
  • Zheng, Q. (2023). Method for a new risk assessment of urban inundation: G-DEMATEL–AHP. MethodsX, 101997.
  • Zhou, Y. (2014). The research on supplier selection model of the third party logistics based on grey clustering. International Journal of Modeling and Optimization, 4(6), 489.

APPLICATION OF PFAHP-GTOPSIS METHODS FOR THIRD-PARTY LOGISTICS PROVIDER SELECTION

Yıl 2024, Cilt: 14 Sayı: 1, 393 - 413, 25.03.2024
https://doi.org/10.30783/nevsosbilen.1435092

Öz

Companies are now considering the option of outsourcing as hedges cost and increase productivity by concentrating on their core skills to update their supply chains due to the competition in global markets, the use of higher-quality products, and rising customer demands. They must carefully select and identify which company to collaborate with before outsourcing their numerous logistics-related tasks to Third-Party Logistics Providers (3PLP). However, the existence of uncertainties and human influence in 3PLP selection problems leads to the usage of fuzzy or related set theories. By incorporating Multi-Criteria Decision Making (MCDM) methods with fuzzy numbers and grey numbers, practical tools can be composed to address the imprecision of subjective judgments. From this perspective, an integrated MCDM model is proposed to provide insight into the 3PLP evaluation and selection. The model comprises an integrated framework with Pythagorean fuzzy numbers and grey numbers. The proposed model has applied a 3PLP a company in the food industry to fulfill customer orders. The evaluation criteria weights are calculated using the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) method, and the 3PLPs are ranked using the grey Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) methods to find the best 3PLP. The analyses and findings concluded that cost, service quality, and on-time delivery were the three criteria that had the greatest influence

Kaynakça

  • Aguezzoul, A., & Pires, S. (2016). 3PL performance evaluation and selection: a MCDM method. Supply Chain Forum: An International Journal, 17(2), 87–94.
  • Akman, G., & Baynal, K. (2014). Logistics service provider selection through an integrated fuzzy multicriteria decision making approach. Journal of Industrial Engineering, 2014.
  • Ali, S. S., Kaur, R., & Dubey, R. (2014). Analysis of 3PL sustainable relationship framework. International Journal of Services and Operations Management, 17(4), 404. https://doi.org/10.1504/IJSOM.2014.060000
  • Alkharabsheh, A., Moslem, S., Oubahman, L., & Duleba, S. (2021). An integrated approach of multi-criteria decision-making and grey theory for evaluating urban public transportation systems. Sustainability, 13(5), 2740.
  • Alwan, S. Y., Hu, Y., Al Asbahi, A. A. M. H., Al Harazi, Y. K., & Al Harazi, A. K. (2023). Sustainable and resilient e-commerce under COVID-19 pandemic: a hybrid grey decision-making approach. Environmental Science and Pollution Research, 1–21.
  • Arunagiri, R., Pandian, P., Krishnasamy, V., Ramasamy, R., & Sivaprakasam, R. (2023). Selection of browsers for smartphones: a fuzzy hybrid approach and machine learning technique. Knowledge and Information Systems, 1–26.
  • Aydın, S. (2021). A fuzzy MCDM method based on new Fermatean fuzzy theories. International Journal of Information Technology & Decision Making, 20(03), 881–902.
  • Badi, I., Alosta, A., Elmansouri, O., Abdulshahed, A., & Elsharief, S. (2023). An application of a novel grey-CODAS method to the selection of hub airport in North Africa. Decision Making: Applications in Management and Engineering, 6(1), 18–33.
  • Bayazit, O., & Karpak, B. (2013). Selection of a third party logistics service provider for an aerospace company: An analytical decision aiding approach. International Journal of Logistics Systems and Management, 15(4), 382–404. https://doi.org/10.1504/IJLSM.2013.054898
  • Bianchini, A. (2018). 3PL provider selection by AHP and TOPSIS methodology. Benchmarking: An International Journal, 25(1), 235–252. https://doi.org/10.1108/BIJ-08-2016-0125
  • Biswas, S., & Pamucar, D. (2023). A modified EDAS model for comparison of mobile wallet service providers in India. Financial Innovation, 9(1), 1–31.
  • Bulgurcu, B., & Nakiboglu, G. (2018). An extent analysis of 3PL provider selection criteria: A case on Turkey cement sector. Cogent Business & Management, 5(1), 1469183. https://doi.org/10.1080/23311975.2018.1469183
  • Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record, 2677(2), 1392–1400.
  • Cebi, S., Gündoğdu, F. K., & Kahraman, C. (2023). Consideration of reciprocal judgments through Decomposed Fuzzy Analytical Hierarchy Process: A case study in the pharmaceutical industry. Applied Soft Computing, 110000.
  • Çelikbilek, Y., & Tüysüz, F. (2016). An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy, 115, 1246–1258.
  • Chang, K.-H. (2023). Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues. Systems, 11(2), 74.
  • Chen, Y. M., Goan, M.-J., & Huang, P.-N. (2011). Selection process in logistics outsourcing – a view from third party logistics provider. Production Planning & Control, 22(3), 308–324. https://doi.org/10.1080/09537287.2010.498611
  • Daim, T. U., Udbye, A., & Balasubramanian, A. (2013). Use of analytic hierarchy process (AHP) for selection of 3PL providers. Journal of Manufacturing Technology Management, 24(1), 28–51. https://doi.org/10.1108/17410381311287472
  • Dey, A., LaGuardia, P., & Srinivasan, M. (2011). Building sustainability in logistics operations: a research agenda. Management Research Review, 34(11), 1237–1259. https://doi.org/10.1108/01409171111178774
  • 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.
  • 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.
  • Erdebilli, B., Gecer, E., Yılmaz, İ., Aksoy, T., Hacıoglu, U., Dinçer, H., & Yüksel, S. (2023). Q-ROF fuzzy TOPSIS and VIKOR methods for the selection of sustainable private health insurance policies. Sustainability, 15(12), 9229.
  • Erkayman, B., Gundogar, E., & Yılmaz, A. (2012). An integrated fuzzy approach for strategic alliance partner selection in third-party logistics. The Scientific World Journal, 2012.
  • 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.
  • Fan, J., Guan, R., & Wu, M. (2020). Z-MABAC method for the selection of third-party logistics suppliers in fuzzy environment. Ieee Access, 8, 199111–199119.
  • Gardas, B. B., D. Raut, R., & Narkhede, B. . (2019). Analysing the 3PL service provider’s evaluation criteria through a sustainable approach. International Journal of Productivity and Performance Management, 68(5), 958–980. https://doi.org/10.1108/IJPPM-04-2018-0154
  • Ghosh, P. (2023). Turkey Earthquake: Where Did It Hit and Why Was It so Deadly. BBC News, 6.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems.
  • Gürcan, Ö. F., Yazıcı, İ., Beyca, Ö. F., Arslan, Ç. Y., & Eldemir, F. (2016). Third party logistics (3PL) provider selection with AHP application. Procedia-Social and Behavioral Sciences, 235, 226–234.
  • 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.
  • Huo, H., & Wei, Z. (2008a). Grey multi-hierarchical evaluation of third party logistics providers in the environment of supply chain. 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, 1–4.
  • Huo, H., & Wei, Z. (2008b). Selection of third party logistics providers based on modified grey multi-hierarchical evaluation method. 2008 Chinese Control and Decision Conference, 2363–2368.
  • Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124–136.
  • Ji-Feng, D., & Chien-Chang, C. (2011). Middle managers selection for third-party logistics service providers. International Journal of Physical Sciences, 6(3), 610–619.
  • Jovčić, S., & Průša, P. (2021). A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection. Mathematics, 9(21), 2729.
  • Jovčić, S., Průša, P., Dobrodolac, M., & Švadlenka, L. (2019). A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach. Sustainability, 11(15), 4236. https://doi.org/10.3390/su11154236
  • Jung, H. (2017). Evaluation of Third Party Logistics Providers Considering Social Sustainability. Sustainability, 9(5), 777. https://doi.org/10.3390/su9050777
  • Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66–78.
  • Khuman, A. S., Yang, Y., & John, R. (2014). A commentary on some of the intrinsic differences between grey systems and fuzzy systems. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2032–2037.
  • Konstantinou, T., & Gkritza, K. (2023). Examining the barriers to electric truck adoption as a system: A Grey-DEMATEL approach. Transportation Research Interdisciplinary Perspectives, 17, 100746.
  • Kuo, M.-S., & Liang, G.-S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476–485.
  • Lakshmi Narayana, S., & Gopalan, V. (2023). Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model. Journal of Natural Fibers, 20(1), 2167033.
  • 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.
  • 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.
  • Luyen, L. A., & Thanh, N. Van. (2022). Logistics Service Provider Evaluation and Selection: Hybrid SERVQUAL–FAHP–TOPSIS Model. Processes, 10(5), 1024.
  • Mohammadkhani, A., & Mousavi, S. M. (2023). A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry. Expert Systems with Applications, 216, 119396.
  • Narkhede, B. E., Raut, R., Gardas, B., Luong, H. T., & Jha, M. (2017). Selection and evaluation of third party logistics service provider (3PLSP) by using an interpretive ranking process (IRP). Benchmarking: An International Journal, 24(6), 1597–1648. https://doi.org/10.1108/BIJ-04-2016-0055
  • Nel, J., De Goede, E., & Niemann, W. (2018). Supply chain disruptions: Insights from South African third-party logistics service providers and clients. Journal of Transport and Supply Chain Management, 12(1), 1–12.
  • Nguyen, N.-A.-T., Wang, C.-N., Dang, L.-T.-H., Dang, L.-T.-T., & Dang, T.-T. (2022). Selection of Cold Chain Logistics Service Providers Based on a Grey AHP and Grey COPRAS Framework: A Case Study in Vietnam. Axioms, 11(4), 154.
  • Özcan, E., & Ahıskalı, M. (2020). 3PL service provider selection with a goal programming model supported with multicriteria decision making approaches. Gazi University Journal of Science, 33(2), 413–427. https://doi.org/10.35378/gujs.552070
  • Oztaysi, B. (2014). A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems. Knowledge-Based Systems, 70, 44–54. https://doi.org/10.1016/J.KNOSYS.2014.02.010
  • 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.
  • 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. (2009). Evaluation of third‐party logistics (3PL) providers by using a two‐phase AHP and TOPSIS methodology. Benchmarking: An International Journal, 16(5), 588–604. https://doi.org/10.1108/14635770910987823
  • Pinar, A., & Boran, F. E. (2022). 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method. In q-Rung Orthopair Fuzzy Sets (pp. 285–301). Springer.
  • Pishdar, M., Danesh Shakib, M., Antucheviciene, J., & Vilkonis, A. (2021). Interval type-2 fuzzy super SBM network DEA for assessing sustainability performance of third-party logistics service providers considering circular economy strategies in the era of industry 4.0. Sustainability, 13(11), 6497.
  • Rahman, S., Ahsan, K., Yang, L., & Odgers, J. (2019). An Investigation into critical challenges for multinational third-party logistics providers operating in China. Journal of Business Research, 103, 607–619. https://doi.org/https://doi.org/10.1016/j.jbusres.2017.09.053
  • Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., & Lenny Koh, S. C. (2012). Generic balanced scorecard framework for third party logistics service provider. International Journal of Production Economics, 140(1), 269–282. https://doi.org/https://doi.org/10.1016/j.ijpe.2012.01.040
  • Raji, S. A., Akintuyi, A. O., Wunude, E. O., & Fashoto, B. (2023). Coupling MCDM-Based ensemble and AHP for the sustainable management of erosion risk in a tropical Sub-Saharan basin.
  • Raut, R. D., Gardas, B. B., Pushkar, S., & Narkhede, B. E. (2019). Third-party logistics service providers selection and evaluation: A hybrid AHP-DEA-COPRAS-G group decision-making approach. International Journal of Procurement Management, 12(6), 632–651. https://doi.org/10.1504/IJPM.2019.102936
  • Raut, R., Kharat, M., Kamble, S., & Kumar, C. S. (2018). Sustainable evaluation and selection of potential third-party logistics (3PL) providers: An integrated MCDM approach. Benchmarking: An International Journal, 25(1), 76–97. https://doi.org/10.1108/BIJ-05-2016-0065
  • 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, A. K., Sahu, A. K., & Sahu, N. K. (2017). Appraisements of material handling system in context of fiscal and environment extent: a comparative grey statistical analysis. The International Journal of Logistics Management.
  • Senturk, S., Erginel, N., & Yazırlı, Y. (2017). Interval Type-2 Fuzzy Analytic Network Process for Modelling a Third-party Logistics (3PL) Company. 28, 311–333.
  • 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, S. P., Adhikari, A., Majumdar, A., & Bisi, A. (2022). Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making-text mining-based investigation. Transportation Research Part E: Logistics and Transportation Review, 157, 102558.
  • Skender, H. P. (2023). An Analysis Of The Logistics Market And Third-Party Logistics Providers. Business Logistics in Modern Management, 23, 63–78.
  • So, S., Kim, J., Cheong, K., & Cho, G. (2006). Evaluating the service quality of third-party logistics service providers using the analytic hierarchy process. Journal of Information Systems and Technology Management, 3(3), 261–270. https://doi.org/10.1590/S1807-17752006000300001
  • SoonHu, S. (2010). A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process. African Journal of Business Management, 4(3), 339–349.
  • Sorooshian, S. (2023). Formulation of a Grey Sequence and an Optimization Solution to Present Multi-Layer Family Networks. Mathematics, 11(1), 144.
  • Tuljak-Suban, D., & Bajec, P. (2020). Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study. Symmetry, 12(5), 766. https://doi.org/10.3390/sym12050766
  • Ulutas, A. (2021). A grey hybrid model to select the optimal third-party logistics provider. South African Journal of Industrial Engineering, 32(1), 171–181.
  • Vafaeipour, M., Zolfani, S. H., Varzandeh, M. H. M., Derakhti, A., & Eshkalag, M. K. (2014). Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach. Energy Conversion and Management, 86, 653–663.
  • 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, M., Jie, F., & Abareshi, A. (2018). Improving logistics performance for one belt one road: a conceptual framework for supply chain risk management in Chinese third-party logistics providers. International Journal of Agile Systems and Management, 11(4), 364–380.
  • Wiangkam, N., Jamrus, T., & Sureeyatanapas, P. (2022). The decision-making for selecting cold chain logistics providers in the food industry. Engineering and Applied Science Research, 49(6), 811–818.
  • Yadav, S., Garg, D., & Luthra, S. (2020). Selection of third-party logistics services for internet of things-based agriculture supply chain management. International Journal of Logistics Systems and Management, 35(2), 204–230. https://doi.org/10.1504/IJLSM.2020.104780
  • Yang, Y., & John, R. (2012). Grey sets and greyness. Information Sciences, 185(1), 249–264.
  • Yang, Y., Liu, S., & John, R. (2013). Uncertainty representation of grey numbers and grey sets. IEEE Transactions on Cybernetics, 44(9), 1508–1517.
  • 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.
  • Yazdani, M., Zarate, P., Coulibaly, A., & Zavadskas, E. K. (2017). A group decision making support system in logistics and supply chain management. Expert Systems with Applications, 88, 376–392. https://doi.org/https://doi.org/10.1016/j.eswa.2017.07.014
  • Ying, Z., & Ru-Chao, Z. (2010). Study on the third party logistics service providers’ performance evaluation based on the weighted entropy and analysis process of grey relation. 2010 International Conference on Management Science & Engineering 17th Annual Conference Proceedings, 582–587.
  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.
  • Zadeh, L. A., Klir, G. J., & Yuan, B. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (Vol. 6). World scientific.
  • Zhang, X. (2016). Multicriteria Pythagorean fuzzy decision analysis: A hierarchical QUALIFLEX approach with the closeness index-based ranking methods. Information Sciences, 330, 104–124.
  • Zheng, Q. (2023). Method for a new risk assessment of urban inundation: G-DEMATEL–AHP. MethodsX, 101997.
  • Zhou, Y. (2014). The research on supplier selection model of the third party logistics based on grey clustering. International Journal of Modeling and Optimization, 4(6), 489.
Toplam 87 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uluslararası Lojistik
Bölüm Makaleler
Yazarlar

Sinan Çizmecioğlu 0000-0002-3355-8882

Esra Boz 0000-0002-1522-1768

Ahmet Çalık 0000-0002-6796-0052

Erken Görünüm Tarihi 20 Mart 2024
Yayımlanma Tarihi 25 Mart 2024
Gönderilme Tarihi 11 Şubat 2024
Kabul Tarihi 14 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 1

Kaynak Göster

APA Çizmecioğlu, S., Boz, E., & Çalık, A. (2024). APPLICATION OF PFAHP-GTOPSIS METHODS FOR THIRD-PARTY LOGISTICS PROVIDER SELECTION. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 14(1), 393-413. https://doi.org/10.30783/nevsosbilen.1435092