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Although a useful and important tool, the potential of mathematical
modelling for decision making is often neglected. Considered an art
by many and weird science by some, modelling is not as widely
appreciated in problem solving and decision making as perhaps it
should be. And although many operations research, management
science, and optimization books touch on modelling techniques, the
short shrift they usually get in coverage is reflected in their
minimal application to problems in the real world. Illustrating the
important influence of modelling on the decision making process,
Optimization Modelling: A Practical Approach helps you come to
grips with a wide range of modelling techniques. Highlighting the
modelling aspects of optimization problems, the authors present the
techniques in a clear and straightforward manner, illustrated by
examples. They provide and analyze the formulation and modelling of
a number of well-known theoretical and practical problems and touch
on solution approaches. The book demonstrates the use of
optimization packages through the solution of various mathematical
models and provides an interpretation of some of those solutions.
It presents the practical aspects and difficulties of problem
solving and solution implementation and studies a number of
practical problems. The book also discusses the use of available
software packages in solving optimization models without going into
difficult mathematical details and complex solution methodologies.
The emphasis on modelling techniques rather than solution
algorithms sets this book apart. It is a single source for a wide
range of methods, classic theoretical and practical problems, data
collection and input preparation, the use of different optimization
software, and practical issues of modelling, model solving, and
implementation. The authors draw directly from their experience to
provide lessons learned when applying modelling techniques to
practical problem solving and implementation difficulties.
In this book, a risk management approach starts off by discussing
important issues related to managing supply chain disruption risks
from various perspectives during VUCA times. It explores the
essence and principles relating to managing these risks and
provides the framework and multi-goal model groups for managing
such unknown-unknown risks and subsequent disruptions at a global
scale. The book explores and presents the latest developments
across different emerging topics in supply chain risk and
disruption management. These include (i) an overview of supply
chain risk, and disruption management tools, techniques, and
approaches, (ii) a review on uncertainty modeling for decentralized
supply chain systems, (iii) supply chain deep uncertainties and
risks - the 'new normal', (iv) emergent technologies for supply
chain risk and disruption management, (v) supply chain resilience
strategies for times of unprecedented uncertainty, (vi) the role of
blockchain in developing supply chain resilience against
disruptions, (vii) a qualitative study on supply chain risk
management adopting blockchain technology, (viii) assessment of
risks and risk management for agriculture supply chain, (ix)
resilience of agri-food supply chains: Australian developments
after a decade of supply and demand shocks, (x) prioritization of
risks in the pharmaceutical supply chains (xi) improving medical
supply chain disruption management with the blockchain technology,
and (xii) impacts of resilience practices on supply chain
sustainability. The book contributes significantly to the growing
body of knowledge concerning the theory and practice of managing
supply chain risks and disruptions in strategic management,
operations and supply chain, and sustainability literature. It
presents contemporary, innovative and latest developments in
applying smart management tools, techniques and approaches for
managing supply chain risk and disruption and future-proofing
supply chains to become agile, resilient and sustainable.
This book consists of eight chapters, authored by distinguished
researchers and practitioners, that highlight the state of the art
and recent trends in addressing the project portfolio selection and
scheduling problem (PPSSP) across a variety of domains,
particularly defense, social programs, supply chains, and finance.
Many organizations face the challenge of selecting and scheduling a
subset of available projects subject to various resource and
operational constraints. In the simplest scenario, the primary
objective for an organization is to maximize the value added
through funding and implementing a portfolio of projects, subject
to the available budget. However, there are other major
difficulties that are often associated with this problem such as
qualitative project benefits, multiple conflicting objectives,
complex project interdependencies, workforce and manufacturing
constraints, and deep uncertainty regarding project costs,
benefits, and completion times. It is well known that the PPSSP is
an NP-hard problem and, thus, there is no known polynomial-time
algorithm for this problem. Despite the complexity associated with
solving the PPSSP, many traditional approaches to this problem make
use of exact solvers. While exact solvers provide definitive
optimal solutions, they quickly become prohibitively expensive in
terms of computation time when the problem size is increased. In
contrast, evolutionary and memetic computing afford the capability
for autonomous heuristic approaches and expert knowledge to be
combined and thereby provide an efficient means for high-quality
approximation solutions to be attained. As such, these approaches
can provide near real-time decision support information for
portfolio design that can be used to augment and improve existing
human-centric strategic decision-making processes. This edited book
provides the reader with a broad overview of the PPSSP, its
associated challenges, and approaches to addressing the problem
using evolutionary and memetic computing.
This book consists of eight chapters, authored by distinguished
researchers and practitioners, that highlight the state of the art
and recent trends in addressing the project portfolio selection and
scheduling problem (PPSSP) across a variety of domains,
particularly defense, social programs, supply chains, and finance.
Many organizations face the challenge of selecting and scheduling a
subset of available projects subject to various resource and
operational constraints. In the simplest scenario, the primary
objective for an organization is to maximize the value added
through funding and implementing a portfolio of projects, subject
to the available budget. However, there are other major
difficulties that are often associated with this problem such as
qualitative project benefits, multiple conflicting objectives,
complex project interdependencies, workforce and manufacturing
constraints, and deep uncertainty regarding project costs,
benefits, and completion times. It is well known that the PPSSP is
an NP-hard problem and, thus, there is no known polynomial-time
algorithm for this problem. Despite the complexity associated with
solving the PPSSP, many traditional approaches to this problem make
use of exact solvers. While exact solvers provide definitive
optimal solutions, they quickly become prohibitively expensive in
terms of computation time when the problem size is increased. In
contrast, evolutionary and memetic computing afford the capability
for autonomous heuristic approaches and expert knowledge to be
combined and thereby provide an efficient means for high-quality
approximation solutions to be attained. As such, these approaches
can provide near real-time decision support information for
portfolio design that can be used to augment and improve existing
human-centric strategic decision-making processes. This edited book
provides the reader with a broad overview of the PPSSP, its
associated challenges, and approaches to addressing the problem
using evolutionary and memetic computing.
Healthcare costs around the globe are on the rise, creating a
strong need for new ways of assisting the requirements of the
healthcare system. Besides applications in other areas, neural
networks have naturally found many promising applications in the
health and medicine areas. ""Neural Networks in Healthcare:
Potential and Challenges"" presents interesting and innovative
developments from leading experts and scientists working in health,
biomedicine, biomedical engineering, and computing areas. This book
covers many important and state-of-the-art applications in the
areas of medicine and healthcare, including: cardiology,
electromyography, electroencephalography, gait and human movement,
therapeutic drug monitoring for patient care, sleep apnea, and
computational fluid dynamics areas. ""Neural Networks in
Healthcare: Potential and Challenges"" is a useful source of
information for researchers, professionals, lecturers, and students
from a wide range of disciplines. Readers of this book will be able
to use the ideas for further research efforts in this very
important and highly multidisciplinary area.
With the large amount of data stored by many organizations,
capitalists have observed that this information is an intangible
asset. Unfortunately, handling large databases is a very complex
process and traditional learning techniques are expensive to use.
Heuristic techniques provide much help in this arena, although
little is known about heuristic techniques. Heuristic and
Optimization for Knowledge Discovery addresses the foundation of
this topic, as well as its practical uses, and aims to fill in the
gap that exists in current literature.
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