Reverse supply chains (RSCs) have been increasingly implemented in recent years to manage the growing flow of solid waste generated by end-of-life (EOL) products and to minimise their environmental impact. Furthermore, recent research has shown that the implementation of RSCs benefits job creation, enables savings in raw materials, and creates income from the sales of re-manufactured products. However, designing RSCs requires dealing with many sources of uncertaintydue to the reverse flow of EOL products. To model these uncertainties, we consider a set of equally possible scenarios. In previous models for scenario sets, the decision often has been influenced by negative scenarios while neglecting opportunities. We propose a new risk/opportunity approach based on the $R_{\ast }$ criterion to give more weight to positive scenarios in the decision-making process. This criterion is used in order to distinguish zones of risk and opportunity and guide the decision-making process accordingly to the existing zones. We develop a lexicographic approach for the consideration of existing scenarios, and propose two methods to compute the optimal solution for lexicographic $R_{\ast }$ ($Lexi{R}_{\ast }$) criterion: one in the form of an algorithm and another in the form of a mixed-integer program (MIP). The performance of the developed approaches is demonstrated in a case study for a reverse facility location problem.

近年来，越来越多地采用反向供应链（RSC），以管理由报废（EOL）产品产生的固体废物不断增长的流量，并最大程度地减少其对环境的影响。此外，最近的研究表明，实施RSC有利于创造就业机会，可以节省原材料，并可以从再制造产品的销售中获得收入。然而，由于EOL产品的逆向流动，设计RSC需要处理许多不确定性源。为了对这些不确定性进行建模，我们考虑了一组同样可能的情况。在以前的场景集模型中，决策常常受到负面场景的影响，而忽略了机会。我们提出了一种基于风险的新的风险/机会方法${\mathrm{[R}}_{\ast }$在决策过程中更加重视积极情景的标准。使用此标准是为了区分风险和机会区域，并根据现有区域相应地指导决策过程。我们为考虑现有情况而开发了词典方法，并提出了两种方法来计算词典的最佳解决方案${\mathrm{[R}}_{\ast }$ （$\mathrm{大号}ËX\mathrm{一世}{\mathrm{[R}}_{\ast }$）标准：一种是算法形式，另一种是混合整数程序（MIP）。针对反向设施位置问题的案例研究证明了所开发方法的性能。