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020			_a9783030883157
024	7		_a10.1007/978-3-030-88315-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.618 _b2022 EB
245	1	0	_aEvolutionary and Memetic Computing for Project Portfolio Selection and Scheduling _cedited by Kyle Robert Harrison, Saber Elsayed, Ivan Leonidovich Garanovich, Terence Weir, Sharon G. Boswell, Ruhul Amin Sarker
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VIII, 214 páginas) _b52 ilustraciones, 24 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aAdaptation Learning and Optimization _x1867-4542 _v26
505	0		_aEvolutionary 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.
520			_aThis 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.
988			_aSpringer_Robotics_2022
650		7	_2embne _9667195 _aComputación evolutiva
650		7	_2embne _9139821 _aProgramación de ordenadores
776	0	8	_iPrinted edition: _z9783030883140
776	0	8	_iPrinted edition: _z9783030883164
776	0	8	_iPrinted edition: _z9783030883171
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-88315-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI