In this paper, a method for scalarizing optimization problems whose final space is endowed with a binary relation is stated without assuming any additional hypothesis on the data of the problem. By this approach, nondominated and minimal solutions are characterized in terms of solutions of scalar optimization problems whose objective functions are the post-composition of the original objective with scalar functions satisfying suitable properties. The obtained results generalize some recent ones stated in quasi ordered sets and real topological linear spaces. Besides, they are applied both to characterize by scalarization approximate solutions of set optimization problems with set ordering and to generalize some recent conditions on robust solutions of optimization problems. For this aim, a new robustness concept in optimization under uncertainty is introduced which is interesting in itself.

在本文中，提出了一种用于量化优化问题的方法，该方法的最终空间具有二元关系，而无需在问题数据上假设任何其他假设。通过这种方法，非标定解和极小解的特征在于标量优化问题的解，其目标函数是原始目标的后组合，标量函数满足合适的性质。获得的结果概括了一些最近的结果，这些结果以准有序集和实际拓扑线性空间表示。此外，它们既可用于以集合排序来量化集优化问题的近似解的特征，又可归纳为优化问题的鲁棒解的一些最新条件。为了这个目的，