Abstract: Traditional ANP-BOCR method （i.e., sub-networks of Analytic Network Process （ANP） are constructed from the viewpoints of Benefits, Opportunities, Costs and Risks （BOCR） merits, respectively. Then, the composite priorities of alternatives under BOCR are finally synthesized）, is regarded as an effective approach to deal with the issues of complex systems. However, first, the complex relation between these elements lying different BOCR sub-networks is ignored in traditional ANP-BOCR method. Furthermore, when the priorities of alternatives are synthesized in ANP-BOCR, simple reductionism thinking cannot be avoided. Second, when group decisions are used, the method would lose some expert preference information, and all information of alternatives is asked to give. To overcome the aforementioned drawbacks, a new group decision method of ANP-BOCR is presented by using Data Envelopment Analysis （DEA） to vote ranking, based on a new ANP-BOCR analysis structure. In the new method, reductionism and holism thinking is well integrated. Also, information lossless is realized. Finally, the new method is validated to be feasible and can be widely applied in the real-world.

Key words: analytic network process, Benefits, Opportunities, Costs and Risks（BOCR）, group decision, voting ranking

摘要： 传统ANP-BOCR方法（即从收益（B）、机会（O）、成本（C）和风险（R）视角分别构造ANP（网络分析法）子网络，再将BOCR子网络下的方案评价值进行综合集成）被认为是一种处理复杂系统问题的有效方法。然而，一方面，该方法不仅尚未考虑隶属于不同BOCR子网络中元素之间的关联关系，而且在对BOCR方案评价值进行集成时也会陷入简单还原论的思维误区。另一方面，该方法集成群组专家意见时通常会损失部分专家的偏好判断信息，并硬性要求所有专家提供各方案的全偏好判断信息。为克服上述缺陷，通过构建复杂问题ANP-BOCR的新分析结构，提出基于DEA（数据包络分析）投票排序的ANP-BOCR群组决策新方法。新方法不仅实现了方法论和整体论的有机融合，而且还可保证群组专家信息集成过程中的信息无损。案例应用结果表明：新方法是行之有效的，有较强的实践应用推广价值。

关键词: 网络分析法, 收益, 机会, 成本, 风险, 群组决策, 投票排序