Semnan University International Journal of Nonlinear Analysis and Applications 2008-6822 15 9 2024 09 01 Designing a sustainable-resilient supply chain network with an emphasis on financing and investment decisions using the NSGA-II algorithm 93 112 8101 10.22075/ijnaa.2023.31030.4547 EN Peyman Taebi Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran Mahmoud Modiri Department of Industrial Management, Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran Visitor of Science and Research Branch, Islamic Azad University, Tehran, Iran Seyyed Mohammad Ali Khatami Firouzabadi Department of Industrial Management, Faculty of Management, University of Allameh Tabatabai, Tehran, Iran Ali Mohtashami Department of Industrial Management, Faculty of Management, Accounting and Humanities, Qazvin Branch, Islamic Azad University, Qazvin, Iran Journal Article 2023 02 22 Nowadays, product recovery and waste recycling are receiving more attention in order to reduce environmental pollution and production costs in the form of a closed-loop and sustainable supply chain. Also, designing a supply chain by considering resilience approaches can protect buyers against disruptions such as natural, human or technological disasters. On the other hand, efficient and effective financial supply chain management (FSCM) way is known as one of the main structures in line with the continuity and stability of the chain's performance, and budget restrictions are of great importance considering the issue of scarcity of resources in the economy. This study has presented a multi-objective mixed integer linear programming (MOMILP) model of a single-period, multi-product and multi-level closed-loop supply chain, taking into account the dimensions of sustainability and resilience, emphasizing the balance between the initial budget and the cost of establishing facilities under uncertainty. subsequently, to eliminate the uncertainty of the demand parameters and costs, its robust counterpart was presented based on Pishvaee’s robust possibilistic programming (RPP) model. The augmented Epsilon Constraint method (AEC) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were used to solve and evaluate it. https://ijnaa.semnan.ac.ir/article_8101_7fa408e74068ee76779c81dc63c3c45c.pdf