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Designing a decentralized reverse logistics system for waste management

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: Reducing the amount of waste sent to landfills requires maximizing the reuse of materials, which in turn depends on effective waste segregation at the source. Once segregated, waste can be efficiently collected through a reverse logistics system (RLS). The need for improved segregation, collection, and recycling underscores the importance of innovative solutions. Methods: This study proposes an approach to modernizing conventional RLSs using blockchain technology. The approach was simulated using NetLogo agent-based modeling software, and its performance is compared to traditional RLSs. The "blockchain-based" system can implement policies to increase waste segregation at the source and leverage smart contracts to enhance automation and operational efficiency. Results: The proposed system achieved exceptional performance, with an average failure rate of less than 2% for collecting segregated waste. In contrast, traditional systems left approximately 45% of segregated waste uncollected. By providing clear information on the location and quantity of segregated waste, the system enables optimized fleet allocation to ensure near-complete collection. Additionally, the approach demonstrates strong adaptability, maintaining operational stability even during sudden fluctuations in waste supply. Conclusions: The blockchain-based RLS offers superior transparency, security, and efficiency compared to conventional systems. Its ability to achieve near-complete waste collection while maintaining high automation positions it as a promising, sustainable solution for modern waste management.
Czasopismo
Rocznik
Strony
347--359
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Transportation Planning, Imam Khomeini International University, Qazvin, Iran
  • Department of Transportation Planning, Imam Khomeini International University, Qazvin, Iran
Bibliografia
  • 1. Amini, M., Wakolbinger, T., Racer, M., and Nejad, M. G. 01 . “Alternative supply chain production-sales policies for new product diffusion: An agent-based modeling and simulation approach.” uropean Journal of Operational Research 216 (2), 301–11 https://doi.org/10.1016/j.ejor.2011.07.040
  • 2. Aponte-Novoa, F. A., Sandoval Orozco, A. L., Villanueva-Polanco, R., and Wightman, P. 0 1. “The 1% attack on blockchains: A mining behavior study.” Access 9:140549–64. https://doi.org/10.1109/ACCESS.2021.3119291
  • 3. Behdani, B. 01 . “ valuation of paradigms for modeling supply chains as complex socio-technical systems.” n Proceedings - Winter Simulation Conference, (pp. 1–15) https://doi.org/10.1109/WSC.2012.6465109
  • 4. Bing, X., Bloemhof-Ruwaard, J. M., and van der Vorst, J. G. A. J. 014. “Sustainable reverse logistics network design for household plastic waste.” Flexible Services and Manufacturing Journal 26 (1–2), 119–42. https://doi.org/10.1007/s10696-012-9149-0
  • 5. Brito, M. P., and Dekker, R. 004. “A framework for reverse logistics.” Reverse Logistics, 3–27. https://doi.org/10.1007/978-3-540-24803-3_1
  • 6. Chan, H. K., and Chan, F. T. S. 2010. “Comparative study of adaptability and flexibility in distributed manufacturing supply chains.” ecision Support Systems 48 (2), 331–341 https://doi.org/10.1016/j.dss.2009.09.001
  • 7. Chang, S. E., Chen, Y. C., and Lu, M. F. 2019. “Supply chain re-engineering using blockchain technology: A case of smart contract based tracking process.” Technological Forecasting and Social Change 144, 1–11. https://doi.org/10.1016/j.techfore.2019.03.015
  • 8. Chen, X., Ong, Y. S., Tan, P. S., Zhang, N. S., and Li, Z. 01 . “Agent-based modeling and simulation for supply chain risk management - A survey of the state-of-the-art.” n Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC (pp. 1294-1299). 2013. https://doi.org/10.1109/SMC.2013.224
  • 9. Choi, T. M. 01 . “Blockchain-technology-supported platforms for diamond authentication and certification in luxury supply chains.” Transportation Research Part E: Logistics and Transportation Review 128, 17–29. https://doi.org/10.1016/j.tre.2019.05.011
  • 10. Clausen, U., Brueggenolte, M., Kirberg, M., Besenfelder, C., Poeting, M., and Gueller, M. 01 . “Agent-based simulation in logistics and supply chain research: Literature review and analysis.” Lecture Notes in Logistics, 45–59. https://doi.org/10.1007/978-3-030-13535-5_4/COVER
  • 11. Hoornweg, D., and Bhada-Tata, P. 01 . “What a Waste: A global review of solid waste management.” https://openknowledge.worldbank.org/handle/10986/17388
  • 12. Krajka, M., Cyplik, P., and Adamczak, M. 2022. “The impact of blockchain technology on operational and strategic risks in the supply chain - A Systematic Literature Review.” Logforum 1 ( ), 365–77. https://doi.org/10.17270/J.LOG.2022.741
  • 13. Law, A. M. 01 . “Simulation Modeling and Analysis, (5th ed.). McGraw-Hill. www.averill-law.com
  • 14. Liang, Z., Huang, Y., Cao, Z., Liu, T., and Wang, Y. 01 . “Creativity in trusted data: Research on application of blockchain in supply chain.” nternational Journal of Performability Engineering 15 (2), 526–535. https://doi.org/10.23940/ijpe.19.02.p17.526535
  • 15. Lima, A. D. P., de Mascarenhas, F. W., and Frazzon, E. M. 01 . “Simulation-based planning and control of transport flows in port logistic systems.” Mathematical Problems in Engineering 2015. https://doi.org/10.1155/2015/862635
  • 16. Liu, Z., and Li, Z. 0 0. “A blockchain-based framework of cross-border e-commerce supply chain.” nternational Journal of Information Management 52. https://doi.org/10.1016/j.ijinfomgt.2019.102059
  • 17. Macal, C. M. 01 . “ verything you need to know about agent-based modelling and simulation.” Journal of Simulation 10 ( ), 144–156. https://doi.org/10.1057/jos.2016.7
  • 18. Macal, C. M., and North, M. J. 010. “Tutorial on agent-based modelling and simulation.” Journal of Simulation 4 (3), 151–162. https://doi.org/10.1057/jos.2010.3
  • 19. Min, H. 01 . “Blockchain technology for enhancing supply chain resilience.” Business Horizons 62 (1), 35–45. https://doi.org/10.1016/j.bushor.2018.08.012
  • 20. Philipp, R., Prause, G., and Gerlitz, L. 2019. “Blockchain and smart contracts for entrepreneurial collaboration in maritime supply chains.” Transport and Telecommunication 20 (4), 365–378. https://doi.org/10.2478/ttj-2019-0030
  • 21. Pournader, M., Shi, Y., Seuring, S., and Koh, S. C. L. 0 0. “Blockchain applications in supply chains, transport and logistics: A systematic review of the literature.” International Journal of Production Research 58 (7), 2063–2081. https://doi.org/10.1080/00207543.2019.1650976
  • 22. Queiroz, M. M., Telles, R., and Bonilla, S. H. 0 0. “Blockchain and supply chain management integration: A systematic review of the literature.” Supply Chain Management 25 (2), 241–54. https://doi.org/10.1108/SCM-03-2018-0143/FULL/HTML
  • 23. Saberi, S., Kouhizadeh, M., Sarkis, J., and Shen, L. 01 . “Blockchain technology and its relationships to sustainable supply chain management.” nternational Journal of Production Research 57 (7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
  • 24. Sheel, A., and Nath, V. 01 . “ ffect of blockchain technology adoption on supply chain adaptability, agility, alignment and performance.” Management Research Review 42 (12), 1353–1374. https://doi.org/10.1108/MRR-12-2018-0490
  • 25. Tisue, S., and Wilensky, U. 014. “NetLogo: A simple environment for modeling complexity.”
  • 26. Wilensky, . 1 . “NetLogo.” vanston, Illinois: Center for Connected Learning and Computer-Based Modeling, Northwestern University.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2cc7bc99-37c9-4e6a-8ccb-0f64b4c72f11
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