En Uygun Tersine Lojistik Hizmet Sağlayıcısının Bulanık Grup Karar Verme Yaklaşımı Altında Belirlenmesi

Abstract

Günümüz pazar koşullarında işletmeler lojistik operasyonlarını maliyet ve rekabet avantajı sağlayacak etkin bir stratejiyle sürdürmek zorundadır. Müşterilerden gelen fiyat baskıları ve özellikle politik, ekonomik ve çevresel hassasiyetler gereği, ürün geri dönüşüm süreçleri -yani ürünün geri kazanılarak yeniden değerlendirilmesi- tersine lojistik (TL) uygulamalarını gerektirmektedir. İşletmeler için, TL yönetiminde genellikle özel bilgi sistemlerine sahip bir altyapı ve iadelerin işlenmesi için özel ekipmanlar gereklidir. Bu nedenle çoğu işletme sınırlı kaynakları ve teknik yeterlilikleri nedeniyle TL faaliyetlerini üçüncü parti TL sağlayıcılarına (3PTLS) devretmektedir. Uygun 3PTLS seçim süreci işletmelerin ekonomik karlılığına ve uzun vadeli gelişimine katkı sunması nedeniyle stratejik olarak önemli bir karardır. 3PTLS seçim kararı, çok sayıda belirsizlik içermesi ve karmaşık doğası gereği çok kriterli karar verme (ÇKKV) problemi olarak ele alınmaktadır. Bu çalışmada, en iyi 3PTLS seçimi için insan düşüncelerindeki belirsizlik ve karmaşıklığı daha iyi yansıtmak amacıyla Pisagor bulanık kümelere dayalı bir grup karar verme modeli olarak Pisagor bulanık TOPSIS yöntemi kullanılarak modellenmiştir. Modelin uygulanabilirliği, bir pil üretim şirketinden alınan verilere dayanan deneysel bir çalışma ile gösterilmiştir. Elde edilen sonuçlar diğer karar verme yöntemleriyle (bulanık TOPSIS, bulanık COPRAS ve klasik TOPSIS) karşılaştırılmış ve çözüm üstünlükleri sunulmuştur. Ayrıca önerilen modelin kararlılığını ve uygulanabilirliğini değerlendirmek için duyarlılık analizi yapılmıştır.

Keywords

• Tersine Lojistik
• Çok Kriterli Karar Verme
• Grup Karar Verme
• Pisagor Bulanık TOPSIS
• Bulanık COPRAS

Selection of the Appropriate Reverse Logistics Provider Under Fuzzy Group Decision Making Approach

Abstract

In today's market conditions, companies have to maintain their logistics operations with an effective strategy that will provide cost and competitive advantage. Pricing pressures from customers, and specifically due to political, economic and environmental sensitivities, require reverse logistics (RL) applications in product recycling processes, that is, for recycling and re-evaluation of the product. Companies usually require an infrastructure with special information systems and particular equipment for processing returns in RL management. For this reason, most companies transfer their RL activities to third-party RL providers (3PRLP) due to their limited resources and technical capabilities. The appropriate 3PTLS selection is a strategically important decision because of its contribution to the economic profitability and long-term development of businesses. 3PRLP selection decision is considered as a multi-criteria decision-making (MCDM) problem due to the high number of uncertainties and complicated natures. In this study, a multi-criteria group decision-making model based on Pythagorean fuzzy sets was modeled using the Pythagorean fuzzy TOPSIS method to better reflect the uncertainty and complexity in human views/opinions for the best 3PRLP selection. The applicability of the model has been demonstrated and tested by an experimental study based on data from a battery manufacturing company. The obtained results were compared with other decision-making methods (classical TOPSIS, fuzzy TOPSIS and fuzzy COPRAS) and solution advantages were presented in the study. In addition, sensitivity analysis was applied to evaluate the stability and applicability of the proposed model.

Keywords

• Reverse Logistic
• Multi-Criteria Decision Making
• Group Decision Making
• Pythagorean Fuzzy TOPSIS
• Fuzzy COPRAS

References

1. [1] S. C. L. Koh, M. Demirbag, E. Bayraktar, E. Tatoglu, and S. Zaim, “The impact of supply chain management practices on performance of SMEs,” Ind. Manag. Data Syst., vol. 107, no. 1, pp. 103–124, 2007, doi: 10.1108/02635570710719089.
2. [2] S. Zaim, M. Sevkli, and M. Tarim, “Fuzzy analytic hierarchy based approach for supplier selection,” in Euromarketing and the Future, Taylor and Francis, 2013, pp. 147–176.
3. [3] James Stock, “Reverse logistics: White paper,” Counc. Logist. Manag., 1992.
4. [4] S. Senthil, B. Srirangacharyulu, and A. Ramesh, “A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics,” Expert Syst. Appl., vol. 41, no. 1, pp. 50–58, Jan. 2014, Accessed: Jul. 29, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417413004879.
5. [5] G. Kannan, S. Pokharel, P. Sasi Kumar, and P. S. Kumar, “A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider,” Resour. Conserv. Recycl., vol. 54, no. 1, pp. 28–36, Nov. 2009, doi: 10.1016/J.RESCONREC.2009.06.004.
6. [6] G. Kannan, S. Pokharel, and P. S. Kumar, “A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider,” Resour. Conserv. Recycl., vol. 54, no. 1, pp. 28–36, Nov. 2009, doi: 10.1016/j.resconrec.2009.06.004.
7. [7] K. Govindan, M. Kadziński, R. Ehling, and G. Miebs, “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,” vol. 85, pp. 1–15, Jun. 2019, Accessed: Sep. 17, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S030504831730378X.
8. [8] S. K. Srivastava, “Network design for reverse logistics,” Omega, vol. 36, no. 4, pp. 535–548, Aug. 2008, doi: 10.1016/J.OMEGA.2006.11.012.

9. [9] Z.-S. Chen, X. Zhang, K. Govindan, X.-J. Wang, and K.-S. Chin, “Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach,” Expert Syst. Appl., vol. 166, p. 114051, Mar. 2021, doi: 10.1016/j.eswa.2020.114051.
10. [10] Y. Li, D. Kannan, K. Garg, S. Gupta, K. Gandhi, and P. C. Jha, “Business orientation policy and process analysis evaluation for establishing third party providers of reverse logistics services,” J. Clean. Prod., vol. 182, pp. 1033–1047, May 2018, doi: 10.1016/J.JCLEPRO.2017.12.241.
11. [11] S. Agrawal, R. K. Singh, and Q. Murtaza, “A literature review and perspectives in reverse logistics,” Resour. Conserv. Recycl., vol. 97, pp. 76–92, Apr. 2015, doi: 10.1016/j.resconrec.2015.02.009.
12. [12] K. Govindan, H. Soleimani, and D. Kannan, “Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future,” Eur. J. Oper. Res., vol. 240, no. 3, pp. 603–626, Feb. 2015, doi: 10.1016/j.ejor.2014.07.012.
13. [13] C. Prakash and M. K. Barua, “An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment,” Resour. Conserv. Recycl., vol. 108, pp. 63–81, Mar. 2016, doi: 10.1016/J.RESCONREC.2015.12.011.
14. [14] M. Tavana, M. Zareinejad, F. J. Santos-Arteaga, and M. A. Kaviani, “A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers,” Int. J. Adv. Manuf. Technol., vol. 86, no. 5–8, pp. 1705–1721, Sep. 2016, doi: 10.1007/s00170-015-8208-6.
15. [15] M. Azadi and R. F. Saen, “A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors,” Expert Syst. Appl., vol. 38, no. 10, pp. 12231–12236, Sep. 2011, doi: 10.1016/J.ESWA.2011.04.001.
16. [16] A. Ç. Suyabatmaz, F. T. Altekin, and G. Şahin, “Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider,” Comput. Ind. Eng., vol. 70, pp. 74–89, Apr. 2014, doi: 10.1016/J.CIE.2014.01.004.
17. [17] H. Min and H.-J. Ko, “The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers,” Int. J. Prod. Econ., vol. 113, no. 1, pp. 176–192, May 2008, doi: 10.1016/J.IJPE.2007.01.017.
18. [18] K. Govindan, M. Palaniappan, Q. Zhu, and D. Kannan, “Analysis of third party reverse logistics provider using interpretive structural modeling,” Int. J. Prod. Econ., vol. 140, no. 1, pp. 204–211, Nov. 2012, doi: 10.1016/J.IJPE.2012.01.043.
19. [19] V. Ravi, “Selection of third-party reverse logistics providers for End-of-Life computers using TOPSIS-AHP based approach,” Int. J. Logist. Syst. Manag., vol. 11, no. 1, p. 24, 2012, doi: 10.1504/IJLSM.2012.044048.
20. [20] K. Govindan and P. Murugesan, “Selection of third‐party reverse logistics provider using fuzzy extent analysis,” Benchmarking An Int. J., vol. 18, no. 1, pp. 149–167, Mar. 2011, doi: 10.1108/14635771111109869.
21. [21] R. R. Yager, “Pythagorean fuzzy subsets,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Jun. 2013, pp. 57–61, doi: 10.1109/IFSA-NAFIPS.2013.6608375.
22. [22] E. Ilbahar, A. Karaşan, S. Cebi, and C. Kahraman, “A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system,” Saf. Sci., vol. 103, pp. 124–136, Mar. 2018, doi: 10.1016/J.SSCI.2017.10.025.
23. [23] S. C. Onar, B. Oztaysı, and C. Kahraman, “Multicriteria Evaluation of Cloud Service Providers Using Pythagorean Fuzzy TOPSIS,” J. Mult. Log. Soft Comput., vol. 30, no. 2–3, pp. 263–283, 2018.
24. [24] X. Zhang and Z. Xu, “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets,” Int. J. Intell. Syst., vol. 29, no. 12, pp. 1061–1078, Dec. 2014, doi: 10.1002/int.21676.
25. [25] X. Zhang, “A Novel Approach Based on Similarity Measure for Pythagorean Fuzzy Multiple Criteria Group Decision Making,” Int. J. Intell. Syst., vol. 31, no. 6, pp. 593–611, Jun. 2016, doi: 10.1002/int.21796.
26. [26] T. Gedikli, B. C. Ervural, and D. T. Sen, “Evaluation of Maintenance Strategies Using Pythagorean Fuzzy TOPSIS Method,” in Advances in Intelligent Systems and Computing, Jul. 2021, vol. 1197 AISC, pp. 512–521, doi: 10.1007/978-3-030-51156-2_59.
27. [27] M. Akram, W. A. Dudek, and F. Ilyas, “Group decision‐making based on pythagorean fuzzy TOPSIS method,” Int. J. Intell. Syst., vol. 34, no. 7, pp. 1455–1475, Jul. 2019, doi: 10.1002/int.22103.
28. [28] M. Akram, W. A. Dudek, and F. Ilyas, “Group decision‐making based on pythagorean fuzzy TOPSIS method,” Int. J. Intell. Syst., vol. 34, no. 7, pp. 1455–1475, Jul. 2019, doi: 10.1002/int.22103.
29. [29] C. C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, vol. 186. New York: Springer, 1981.
30. [30] S. Opricovic and G.-H. Tzeng, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS,” Eur. J. Oper. Res., vol. 156, no. 2, pp. 445–455, Jul. 2004, doi: 10.1016/S0377-2217(03)00020-1.
31. [31] T. Gedikli and B. Cayir Ervural, “Selection Optimum Maintenance Strategy Using Multi-Criteria Decision Making Approaches,” in Industrial Engineering in the Digital Disruption Era, F. Calısır and O. Korhan, Eds. Springer, 2019.
32. [32] C.-T. Chen, “Extensions of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy Sets Syst., vol. 114, no. 1, pp. 1–9, Aug. 2000, doi: 10.1016/S0165-0114(97)00377-1.
33. [33] P. Guarnieri, V. A. Sobreiro, M. S. Nagano, and A. L. Marques Serrano, “The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case,” J. Clean. Prod., vol. 96, pp. 209–219, Jun. 2015, doi: 10.1016/j.jclepro.2014.05.040.