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Books > Business & Economics > Business & management > Management of specific areas > Production & quality control management
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Sequential Decision Analytics and Modeling: Modeling with Python (Hardcover)
Loot Price: R2,155
Discovery Miles 21 550
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Sequential Decision Analytics and Modeling: Modeling with Python (Hardcover)
Series: Foundations and Trends (R) in Technology, Information and Operations Management
Expected to ship within 18 - 22 working days
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Sequential decision problems arise in virtually every human
process. They span finance, energy, transportation, health,
e-commerce, and supply chains and include pure learning problems
that arise in laboratory or field experiments. They even cover
search algorithms to maximize uncertain functions. An important
dimension of every problem setting is the need to make decisions in
the presence of different forms of uncertainty and evolving
information processes. Warren B. Powell's work in sequential
decision problems started in the 1980s and spanned rail, energy,
health, finance, e-commerce, supply chain management, and even
learning for materials science. His work on a wide range of
problems highlighted the importance of using a variety of methods.
In the process, he came to realize that any sequential decision
problem can be modeled using a single universal framework that
involves searching over methods for making decisions. The goal of
this book is to enable readers to understand how to approach, model
and solve a sequential decision problem. To that end, it uses a
teach-by-example style to illustrate a modeling framework that can
represent any sequential decision problem. It tackles the challenge
of designing methods, called policies, for making decisions and
describes four classes of policies that are universal in that they
span any method that might be used; whether from the academic
literature or heuristics used in practice. While this does not mean
that every problem can be solved immediately, the framework helps
avoid the tendency in the academic literature of focusing on narrow
classes of methods.
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