This paper aims to develop a scenario-based optimization framework to deals with several issues related to redesign sustainable reverse supply chain; focused particularly in the epistemic uncertainty of supply and demand input parameters and their relationship with supply chain performance, both important for the redesign problem. The two-step optimization framework starts with a Fuzzy Inference System methodology for scenario generation that faces the lack of information, and the necessity of estimate the expected operation cost and environmental impact. Then we use generated scenarios into the epsilon-constraint method which solves a multi-objective model to obtain a relevant set of solutions. After solving the second optimization model, we propose to analyze the robustness of the achieved redesign solutions considering a customer satisfaction approach. The computational experiments show that our proposed framework supports better the inclusion of scenarios for redesigning the plastic recycling supply chain. Furthermore, we study a real-life plastic recycling problem in Cuba which demonstrates that the framework is able to support the redesign decision making with robust solutions sensitive to the changes of the studied uncertain parameters.

本文旨在开发一种基于场景的优化框架，以处理与重新设计可持续逆向供应链有关的若干问题。特别关注供应和需求输入参数的认知不确定性及其与供应链绩效的关系，这对于重新设计问题都很重要。两步优化框架从面向场景生成的模糊推理系统方法入手，该方法面临信息匮乏以及估计预期运营成本和环境影响的必要性。然后，我们将生成的场景用于epsilon-constraint方法中，该方法解决了多目标模型以获得一组相关的解决方案。解决第二个优化模型后，我们建议考虑客户满意度方法来分析所获得的重新设计解决方案的稳定性。计算实验表明，我们提出的框架可以更好地支持重新设计塑料回收供应链的方案。此外，我们研究了古巴的一个现实生活中的塑料回收问题，这表明该框架能够通过对研究的不确定参数变化敏感的强大解决方案来支持重新设计决策。