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Books > Computing & IT > Applications of computing > Artificial intelligence
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Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling (Hardcover, 1st ed. 2022)
Loot Price: R3,987
Discovery Miles 39 870
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Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling (Hardcover, 1st ed. 2022)
Series: Adaptation, Learning, and Optimization, 26
Expected to ship within 10 - 15 working days
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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.
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