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001442733 020__ $$a9783030883157$$q(electronic bk.)
001442733 020__ $$a3030883159$$q(electronic bk.)
001442733 020__ $$z9783030883140$$q(print)
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001442733 0247_ $$a10.1007/978-3-030-88315-7$$2doi
001442733 035__ $$aSP(OCoLC)1285537689
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001442733 08204 $$a006.3/823$$223
001442733 24500 $$aEvolutionary and memetic computing for project portfolio selection and scheduling /$$cKyle Robert Harrison, Saber Elsayed, Ivan Leonidovich Garanovich, Terence Weir, Sharon G. Boswell, Ruhul Amin Sarker, editors.
001442733 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001442733 300__ $$a1 online resource (viii, 214 pages) :$$billustrations (some color)
001442733 336__ $$atext$$btxt$$2rdacontent
001442733 337__ $$acomputer$$bc$$2rdamedia
001442733 338__ $$aonline resource$$bcr$$2rdacarrier
001442733 347__ $$atext file
001442733 347__ $$bPDF
001442733 4901_ $$aAdaptation, learning, and optimization,$$x1867-4542 ;$$vvolume 26
001442733 500__ $$aIncludes index.
001442733 5050_ $$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.
001442733 506__ $$aAccess limited to authorized users.
001442733 520__ $$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.
001442733 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 18, 2021).
001442733 650_0 $$aEvolutionary computation.
001442733 650_0 $$aComputer scheduling.
001442733 650_6 $$aRéseaux neuronaux à structure évolutive.
001442733 650_6 $$aOrdonnancement (Informatique)
001442733 655_0 $$aElectronic books.
001442733 7001_ $$aHarrison, Kyle Robert,$$eeditor$$0(orcid)0000-0002-1443-3005$$1https://orcid.org/0000-0002-1443-3005
001442733 7001_ $$aElsayed, Saber,$$eeditor.
001442733 7001_ $$aGaranovich, Ivan Leonidovich,$$eeditor$$0(orcid)0000-0001-6676-6584$$1https://orcid.org/0000-0001-6676-6584
001442733 7001_ $$aWeir, Terence,$$eeditor.
001442733 7001_ $$aBoswell, Sharon G.,$$eeditor.
001442733 7001_ $$aSarker, Ruhul A.,$$eeditor.
001442733 77608 $$iPrint version:$$tEvolutionary and memetic computing for project portfolio selection and scheduling.$$dCham, Switzerland : Springer, [2022]$$z3030883140$$z9783030883140$$w(OCoLC)1266253223
001442733 830_0 $$aAdaptation, learning and optimization ;$$vv. 26.$$x1867-4542
001442733 852__ $$bebk
001442733 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-88315-7$$zOnline Access$$91397441.1
001442733 909CO $$ooai:library.usi.edu:1442733$$pGLOBAL_SET
001442733 980__ $$aBIB
001442733 980__ $$aEBOOK
001442733 982__ $$aEbook
001442733 983__ $$aOnline
001442733 994__ $$a92$$bISE