SavedAdd your journal

Journals / Journal of Transportation and Logistics / 2026 / Volume: 11 Issue: 1

An Integrated Collection-Disassembly-Distribution Model with Fuzzy Demand: Toward Sustainable Reverse Logistics

Yusuf Bayhan(HASAN KALYONCU UNIVERSITY)0009-0000-0858-9022
Zeynel Abidin Çil(IZMIR DEMOCRACY UNIVERSITY)0000-0002-7270-9321
Zülal Diri Kenger(GAZIANTEP UNIVERSITY)0000-0002-0114-8870
Pages
—
Publication Date
2026-05-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the proposed model provides a realistic decision-support framework that accounts for the vagueness faced by decision-makers and manufacturers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study addresses the integrated collection–disassembly–distribution problem under fuzzy demand.
📊
Key Findings & Data:The problem provides a roadmap for effectively managing reverse supply chains under uncertain conditions.
💡
Conclusion & Impact:The results show that the proposed model provides a realistic decision-support framework that accounts for the vagueness faced by decision-makers and manufacturers.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Disassembly line balancing · environmental sustainability · fuzzy demand

Abstract

This study addresses the integrated collection–disassembly–distribution problem under fuzzy demand. The problem provides a roadmap for effectively managing reverse supply chains under uncertain conditions. It involves collecting end-of-life products, disassembling them, and distributing the components obtained from the disassembly process to demand centers under fuzzy demand. The reuse of valuable components not only provides cost savings but also supports sustainable supply chain practices. A nonlinear mathematical model is developed to represent the problem and is tested through various scenario analyses. The results show that the proposed model provides a realistic decision-support framework that accounts for the vagueness faced by decision-makers and manufacturers. Moreover, the findings indicate that it is possible to achieve cost-effectiveness and efficient resource utilization even in the presence of demand ambiguity, highlighting the model’s applicability to industries operating in volatile and unpredictable market environments.

🔖

Cite This Article

Bayhan, Y., Çil, Z. A., Kenger, Z. D. (2026). An Integrated Collection-Disassembly-Distribution Model with Fuzzy Demand: Toward Sustainable Reverse Logistics. Journal of Transportation and Logistics, Volume: 11 Issue: 1. https://doi.org/10.26650/JTL.2026.1866833

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (32)

  1. Al-Sheyadi, A., Muyldermans, L., & Kauppi, K. (2019). The complementarity of green supply chain management practices and the impact on environmental performance. _Journal of Environmental Management_, _242_, 186-198. https://doi.org/10.1016/j.jenvman.2019.04.078 google scholar
  2. Augerat, P., Belenguer, J. M., Benavent, E., Corberan, A., Naddef, D., & Rinaldi, G. (1995). _Computational results with a branch and cut code for the capacitated vehicle routing problem_. Rapport de recherche-IMAG. google scholar
  3. Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009) Robust Optimization. _Princeton University Press_, Princeton. https://doi.org/10.1515/9781400831050 google scholar
  4. Bertsimas, D., & Sim, M. (2004). The price of robustness. _Operations Research_. _52_(1), 35-53. https://doi.org/10.1287/opre.1030.0065 google scholar
  5. Birge, J.R. & Louveaux, F. (2011) Introduction to Stochastic Programming. _Springer_, NewYork. https://doi.org/10.1007/978-1-4614-0237-4 google scholar
  6. Cao, E., & Lai, M. (2010). The open vehicle routing problem with fuzzy demands. _Expert Systems with Applications_, _37_(3), 2405-2411. https://doi.org/10.1016/j.eswa.2009.07.021 google scholar
  7. Çil, Z. A., Öztop, H., Diri Kenger, Z., & Kizilay, D. (2023). Integrating distributed disassembly line balancing and vehicle routing problem in supply chain: Integer programming, constraint programming, and heuristic algorithms. _International Journal of Production Economics_, 109014. https://doi.org/10.1016/j.ijpe.2023.109014 google scholar
  8. Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. _Management Science_, _6_(1), 80-91. google scholar
  9. Diri Kenger, Z., Koç, Ç., & Özceylan, E. (2020a). Integrated disassembly line balancing and routing problem. _International Journal of Production Research_, 1-19. https://doi.org/10.1080/00207543.2020.1740346 google scholar
  10. Diri Kenger, Z., Koç, Ç., & Özceylan, E. (2020b). Smart Factories: Integrated Disassembly Line Balancing and Routing Problem with 3D Printers. _Logistics 4.0_. CRC Press. google scholar
  11. Diri Kenger, Z., Koç, Ç., & Özceylan, E. (2021a). Integrated Collection-Disassembly-Distribution Problem. _Sustainable Production and Logistics_. CRC Press. google scholar
  12. Diri Kenger, Z., Koç, Ç., & Özceylan, E. (2021b). Integrated disassembly line balancing and routing problem with mobile additive manufacturing. _International Journal of Production Economics_, _235_, 108088. https://doi.org/10.1016/j.ijpe.2021.108088 google scholar
  13. Durmaz, N., & Budak, A. (2025). An integrated Bi-objective green vehicle routing and partial disassembly line problem for electronic waste: An industrial case study. _International Journal of Computer Integrated Manufacturing_, _38_(3), 408-433. https://doi.org/10.1080/0951192X.2024.2335984 google scholar
  14. E-wastemonitor, 2024. The Global E-waste Monitor 2024. E-Waste Monitor. https://ewastemonitor.info/the-global-e-waste-monitor-2024/ (accessed 19 January 2026). google scholar
  15. Habibi, M. K. K., Battaïa, O., Cung, V.-D., & Dolgui, A. (2014). Integrated Procurement–Disassembly Problem. In B. Grabot, B. Vallespir, S. Gomes, A. Bouras, & D. Kiritsis (Ed.), _Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World_ (pp. 482-490). Springer Berlin Heidelberg. google scholar
  16. Hu, P., Chu, F., Dolgui, A., Chu, C., & Liu, M. (2024). Integrated multi-product reverse supply chain design and disassembly line balancing under uncertainty. _Omega_, _126_, 103062. https://doi.org/10.1016/j.omega.2024.103062 google scholar
  17. Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. _Journal of Environmental Management_, _91_(3), 563-591. https://doi.org/10.1016/j.jenvman.2009.09.037 google scholar
  18. Jiménez, M., Arenas, M., Bilbao, A., & Rodrı´guez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. _European Journal of Operational Research_, _177_(3), 1599-1609. https://doi.org/10.1016/j.ejor.2005.10.002 google scholar
  19. Kannan, D., Garg, K., Jha, P. C., & Diabat, A. (2017). Integrating disassembly line balancing in the planning of a reverse logistics network from the perspective of a third party provider. _Annals of Operations Research_, _253_(1), 353-376. https://doi.org/10.1007/s10479-016-2272-7 google scholar
  20. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review, _European Journal of Operational Research_, 203(2), 283-293. https://doi.org/10.1016/j.ejor.2009.06.011 google scholar
  21. Özceylan, E., & Paksoy, T. (2013). Reverse supply chain optimisation with disassembly line balancing. _International Journal of Production Research_, _51_(20), 5985-6001. https://doi.org/10.1080/00207543.2013.784405 google scholar
  22. Özceylan, E., & Paksoy, T. (2014). Fuzzy mathematical programming approaches for reverse supply chain optimization with disassembly line balancing problem. _Journal of Intelligent & Fuzzy Systems_, (26), 1969-1985. https://doi.org/10.3233/IFS-130875 google scholar
  23. Özceylan, E., Paksoy, T., & Bektaş, T. (2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. _Transportation Research Part E: Logistics and Transportation Review_, _61_, 142-164. https://doi.org/10.1016/j.tre.2013.11.001 google scholar
  24. Paksoy, T., Güngör, A., Özceylan, E., & Hancilar, A. (2013). Mixed model disassembly line balancing problem with fuzzy goals. _International Journal of Production Research_, _51_(20), 6082-6096. https://doi.org/10.1080/00207543.2013.795251 google scholar
  25. Serpe, A., Purchase, D., Bisschop, L., Chatterjee, D., De Gioannis, G., Garelick, H., & Verbeek, S. (2025). 2002–2022: 20 years of e-waste regulation in the European Union and the worldwide trends in legislation and innovation technologies for a circular economy. _RSC Sustainability_, _3_(3), 1039-1083. https://doi.org/10.1039/D4SU00548A google scholar
  26. Tang, Y. (2002). Disassembly Modeling, Planning, and Application. _Journal of Manufacturing Systems_. _21_, 200-217. https://doi.org/10.1016/S0278-6125(02)80162-5 google scholar
  27. Tasoglu, G., & Ilgin, M. A. (2024). A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing. _Computers & Industrial Engineering_, _187_, 109794. https://doi.org/10.1016/j.cie.2023.109794 google scholar
  28. Teodorović, D., & Pavković, G. (1996). The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain. _Fuzzy Sets and Systems_, _82_(3), 307-317. https://doi.org/10.1016/0165-0114(95)00276-6 google scholar
  29. Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. _Journal of Intelligent Manufacturing_, _25_(4), 647-659. https://doi.org/10.1007/s10845-012-0711-0 google scholar
  30. Werners, B., & Drawe, M. (2003). Capacitated Vehicle Routing Problem with Fuzzy Demand. J.-L. Verdegay (Ed.), _Fuzzy Sets Based Heuristics for Optimization_ (pp. 317-335). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-36461-0_21 google scholar
  31. Yolmeh, A., & Saif, U. (2021). Closed-loop supply chain network design integrated with assembly and disassembly line balancing under uncertainty: An enhanced decomposition approach. _International Journal of Production Research_, _59_(9), 2690-2707. https://doi.org/10.1080/00207543.2020.1736723 google scholar Zadeh, L.A. (1965). Fuzzy sets. _Information and Control_. _8_(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X google scholar
  32. Zimmermann, H. J. (2001). _Fuzzy Set Theory—And Its Applications_. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-010-0646-0 google scholar