A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints

In response to the increasing complexity of modern products, dynamic markets, and intensified competition, project-based organizations are actively seeking methodologies to efficiently manage their expanding project portfolios. This paper analyzes the project portfolio selection problem in uncertain...

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Detalles Bibliográficos
Autores: Saiz, Miguel, Lopez-Lopez, David, Calvet Liñán, Laura, Juan, Angel A.
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.14342/5921
Acceso en línea:https://hdl.handle.net/20.500.14342/5921
https://doi.org/10.1111/itor.70064
Access Level:acceso abierto
Palabra clave:Genetic algorithms
Project-based organizations
Project portfolio selection
Simheuristics
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spelling A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraintsSaiz, MiguelLopez-Lopez, DavidCalvet Liñán, LauraJuan, Angel A.Genetic algorithmsProject-based organizationsProject portfolio selectionSimheuristicsIn response to the increasing complexity of modern products, dynamic markets, and intensified competition, project-based organizations are actively seeking methodologies to efficiently manage their expanding project portfolios. This paper analyzes the project portfolio selection problem in uncertain environments. Despite recent advances in the field, there is a pressing need for decision-making frameworks that blend optimization and simulation with realistic project information and portfolio constraints. Through an extensive literature review, we identify key variables critical for handling practical scenarios, such as project schedule interdependencies, duration estimations across various scenarios, baseline budget, risk registers, interproject correlations, and cost overrun correlation. To tackle the inherent stochasticity, we introduce a simheuristic algorithm that combines genetic optimization with Monte Carlo simulation. This strategy maximizes the expected value while adhering to project and portfolio constraints under a set portfolio budget reliability level. This approach provides decision-makers with a powerful tool for enhancing project selection processes, promoting upfront planning, improving risk management, and the achievement of strategic goals. The performance of this approach is validated against deterministic methodologies, such as employing a mixed-integer linear programming solver in stochastic environments, demonstrating its effectiveness and practical applicability.info:eu-repo/semantics/publishedVersionJohn Wiley & Sons Ltd.Universitat Ramon Llull. Esade2025info:eu-repo/semantics/article33 p.https://hdl.handle.net/20.500.14342/5921https://doi.org/10.1111/itor.70064reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésInternational Transactions in Operational Research© L'autor/aAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:20.500.14342/59212026-05-29T05:05:01Z
dc.title.none.fl_str_mv A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
title A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
spellingShingle A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
Saiz, Miguel
Genetic algorithms
Project-based organizations
Project portfolio selection
Simheuristics
title_short A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
title_full A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
title_fullStr A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
title_full_unstemmed A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
title_sort A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints
dc.creator.none.fl_str_mv Saiz, Miguel
Lopez-Lopez, David
Calvet Liñán, Laura
Juan, Angel A.
author Saiz, Miguel
author_facet Saiz, Miguel
Lopez-Lopez, David
Calvet Liñán, Laura
Juan, Angel A.
author_role author
author2 Lopez-Lopez, David
Calvet Liñán, Laura
Juan, Angel A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. Esade
dc.subject.none.fl_str_mv Genetic algorithms
Project-based organizations
Project portfolio selection
Simheuristics
topic Genetic algorithms
Project-based organizations
Project portfolio selection
Simheuristics
description In response to the increasing complexity of modern products, dynamic markets, and intensified competition, project-based organizations are actively seeking methodologies to efficiently manage their expanding project portfolios. This paper analyzes the project portfolio selection problem in uncertain environments. Despite recent advances in the field, there is a pressing need for decision-making frameworks that blend optimization and simulation with realistic project information and portfolio constraints. Through an extensive literature review, we identify key variables critical for handling practical scenarios, such as project schedule interdependencies, duration estimations across various scenarios, baseline budget, risk registers, interproject correlations, and cost overrun correlation. To tackle the inherent stochasticity, we introduce a simheuristic algorithm that combines genetic optimization with Monte Carlo simulation. This strategy maximizes the expected value while adhering to project and portfolio constraints under a set portfolio budget reliability level. This approach provides decision-makers with a powerful tool for enhancing project selection processes, promoting upfront planning, improving risk management, and the achievement of strategic goals. The performance of this approach is validated against deterministic methodologies, such as employing a mixed-integer linear programming solver in stochastic environments, demonstrating its effectiveness and practical applicability.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14342/5921
https://doi.org/10.1111/itor.70064
url https://hdl.handle.net/20.500.14342/5921
https://doi.org/10.1111/itor.70064
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Transactions in Operational Research
dc.rights.none.fl_str_mv © L'autor/a
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © L'autor/a
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 33 p.
dc.publisher.none.fl_str_mv John Wiley & Sons Ltd.
publisher.none.fl_str_mv John Wiley & Sons Ltd.
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.808905

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