Farklı kurumsal amaçlar altında bulanık ÇKKV tekniklerine dayalı en iyi geri kazanım opsiyonunun belirlenmesi
Abstract
Kullanılmış ürünlerin geri kazanılarak kapalı döngü bir ekonomi oluşturulması, ekonomik ve çevresel sürdürülebilirliğin sağlanması açısından büyük önem taşımaktadır. Geri kazanım sürecinden elde edilen faydayı maksimize etmek açısından en uygun geri kazanım opsiyonuna göre ürünlerin sisteme dahil edilmesi büyük önem taşımaktadır. Geri kazanım süreci kapsamına giren en önemli ürün tiplerinden birisi de bu çalışma kapsamında ele alınan kiralık ürünlerdir. Kiralık ürünlerin doğası gereği sıklıkla geri kazanım sürecine girmesi, geri kazanım opsiyonlarından en uygun olanının belirlenmesi ihtiyacını daha da önemli hale getirmektedir. İlgili karar sürecinde, uygulamada karşılaşılan durumlardan birisi de kurumların farklı departmanlarının farklı amaçlar içermesidir. Geri kazanım opsiyonlarının değerleri ilgili amaçlara göre farklılaşabilmektedir. Bu çalışmayla farklı departmanların amaçlarının göz önünde tutularak her bir geri kazanım opsiyonu için skor üretilebilen bir yapı sunulması hedeflenmektedir. Bu yapıda bulanık Çok Kriterli Karar Verme (ÇKKV) tekniklerinden Küresel Bulanık AHP (KB-AHP) ve Küresel Bulanık TOPSIS (KB-TOPSIS) bütünleşik bir şekilde kullanılmıştır. Sunulan yaklaşımın geçerliliğini göstermek için Türkiye’nin büyük telekomünikasyon şirketlerinden birinde kiralık modemler üzerinde uygulama yapılmıştır. Farklı departman ağırlıkları ve kriterlerin alabileceği farklı durumlar altında olası bütün senaryolar çeşitli açılardan analiz edilmiş ve yorumlanmıştır. Böylece departman hedeflerinin kriterler üzerindeki etkileri gözlemlenmiş ve kararlar üzerindeki etkileri sayısal olarak incelenebilmiştir.
Keywords
Geri kazanım opsiyonları Kiralık ürünler sürdürülebilirlik Çok kriterli karar verme KB-AHP KB-TOPSIS
Supporting Institution
TÜBİTAK
Project Number
121M176
References
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- Alamerew, Y. A., Brissaud, D. 2019. “Circular economy assessment tool for end of life product recovery strategies”, Journal of Remanufacturing, 9 (3), 169-185.
- Vahdani, B., Dehbari, S., & Beni, M. (2014). An artificial intelligence approach for fuzzy possibilistic-stochastic multi-objective logistics network design. Neural Comput & Applic, 1887–1902.
- Alamerew, Y. A., Brissaud, D. 2018. “Modelling and assessment of product recovery strategies through systems Dynamics”, Procedia CIRP, 69, 822-826.
- Okumura, S., Matsumoto, Y., Hatanaka, Y., Ogohara, K. 2016. “Simultaneous Evaluation of Environmental Impact and Incurred Cost on Selection of End-Of-Life Products Recovery Options”, International Journal of Automation Technology, 10(5), 699-707.
- Evler, J., Asadi, E., Preis, H., Fricke, H. 2021. “Airline ground operations: Schedule recovery optimization approach with constrained resources”, Transportation Research Part C: Emerging Technologies, 128, 103129.
- Chen, Y., Wang, J., Jia, X. 2020. “Refurbished or remanufactured An experimental study on consumer choice behavior”, Frontiers in psychology, 11, 781.
- Meng, K., Cao, Y., Peng, X., Prybutok, V., Youcef-Toumi, K. 2020. “Smart recovery decision-making for end-of-life products in the context of ubiquitous information and computational intelligence”, Journal of Cleaner Production, 272, 122804.
- Meng, K., Lou, P., Peng, X., Prybutok, V. 2016. “An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products”, International Journal of Production Research, 54 (18), 5567-5593.
- Parlikad, A. K., McFarlane, D. 2009. “A Bayesian decision support system for vehicle component recovery”, International Journal of Sustainable Manufacturing, 1 (4), 415.
- Barker, T., & Zabinsky, Z. (2011). A multicriteria decision making model for reverse logistics using analytical hierarchy process. Omega, 558–573.
- Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 50-58.
- Özceylan, E., & Paksoy, T. (2013). Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network. International Journal of Production Research, 2443–2461.
- Moghaddam, K. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 6237–6254.
- Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432).
- Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
- Smarandache, F. (2003, September). Definiton of neutrosophic logic-a generalization of the intuitionistic fuzzy logic. In EUSFLAT Conf. (pp. 141-146).
- Torra, V. (2010). Hesitant fuzzy sets. International journal of intelligent systems, 25(6), 529-539.
- Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of intelligent & fuzzy systems, 36(1), 337-352.
- Yager, R. R. (2013, June). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.
- Ayyildiz, E., & Taskin, A. (2022). A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul. Socio-Economic Planning Sciences, 101345.
- Kul, Y., Şeker, A., & Yurdakul, M. (2014). Usage of fuzzy multı crıterıa decısıon makıng methods ın selectıon of nontradıtıonal manufacturıng methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(3).
- Erdemir, N., Öztürk, F., & Kaya, G. K. (2022). Integrated decision support model for performance evaluation of public staff: using AHP and fuzzy TOPSIS. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(4), 1809-1822.
- Saaty, T. (1980, November). The analytic hierarchy process (AHP) for decision making. In Kobe, Japan (pp. 1-69).
- Sharaf, I. M. (2022). A New Approach for Spherical Fuzzy TOPSIS and Spherical Fuzzy VIKOR Applied to the Evaluation of Hydrogen Storage Systems.
Abstract
Project Number
121M176
References
- Kilic, H. S., Zaim, S., Delen, D. 2014. “Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines”, Decision Support Systems, 66, 82-92.
- Alamerew, Y. A., Brissaud, D. 2019. “Circular economy assessment tool for end of life product recovery strategies”, Journal of Remanufacturing, 9 (3), 169-185.
- Vahdani, B., Dehbari, S., & Beni, M. (2014). An artificial intelligence approach for fuzzy possibilistic-stochastic multi-objective logistics network design. Neural Comput & Applic, 1887–1902.
- Alamerew, Y. A., Brissaud, D. 2018. “Modelling and assessment of product recovery strategies through systems Dynamics”, Procedia CIRP, 69, 822-826.
- Okumura, S., Matsumoto, Y., Hatanaka, Y., Ogohara, K. 2016. “Simultaneous Evaluation of Environmental Impact and Incurred Cost on Selection of End-Of-Life Products Recovery Options”, International Journal of Automation Technology, 10(5), 699-707.
- Evler, J., Asadi, E., Preis, H., Fricke, H. 2021. “Airline ground operations: Schedule recovery optimization approach with constrained resources”, Transportation Research Part C: Emerging Technologies, 128, 103129.
- Chen, Y., Wang, J., Jia, X. 2020. “Refurbished or remanufactured An experimental study on consumer choice behavior”, Frontiers in psychology, 11, 781.
- Meng, K., Cao, Y., Peng, X., Prybutok, V., Youcef-Toumi, K. 2020. “Smart recovery decision-making for end-of-life products in the context of ubiquitous information and computational intelligence”, Journal of Cleaner Production, 272, 122804.
- Meng, K., Lou, P., Peng, X., Prybutok, V. 2016. “An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products”, International Journal of Production Research, 54 (18), 5567-5593.
- Parlikad, A. K., McFarlane, D. 2009. “A Bayesian decision support system for vehicle component recovery”, International Journal of Sustainable Manufacturing, 1 (4), 415.
- Barker, T., & Zabinsky, Z. (2011). A multicriteria decision making model for reverse logistics using analytical hierarchy process. Omega, 558–573.
- Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 50-58.
- Özceylan, E., & Paksoy, T. (2013). Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network. International Journal of Production Research, 2443–2461.
- Moghaddam, K. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 6237–6254.
- Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432).
- Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
- Smarandache, F. (2003, September). Definiton of neutrosophic logic-a generalization of the intuitionistic fuzzy logic. In EUSFLAT Conf. (pp. 141-146).
- Torra, V. (2010). Hesitant fuzzy sets. International journal of intelligent systems, 25(6), 529-539.
- Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of intelligent & fuzzy systems, 36(1), 337-352.
- Yager, R. R. (2013, June). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.
- Ayyildiz, E., & Taskin, A. (2022). A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul. Socio-Economic Planning Sciences, 101345.
- Kul, Y., Şeker, A., & Yurdakul, M. (2014). Usage of fuzzy multı crıterıa decısıon makıng methods ın selectıon of nontradıtıonal manufacturıng methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(3).
- Erdemir, N., Öztürk, F., & Kaya, G. K. (2022). Integrated decision support model for performance evaluation of public staff: using AHP and fuzzy TOPSIS. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(4), 1809-1822.
- Saaty, T. (1980, November). The analytic hierarchy process (AHP) for decision making. In Kobe, Japan (pp. 1-69).
- Sharaf, I. M. (2022). A New Approach for Spherical Fuzzy TOPSIS and Spherical Fuzzy VIKOR Applied to the Evaluation of Hydrogen Storage Systems.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Project Number | 121M176 |
| Early Pub Date | May 16, 2024 |
| Publication Date | May 20, 2024 |
| Submission Date | October 17, 2022 |
| Acceptance Date | September 17, 2023 |
| Published in Issue | Year 2024 Volume: 39 Issue: 3 |