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Evolutionary and memetic computing f...

Harrison, Kyle Robert.

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Evolutionary and memetic computing for project portfolio selection and scheduling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evolutionary and memetic computing for project portfolio selection and scheduling/ edited by Kyle Robert Harrison ... [et al.].
其他作者:	Harrison, Kyle Robert.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	viii, 214 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Evolutionary computation. -
電子資源:	https://doi.org/10.1007/978-3-030-88315-7
ISBN:	9783030883157

Evolutionary and memetic computing for project portfolio selection and scheduling
Evolutionary and memetic computing for project portfolio selection and scheduling[electronic resource] /edited by Kyle Robert Harrison ... [et al.]. - Cham :Springer International Publishing :2022. - viii, 214 p. :ill. (some col.), digital ;24 cm. - Adaptation, learning, and optimization,v. 261867-4542 ;. - Adaptation, learning, and optimization ;v. 26..

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.

ISBN: 9783030883157

Standard No.: 10.1007/978-3-030-88315-7doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: QA76.618 / .E86 2022

Dewey Class. No.: 006.3823

Evolutionary and memetic computing for project portfolio selection and scheduling
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520 $a 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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