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Dynamic programming (DP) has a relevant history as a powerful and
flexible optimization principle, but has a bad reputation as a
computationally impractical tool. This book fills a gap between the
statement of DP principles and their actual software
implementation. Using MATLAB throughout, this tutorial gently gets
the reader acquainted with DP and its potential applications,
offering the possibility of actual experimentation and hands-on
experience. The book assumes basic familiarity with probability and
optimization, and is suitable to both practitioners and graduate
students in engineering, applied mathematics, management, finance
and economics.
The scope of this volume is primarily to analyze from different
methodological perspectives similar valuation and optimization
problems arising in financial applications, aimed at facilitating a
theoretical and computational integration between methods largely
regarded as alternatives. Increasingly in recent years, financial
management problems such as strategic asset allocation,
asset-liability management, as well as asset pricing problems, have
been presented in the literature adopting formulation and solution
approaches rooted in stochastic programming, robust optimization,
stochastic dynamic programming (including approximate SDP) methods,
as well as policy rule optimization, heuristic approaches and
others. The aim of the volume is to facilitate the comprehension of
the modeling and methodological potentials of those methods, thus
their common assumptions and peculiarities, relying on similar
financial problems. The volume will address different valuation
problems common in finance related to: asset pricing, optimal
portfolio management, risk measurement, risk control and
asset-liability management.The volume features chapters of
theoretical and practical relevance clarifying recent advances in
the associated applied field from different standpoints, relying on
similar valuation problems and, as mentioned, facilitating a mutual
and beneficial methodological and theoretical knowledge transfer.
The distinctive aspects of the volume can be summarized as follows:
Strong benchmarking philosophy, with contributors explicitly asked
to underline current limits and desirable developments in their
areas. Theoretical contributions, aimed at advancing the
state-of-the-art in the given domain with a clear potential for
applications The inclusion of an algorithmic-computational
discussion of issues arising on similar valuation problems across
different methods. Variety of applications: rarely is it possible
within a single volume to consider and analyze different, and
possibly competing, alternative optimization techniques applied to
well-identified financial valuation problems. Clear definition of
the current state-of-the-art in each methodological and applied
area to facilitate future research directions.
The scope of this volume is primarily to analyze from different
methodological perspectives similar valuation and optimization
problems arising in financial applications, aimed at facilitating a
theoretical and computational integration between methods largely
regarded as alternatives. Increasingly in recent years, financial
management problems such as strategic asset allocation,
asset-liability management, as well as asset pricing problems, have
been presented in the literature adopting formulation and solution
approaches rooted in stochastic programming, robust optimization,
stochastic dynamic programming (including approximate SDP) methods,
as well as policy rule optimization, heuristic approaches and
others. The aim of the volume is to facilitate the comprehension of
the modeling and methodological potentials of those methods, thus
their common assumptions and peculiarities, relying on similar
financial problems. The volume will address different valuation
problems common in finance related to: asset pricing, optimal
portfolio management, risk measurement, risk control and
asset-liability management.The volume features chapters of
theoretical and practical relevance clarifying recent advances in
the associated applied field from different standpoints, relying on
similar valuation problems and, as mentioned, facilitating a mutual
and beneficial methodological and theoretical knowledge transfer.
The distinctive aspects of the volume can be summarized as follows:
Strong benchmarking philosophy, with contributors explicitly asked
to underline current limits and desirable developments in their
areas. Theoretical contributions, aimed at advancing the
state-of-the-art in the given domain with a clear potential for
applications The inclusion of an algorithmic-computational
discussion of issues arising on similar valuation problems across
different methods. Variety of applications: rarely is it possible
within a single volume to consider and analyze different, and
possibly competing, alternative optimization techniques applied to
well-identified financial valuation problems. Clear definition of
the current state-of-the-art in each methodological and applied
area to facilitate future research directions.
Advanced modeling techniques are a necessary tool in order to
design and manage manufacturing systems effectively. This book
contains a set of tutorial chapters on topics ranging from
aggregate production planning to real time control, including
predictive and reactive scheduling, flow management in assembly
systems, simulation of robotic cells, design of manufacturing
systems under uncertainty and a historical perspective on
production management philosophies. The book will be of interest
both to researchers and practitioners, including graduate students
in Manufacturing Engineering and Operations Research.
Advanced modeling techniques are a necessary tool in order to design and manage manufacturing systems effectively. This book contains a set of tutorial chapters on topics ranging from aggregate production planning to real time control, including predictive and reactive scheduling, flow management in assembly systems, simulation of robotic cells, design of manufacturing systems under uncertainty and a historical perspective on production management philosophies. The book will be of interest both to researchers and practitioners, including graduate students in Manufacturing Engineering and Operations Research.
Dynamic programming (DP) has a relevant history as a powerful and
flexible optimization principle, but has a bad reputation as a
computationally impractical tool. This book fills a gap between the
statement of DP principles and their actual software
implementation. Using MATLAB throughout, this tutorial gently gets
the reader acquainted with DP and its potential applications,
offering the possibility of actual experimentation and hands-on
experience. The book assumes basic familiarity with probability and
optimization, and is suitable to both practitioners and graduate
students in engineering, applied mathematics, management, finance
and economics.
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