DEA model with shared resources and efficiency decomposition

数据包络分析 分解 计算机科学 集合(抽象数据类型) 阶段(地层学) 过程(计算) 家务 生产(经济) 运筹学 数学优化 数学 化学 经济 生态学 古生物学 生物化学 宏观经济学 基因 生物 程序设计语言 操作系统
作者
Hao Chen,Juan Du,Herbert Sherman,Joe Zhu
出处
期刊:European Journal of Operational Research [Elsevier BV]
卷期号:207 (1): 339-349 被引量:188
标识
DOI:10.1016/j.ejor.2010.03.031
摘要

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both stages of operations. For example, in hospital operations, some of the input resources such as equipment, personnel, and information technology are used in the first stage to generate medical record to track treatments, tests, drug dosages, and costs. The same set of resources used by first stage activities are used to generate the second-stage patient services. Patient services also use the services generated by the first stage operations of housekeeping, medical records, and laundry. These DMUs have not only inputs and outputs, but also intermediate measures that exist in-between the two-stage operations. The distinguishing characteristic is that some of the inputs to the first stage are shared by both the first and second stage, but some of the shared inputs cannot be conveniently split up and allocated to the operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and can understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. The current paper develops a set of DEA models for measuring the performance of two-stage network processes with non splittable shared inputs. An additive efficiency decomposition for the two-stage network process is presented. The models are developed under the assumption of variable returns to scale (VRS), but can be readily applied under the assumption of constant returns to scale (CRS). An application is provided.
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