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This book introduces readers to the use of R codes for optimization
problems. First, it provides the necessary background to understand
data envelopment analysis (DEA), with a special emphasis on fuzzy
DEA. It then describes DEA models, including fuzzy DEA models, and
shows how to use them to solve optimization problems with R.
Further, it discusses the main advantages of R in optimization
problems, and provides R codes based on real-world data sets
throughout. Offering a comprehensive review of DEA and fuzzy DEA
models and the corresponding R codes, this practice-oriented
reference guide is intended for masters and Ph.D. students in
various disciplines, as well as practitioners and researchers.
This book introduces readers to the use of R codes for optimization
problems. First, it provides the necessary background to understand
data envelopment analysis (DEA), with a special emphasis on fuzzy
DEA. It then describes DEA models, including fuzzy DEA models, and
shows how to use them to solve optimization problems with R.
Further, it discusses the main advantages of R in optimization
problems, and provides R codes based on real-world data sets
throughout. Offering a comprehensive review of DEA and fuzzy DEA
models and the corresponding R codes, this practice-oriented
reference guide is intended for masters and Ph.D. students in
various disciplines, as well as practitioners and researchers.
Uncertainty in Data Envelopment Analysis: Fuzzy and Belief
Degree-Based Uncertainties introduces methods to investigate
uncertain data in DEA models, providing a deeper look into two
types of uncertain DEA methods: Fuzzy DEA and Belief Degree Based
Uncertainty DEA, which are based on uncertain measures. These
models aim to solve problems encountered by classical data analysis
in cases where inputs and outputs of systems and processes are
volatile and complex, making measurement difficult. Classical data
envelopment analysis (DEA) models use crisp data in order to
measure inputs and outputs of a given system. Crisp input and
output data are fundamentally indispensable in the conventional DEA
models. If these models contain complex-uncertain data, then they
will become more important and practical for decision-makers.
This book presents the theory and application of the models
presented in this regard and establishes a meaningful relationship
between data envelopment analysis and multi-attribute decision
making. The issue of "choice" using the aggregation of voters'
votes is one of the most important group decision-making issues
that are always considered by decision makers in electoral systems.
Voting is a method of group decision making in a democratic society
that expresses the will of the majority. Voting is perhaps the
simplest way to gather the opinions of experts, and this ease of
application has made it a multi-attribute decision-making method in
group decisions. Preferential voting is a type of voting that may
refer to electoral systems or groups of the electoral system. In
preferential voting, voters vote for multiple candidates, and how
the candidates are arranged on the ballot is important. Researchers
have made many efforts to provide models of voter aggregation, and
one of the best results of these efforts is the aggregation of
votes based on the policy of data envelopment analysis. Thus, in
group decisions, the opinions of experts are obtained in a simple
structure and consolidated in an interactive and logical structure,
and the results can be a powerful tool for decision support. This
book provides a complete set of voting models based on data
envelopment analysis and expressing its various applications in
industry and society. However, most decision-making methods do not
use the opinions of experts or reduce the motivation of experts to
participate in complex interactions and time, while voting methods
do not have this shortcoming. This book is suitable for graduate
students in the fields of industrial management, business
management, industrial engineering, applied mathematics, and
economics. It can also be a good source for researchers in decision
science, decision support systems, data envelopment analysis,
supply chain management, healthcare management, and others. The
methods presented in this book can not only offer a comprehensive
framework for solving the problems of these areas but also can
inspire researchers to pursue new innovative hybrid methods.
For any organization, analysis of performance and effectiveness
through available data allows for informed decision making. Data
envelopment analysis, or DEA, is a popular, effective method that
can be used to measure productive efficiency in operations
management assessment. Data Envelopment Analysis and Effective
Performance Assessment addresses the myriad of practical uses and
innovative developments of DEA. Emphasizing the importance of
analyzing productivity by measuring inputs, goals, economic growth,
and performance, this book covers a wide breadth of innovative
knowledge. This book is essential reading for managers, business
professionals, students of business and ICT, and computer
engineers.
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