A neural network for resource constrained project scheduling programming

A neural network for resource constrained project scheduling programming

The resource constrained project-scheduling problem (RCPSP) aims to minimize the duration of a project. RCPSP is prevalently used in programming the projects with high number of activities and resources such as construction projects. In this study, 240 projects such as residential, office, school,...

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Bibliographic Details
Main Authors: Ömer Özkan, Ümit Gülçiçek
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2015-01-01
Series:Journal of Civil Engineering and Management
Subjects:
project management
construction project
resource constraints
priority rules
artificial neural network
Online Access:http://journals.vgtu.lt/index.php/JCEM/article/view/2987
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spelling doaj-237874ee78e447358aac6e98f1a22e0c2021-07-02T09:15:27ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052015-01-0121210.3846/13923730.2013.802723A neural network for resource constrained project scheduling programmingÖmer Özkan0Ümit Gülçiçek1Istanbul Medeniyet University, Department of Civil Engineering, 34730, Istanbul, TurkeyKahramanmaraş Sutcu Imam University, Elbistan Vocational School, 46300, Kahramanmaraş, Turkey The resource constrained project-scheduling problem (RCPSP) aims to minimize the duration of a project. RCPSP is prevalently used in programming the projects with high number of activities and resources such as construction projects. In this study, 240 projects such as residential, office, school, etc. are designed and programmed under limited resources. The resource amounts of these projects are determined using three priority rules, these are Latest Finish Time, Minimum Slack Time and Maximum Remaining Path Length which have the highest performance according to the literature, in the amounts of 2, 4, 6 and 8. The project times are estimated using artificial neural network (ANN). A correlation coefficient of 0.70 was obtained from the ANN estimation model. http://journals.vgtu.lt/index.php/JCEM/article/view/2987project managementconstruction projectresource constraintspriority rulesartificial neural network
collection DOAJ
language English
format Article
sources DOAJ
author Ömer Özkan
Ümit Gülçiçek
spellingShingle Ömer Özkan
Ümit Gülçiçek
A neural network for resource constrained project scheduling programming
Journal of Civil Engineering and Management
project management
construction project
resource constraints
priority rules
artificial neural network
author_facet Ömer Özkan
Ümit Gülçiçek
author_sort Ömer Özkan
title A neural network for resource constrained project scheduling programming
title_short A neural network for resource constrained project scheduling programming
title_full A neural network for resource constrained project scheduling programming
title_fullStr A neural network for resource constrained project scheduling programming
title_full_unstemmed A neural network for resource constrained project scheduling programming
title_sort neural network for resource constrained project scheduling programming
publisher Vilnius Gediminas Technical University
series Journal of Civil Engineering and Management
issn 1392-3730
1822-3605
publishDate 2015-01-01
description The resource constrained project-scheduling problem (RCPSP) aims to minimize the duration of a project. RCPSP is prevalently used in programming the projects with high number of activities and resources such as construction projects. In this study, 240 projects such as residential, office, school, etc. are designed and programmed under limited resources. The resource amounts of these projects are determined using three priority rules, these are Latest Finish Time, Minimum Slack Time and Maximum Remaining Path Length which have the highest performance according to the literature, in the amounts of 2, 4, 6 and 8. The project times are estimated using artificial neural network (ANN). A correlation coefficient of 0.70 was obtained from the ANN estimation model.
topic project management
construction project
resource constraints
priority rules
artificial neural network
url http://journals.vgtu.lt/index.php/JCEM/article/view/2987
work_keys_str_mv AT omerozkan aneuralnetworkforresourceconstrainedprojectschedulingprogramming
AT umitgulcicek aneuralnetworkforresourceconstrainedprojectschedulingprogramming
AT omerozkan neuralnetworkforresourceconstrainedprojectschedulingprogramming
AT umitgulcicek neuralnetworkforresourceconstrainedprojectschedulingprogramming
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