The development of fast, robust and reliable computational tools capable of addressing process management under uncertain conditions is an active topic in the current literature, and more precisely for the process systems engineering one. Particularly, scenario reduction strategies have emerged as an alternative to overcome the traditional issues associated with large-scale scenario-based problems. This work proposes a novel and flexible scenario-reduction alternative that integrates data mining, graph theory and community detection concepts to represent the uncertain information as a network and identify the most efficient communities/clusters. The capabilities of the proposed approach were tested by solving a set of two-stage mixed-integer linear programming problems under uncertainty. For comparison and validation purposes, these problems were also solved using two available methods (SCENRED and OSCAR). This comparison demonstrates that the results obtained by using the proposed approach are at least as good or better, in terms of quality and accuracy, than the results obtained bu using SCENRED and OSCAR. Additionally, the practical advantage of the proposed parameter definition rule is demonstrated as a way to overcome the limitations of the current alternatives (i.e. arbitrary user-defined parameters).

能够解决不确定条件下的过程管理的快速，健壮和可靠的计算工具的开发是当前文献中的一个活跃主题，对于过程系统工程而言，更准确地说是一个活跃的主题。特别是，减少情景策略已成为克服与大规模基于情景的问题相关的传统问题的替代方法。这项工作提出了一种新颖而灵活的场景减少方案，该方案集成了数据挖掘，图论和社区检测概念，以网络形式表示不确定信息，并确定最有效的社区/集群。通过解决不确定性下的一组两阶段混合整数线性规划问题，测试了该方法的功能。为了进行比较和验证，SCENRED和OSCAR）。该比较表明，就质量和准确性而言，使用建议的方法获得的结果至少与使用SCENRED和OSCAR获得的结果相同或更好。此外，所提出的参数定义规则的实际优势被证明是克服当前替代方法（即任意用户定义的参数）限制的一种方式。