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ÜÇÜNCÜ TARAF LOJİSTİK SAĞLAYICI SEÇİMİNDE PFAHP-GTOPSIS YÖNTEMLERİNİN UYGULANMASI

Yıl 2024, , 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

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

Yıl 2024, , 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.
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  • 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
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  • 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
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  • 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.
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  • 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

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