Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

Longitudinal Targets in Data Envelopment Analysis

A typical use and interpretation of data envelopment analysis (DEA) targets is to identify performance goals that if achieved will project an inefficient decision making unit onto the efficiency frontier. This will occur, however, only if all other units maintain their same input and output levels, which is rarely the case over time. This paper investigates alternate approaches for interpreting...

A Non-radial Approach for Setting Integer-valued Targets in Data Envelopment Analysis

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception with Charnes, Cooper and Rhodes work in 1978. The methodology behind the classical DEA method is to determine how much improvements in the outputs (inputs) dimensions is necessary in order to render them efficient. One of the underlying assumptions of this methodology is that the units consume and prod...

Efficient location by using data envelopment analysis

So far, many types of location models have been developed to find optimal spatial patterns according to different spatial metrics such as cost, coverage and availability.  The initial focus of these models is on the location availability of service providers and demand estimates, and some of these models are within the framework of multi-objective programming models.  After the advent of scienc...

Failure Mode and Effects Analysis Using Data Envelopment Analysis

Conflict Patterns: Toward Identifying Suitable Middleware

Architectural patterns aid developers in resolving coarse-grained integration problems among components. These patterns are assembled from functionality slices that resolve various communication problems between applications. However, little attention has been paid to how interoperability problems and their resolution are embodied in these patterns. Mapping these problems to specific functional...