Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling / edited by Kyle Robert Harrison, Saber Elsayed, Ivan Leonidovich Garanovich, Terence Weir, Sharon G. Boswell, Ruhul Amin Sarker
(Adaptation, Learning, and Optimization. ISSN:18674542 ; 26)
| データ種別 | 電子ブック |
|---|---|
| 版 | 1st ed. 2022. |
| 出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
| 出版年 | 2022 |
| 大きさ | VIII, 214 p. 52 illus., 24 illus. in color : online resource |
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| 一般注記 | Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction -- Evolutionary Approaches for Project Portfolio Optimization: An Overview -- An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization -- An Overall Characterization of the Project Portfolio Optimization Problem and an Approach Based on Evolutionary Algorithms to Address It -- A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options -- Analysis of New Approaches used in Portfolio Optimization: A Systematic Literature Review -- A Temporal Knapsack Approach to Defence Portfolio Selection -- A Decision Support System for Planning Portfolios of Supply Chain Improvement Projects in the Semiconductor Industry 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 HTTP:URL=https://doi.org/10.1007/978-3-030-88315-7 |
|---|---|
| 著者標目 | Harrison, Kyle Robert editor Elsayed, Saber editor Garanovich, Ivan Leonidovich editor Weir, Terence editor Boswell, Sharon G editor Sarker, Ruhul Amin editor SpringerLink (Online service) |
| 件 名 | LCSH:Computational intelligence LCSH:Artificial intelligence FREE:Computational Intelligence FREE:Artificial Intelligence |
| 分 類 | LCC:Q342 DC23:006.3 |
| 巻冊次 | ebook ; ISBN:9783030883157 
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| ISBN | 9783030883157 |
| URL | https://doi.org/10.1007/978-3-030-88315-7 |
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※2021年9月12日以降