TY - JOUR
T1 - Designing reverse logistics network for end-of-life vehicles
T2 - A sustainability perspective in a fragile supply chain
AU - Ayvaz, Berk
AU - Kusakci, Ali Osman
AU - Aydin, Nezir
AU - Ertas, Emine
N1 - Publisher Copyright:
© 2021 University of Cincinnati. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Environmental guidelines in the automotive industry greatly emphasize the recycling, remanufacturing, and recovering of end-of-life vehicles (ELVs). Given the principle of extended producer responsibility, developing an effective reverse logistics network is the most significant digit ahead of the industry. However, initial attempts addressing the reverse logistics network design (RLND) problem were short-sighted, focusing on cost minimization. Undoubtedly, the whole concept of recycling was founded on the pillars of sustainability. Accordingly, reverse logistics network design must be motivated by long-term environmental and societal benefits. This fact has become even more prominent in the current pandemic environment as COVID-19 has added serious uncertainties and risks to the supply chain processes. This paper reiterates the essence of sustainability goals and proposes a multi-objective fuzzy mathematical model to RLND problem for ELVs under such a fragile and fuzzy environment. The coverage of the proposed model is to optimally determine the locations and numbers of the facilities and the flows among them concerning environmental, social, and economic aspects. Hence, the model aims to reach a robust compromise solution that leads to a resilient network design. A real case study on the ELV market in Istanbul/Turkey proves the merit of the developed model.
AB - Environmental guidelines in the automotive industry greatly emphasize the recycling, remanufacturing, and recovering of end-of-life vehicles (ELVs). Given the principle of extended producer responsibility, developing an effective reverse logistics network is the most significant digit ahead of the industry. However, initial attempts addressing the reverse logistics network design (RLND) problem were short-sighted, focusing on cost minimization. Undoubtedly, the whole concept of recycling was founded on the pillars of sustainability. Accordingly, reverse logistics network design must be motivated by long-term environmental and societal benefits. This fact has become even more prominent in the current pandemic environment as COVID-19 has added serious uncertainties and risks to the supply chain processes. This paper reiterates the essence of sustainability goals and proposes a multi-objective fuzzy mathematical model to RLND problem for ELVs under such a fragile and fuzzy environment. The coverage of the proposed model is to optimally determine the locations and numbers of the facilities and the flows among them concerning environmental, social, and economic aspects. Hence, the model aims to reach a robust compromise solution that leads to a resilient network design. A real case study on the ELV market in Istanbul/Turkey proves the merit of the developed model.
KW - End-of-Life Vehicles (ELV)
KW - Multi-Objective Fuzzy Linear Programming
KW - Resilient Network Design
KW - Reverse Logistics
UR - http://www.scopus.com/inward/record.url?scp=85120609911&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85120609911
SN - 1072-4761
VL - 28
SP - 298
EP - 328
JO - International Journal of Industrial Engineering : Theory Applications and Practice
JF - International Journal of Industrial Engineering : Theory Applications and Practice
IS - 3
ER -