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Generating datasets for the project portfolio selection and scheduling problem

The article presents two variants of the project portfolio selection and scheduling problem (PPSSP). The primary objective of the PPSSP is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This article descr...

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Detalles Bibliográficos
Autores principales: Harrison, Kyle Robert, Elsayed, Saber M., Garanovich, Ivan L., Weir, Terence, Boswell, Sharon G., Sarker, Ruhul A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9079686/
https://www.ncbi.nlm.nih.gov/pubmed/35539021
http://dx.doi.org/10.1016/j.dib.2022.108208
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author Harrison, Kyle Robert
Elsayed, Saber M.
Garanovich, Ivan L.
Weir, Terence
Boswell, Sharon G.
Sarker, Ruhul A.
author_facet Harrison, Kyle Robert
Elsayed, Saber M.
Garanovich, Ivan L.
Weir, Terence
Boswell, Sharon G.
Sarker, Ruhul A.
author_sort Harrison, Kyle Robert
collection PubMed
description The article presents two variants of the project portfolio selection and scheduling problem (PPSSP). The primary objective of the PPSSP is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This article describes two recently-proposed, generalised models of the PPSSP [1], [2] and proposes a set of synthetically generated problem instances for each. These datasets can be used by researchers to compare the performance of heuristic and meta-heuristic solution strategies. In addition, the Python program used to generate the problem instances is supplied, allowing researchers to generate new problem instances.
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spelling pubmed-90796862022-05-09 Generating datasets for the project portfolio selection and scheduling problem Harrison, Kyle Robert Elsayed, Saber M. Garanovich, Ivan L. Weir, Terence Boswell, Sharon G. Sarker, Ruhul A. Data Brief Data Article The article presents two variants of the project portfolio selection and scheduling problem (PPSSP). The primary objective of the PPSSP is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This article describes two recently-proposed, generalised models of the PPSSP [1], [2] and proposes a set of synthetically generated problem instances for each. These datasets can be used by researchers to compare the performance of heuristic and meta-heuristic solution strategies. In addition, the Python program used to generate the problem instances is supplied, allowing researchers to generate new problem instances. Elsevier 2022-04-25 /pmc/articles/PMC9079686/ /pubmed/35539021 http://dx.doi.org/10.1016/j.dib.2022.108208 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Harrison, Kyle Robert
Elsayed, Saber M.
Garanovich, Ivan L.
Weir, Terence
Boswell, Sharon G.
Sarker, Ruhul A.
Generating datasets for the project portfolio selection and scheduling problem
title Generating datasets for the project portfolio selection and scheduling problem
title_full Generating datasets for the project portfolio selection and scheduling problem
title_fullStr Generating datasets for the project portfolio selection and scheduling problem
title_full_unstemmed Generating datasets for the project portfolio selection and scheduling problem
title_short Generating datasets for the project portfolio selection and scheduling problem
title_sort generating datasets for the project portfolio selection and scheduling problem
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9079686/
https://www.ncbi.nlm.nih.gov/pubmed/35539021
http://dx.doi.org/10.1016/j.dib.2022.108208
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