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Resource Allocation (RA) involves the distribution and utilization
of available resources in the system. Because resource availability
is usually scarce and expensive, it becomes important to find
optimal solutions to such problems. Thus RA problems represent an
important class of problems faced by mathematical programmers. This
book focuses on development of models and heuristics for six new
and complex sub-classes of RA problems in Supply Chain (SC)
networks, focusing on bi-objectives, dynamic input data, and
multiple performance measures based allocation and integrated
allocation, and routing with complex constraints. It considers six
set of variants of the RA problems normally encountered in practice
but have not yet been studied. These variants of the classical RA
are complex and pertaining to both manufacturing and service
industry.
This book offers an elaborate and empirical look at service quality
of hospitals in the emerging market of India. The poor quality of
service is a major issue in a large number of hospitals
(particularly in government hospitals), which forces patients to
opt for private hospitals that are generally much more expensive
than government hospitals. This book provides a comprehensive
understanding of service quality antecedents in Indian hospitals.
It focuses on patient satisfaction and includes valuable insights
and implications for hospital management and government. The book
is theoretically grounded in SERVQUAL literature and uses
appropriate and sophisticated techniques and tools to analyse data.
It highlights causal model development with Structural Equation
Modelling (SEM) and introduces a classification model, developed
using Artificial Neural Networks (ANNs), in order to benchmark
specialty cardiac care. The book also deals with Support Vector
Machines (SVMs) and compares the error rates between SVM and ANN to
find the best classification technique among the two. Overall, this
book is a timely and relevant work that contributes to the theory,
practice and policy of service quality in hospitals.
Stock market manipulation is detrimental to traders and
corporations, causes unnecessary price fluctuations, and only
benefits financial criminals. The research presented here
determines an appropriate model to help identify stocks witnessing
activities that are indicative of potential manipulation through
three separate but related studies. In Developing an Effective
Model for Detecting Trade-Based Market Manipulation, classifiers
based on three different techniques namely discriminant analysis, a
composite classifier based on Artificial Neural Network and Genetic
Algorithm and support Vector Machines is proposed. The proposed
models help investigators, with varying degree of accuracy, to
arrive at a shortlist of securities which could be subject to
further detailed investigation to detect the type and nature of the
manipulation, if any. Following a fluid outline, Developing an
Effective Model for Detecting Trade-Based Market Manipulation,
introduces the topic, explores the aims and scopes of the research,
before delving into the data and modelling to explore their
application to the stock market to detect price manipulation.
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