Overview
- Provides the reader with a broad overview of the project portfolio selection and scheduling problem
- Highlights the state of the art and recent trends in evolutionary and memetic computing
- Addresses the integrated problem of both selection and scheduling of projects using evolutionary computation
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 26)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling
Editors: Kyle Robert Harrison, Saber Elsayed, Ivan Leonidovich Garanovich, Terence Weir, Sharon G. Boswell, Ruhul Amin Sarker
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-030-88315-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-88314-0Published: 14 November 2021
Softcover ISBN: 978-3-030-88317-1Published: 14 November 2022
eBook ISBN: 978-3-030-88315-7Published: 13 November 2021
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: VIII, 214
Number of Illustrations: 28 b/w illustrations, 24 illustrations in colour