With new global regulations on supply chains (SCs), sustainable regulation mechanisms have become subject to controversy. The intention is to create and expand green and sustainable supply chains (SSC) to meet environmental and economic standards and to boost one’s position in competitive markets. This study examines the resilient sustainable reverse logistics network (RLN) process for end-of-life vehicles (ELVs) in Iran. We pursue both actual and uncertain situations that possess big data characteristics (3 V’s) in information between facilities of the proposed reverse logistics (RL), and we consider recycling technology due to its societal impacts. Due to unpredictable environmental and social factors, the various proposed network facilities may not utilize their full capacity, so we also consider situations in which the network facility capacity is disrupted. Our primary objective is to minimize the total cost of the resilient sustainable RLN. For most parameters, finding the best solution through traditional methods is time-consuming and costly. Hence, to enhance decision-making power, the value of model parameters in each scenario is considered. A Cross-Entropy (CE) algorithm with basic scenario concepts is used in robust model optimization. The results demonstrate that changing the scenario situation significantly impacts optimal environmental and social costs. In particular, when the situation is “pessimistic,” environmental impact costs are at their highest levels. Hence, scenario-based modeling of the network is a good approach to implement under uncertainty conditions. On the other hand, results show that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition.

随着新的全球供应链法规（SC）的出现，可持续的监管机制已引起争议。目的是创建和扩展绿色和可持续供应链（SSC），以满足环境和经济标准，并提高自己在竞争市场中的地位。这项研究研究了伊朗报废车辆（ELV）的弹性可持续逆向物流网络（RLN）流程。我们将在拟议的逆向物流（RL）设施之间的信息中寻求具有大数据特征（3 V）的实际和不确定情况，并且由于其社会影响，我们考虑了回收技术。由于不可预测的环境和社会因素，建议的各种网络设施可能无法充分利用其全部容量，因此，我们还考虑了网络设施容量受到干扰的情况。我们的主要目标是最大程度地降低可恢复的可持续性RLN的总成本。对于大多数参数，通过传统方法找到最佳解决方案既费时又费钱。因此，为了增强决策能力，需要考虑每种情况下模型参数的值。具有基本方案概念的交叉熵（CE）算法用于鲁棒模型优化。结果表明，改变情景会极大地影响最佳的环境和社会成本。特别是当情况“悲观”时，环境影响成本处于最高水平。因此，基于场景的网络建模是在不确定条件下实施的良好方法。另一方面，