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Data Envelopment Analysis - A Handbook of Modeling Internal Structure and Network (Hardcover, 2014 ed.)
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Data Envelopment Analysis - A Handbook of Modeling Internal Structure and Network (Hardcover, 2014 ed.)
Series: International Series in Operations Research & Management Science, 208
Expected to ship within 12 - 17 working days
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This handbook serves as a complement to the Handbook on Data
Envelopment Analysis (eds, W.W. Cooper, L.M. Seiford and J, Zhu,
2011, Springer) in an effort to extend the frontier of DEA
research. It provides a comprehensive source for the state-of-the
art DEA modeling on internal structures and network DEA. Chapter 1
provides a survey on two-stage network performance decomposition
and modeling techniques. Chapter 2 discusses the pitfalls in
network DEA modeling. Chapter 3 discusses efficiency decompositions
in network DEA under three types of structures, namely series,
parallel and dynamic. Chapter 4 studies the determination of the
network DEA frontier. In chapter 5 additive efficiency
decomposition in network DEA is discussed. An approach in scale
efficiency measurement in two-stage networks is presented in
chapter 6. Chapter 7 further discusses the scale efficiency
decomposition in two stage networks. Chapter 8 offers a bargaining
game approach to modeling two-stage networks. Chapter 9 studies
shared resources and efficiency decomposition in two-stage
networks. Chapter 10 introduces an approach to computing the
technical efficiency scores for a dynamic production network and
its sub-processes. Chapter 11 presents a slacks-based network DEA.
Chapter 12 discusses a DEA modeling technique for a two-stage
network process where the inputs of the second stage include both
the outputs from the first stage and additional inputs to the
second stage. Chapter 13 presents an efficiency measurement
methodology for multi-stage production systems. Chapter 14
discusses network DEA models, both static and dynamic. The
discussion also explores various useful objective functions that
can be applied to the models to find the optimal allocation of
resources for processes within the black box, that are normally
invisible to DEA. Chapter 15 provides a comprehensive review of
various type network DEA modeling techniques. Chapter 16 presents
shared resources models for deriving aggregate measures of
bank-branch performance, with accompanying component measures that
make up that aggregate value. Chapter 17 examines a set of
manufacturing plants operating under a single umbrella, with the
objective being to use the component or function measures to decide
what might be considered as each plant's core business. Chapter 18
considers problem settings where there may be clusters or groups of
DMUs that form a hierarchy. The specific case of a set off electric
power plants is examined in this context. Chapter 19 models bad
outputs in two-stage network DEA. Chapter 20 presents an
application of network DEA to performance measurement of Major
League Baseball (MLB) teams. Chapter 21 presents an application of
a two-stage network DEA model for examining the performance of 30
U.S. airline companies. Chapter 22 then presents two distinct
network efficiency models that are applied to engineering systems.
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