【摘要】 数据包络分析(DEA)是一种评价具有多投入、多产出决策单元的相对效率的线性规划方法.在现实世界中,决策单元有时呈现出由多个独立子系统构成的复杂并联网络系统,各子系统的投入/产出之和构成了系统的总投入/产出.目前,用于评价这种具有并联网络生产系统相对效率的模型主要有三种:网络DEA模型、多部门DEA模型和关联DEA模型.现有这些模型的基本特性和相互关系存在着不足,即子系统的效率分解和优化指数不唯一.为解决这一问题,提出了改进的并联DEA模型,并采用加拿大银行系统实例来说明所提出模型的合理性和有效性.更多还原

【Abstract】 Data envelopment analysis(DEA) is a linear programming approach for evaluating relative efficiency of peer decision making units(DMUs) that consume multiple inputs to produce multiple outputs.DMUs can have parallel independent sub-units,where inputs and outputs of each DMU are the sum of those its sub-units.This paper investigates the properties and relationships of the existing parallel DEA models,i.e.,network,multi-component and relational DEA models,that address measuring the performance of parallel production systems.A major limitation of the existing DEA models is that efficiency optimization may produce multiple sets of efficiency scores for individual sub-units.The current paper proposes a new DEA approache through efficiency decomposition to deal with this problem.A case of Canadian bank branches is employed to illustrate these parallel DEA approach.更多还原