摘要：This paper combines the theory of teams and data envelopment analysis（DEA） to design a mechanism to optimally allocate resources in public healthcare. A statutory authority and the public hospitals under its governance are interpreted as a team, the members of which seek to operate efficiently under the shared institutional constraint that public healthcare is a public good. The individual public hospital exploits DEA to maximize own-payoff, subject to the team-condition that the payoff of each other public hospital does not fall and thereby subtract from the external effects created by the public supply of healthcare. The resulting team-DEA solution, which is shown to be both an individuallyefficient and team-satisficing equilibrium and to be computable in terms of a convergent algorithm, can then be applied by the authority to determine the optimal allocation of resources in public healthcare.A case based on Chinese data is presented to illustrate the team-DEA model＇s ready operationalization and computation.

摘要：为了及时有效地回收城市道路网络中的故障共享单车,对分散于路网边上的故障单车进行聚类形成收集点,考虑聚类收集点上回收需求呈现的不确定性特征,建立以行驶总距离最小为目标的回收周期性车辆路径选择模型.采用基约束鲁棒优化方法,利用有界区间对不确定的回收量进行描述,引入扰动系数和控制系数调节模型的鲁棒性和适应性.针对模型设计近似算法求解,证明近似算法的时间复杂性,分析算法近似比的上下界,用实例验证算法的近似比,结果表明算法性能较好.最后,通过分析回收量发生波动时,即扰动系数和控制系数对目标函数和算法近似比的影响,进一步验证了算法和模型的有效性.

摘要：In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.