An integrated AHP-GP-GA approach for public project portfolio selection problem

Strategically selecting projects and managing the project portfolio enables firms to enhance their comprehension of project risks and benefits. The capacity to choose a suitable combination of projects is a considerable benefit in the project selection process when confronted with budgetary and othe...

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Main Authors: A.O. Mogbojuri, O.A. Olanrewaju
Format: Article
Language:English
Published: Elsevier 2025-10-01
Series:Ain Shams Engineering Journal
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Online Access:http://www.sciencedirect.com/science/article/pii/S2090447925003855
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author A.O. Mogbojuri
O.A. Olanrewaju
author_facet A.O. Mogbojuri
O.A. Olanrewaju
author_sort A.O. Mogbojuri
collection DOAJ
description Strategically selecting projects and managing the project portfolio enables firms to enhance their comprehension of project risks and benefits. The capacity to choose a suitable combination of projects is a considerable benefit in the project selection process when confronted with budgetary and other limitations. The selection of projects via an effective methodology is rare, as numerous approaches are considered useless due to constraints on the number of projects available and the inability to identify cost-efficient initiatives. The study presents integrated models of the Analytic Hierarchy Process, Goal Programming, and Genetic Algorithm (AHP-GP-GA) by removing the bias of each model for Public PPSP and building a relationship between the developed models.The AHP method was utilized to establish project selection criteria, allocate relative priority values to stakeholders, and calculate the overall weighting of project alternatives. The GP employed a computational approach to address numerous goals and limitations. The GA is a suitable mechanism for an efficient and versatile algorithm for optimisation that yields the best possible results. The empirical study demonstrates the fact the integrated approach effectively tackles wide ranging or multiplex issues involving various decision factors, yielding more optimal solutions and exhibiting resilience and adaptability in decision-making problems. The AHP-GP-GA model gives project selection criteria that are more significant in societal sustainability and the Public Project Portfolio Selection Problem (PPSP), has been used to more than 200 large projects, and has been contributed to the literature to improve the current approaches. It also removes the bias in selecting projects.
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spelling doaj-art-fa63012bfcdf424a80804ffdf58202072025-08-20T04:02:31ZengElsevierAin Shams Engineering Journal2090-44792025-10-01161010364410.1016/j.asej.2025.103644An integrated AHP-GP-GA approach for public project portfolio selection problemA.O. Mogbojuri0O.A. Olanrewaju1Department of Industrial Engineering, Durban University of Technology, Durban, South Africa; Institute of Systems Science, Durban University of Technology, Durban, South Africa; Corresponding author.Institute of Systems Science, Durban University of Technology, Durban, South AfricaStrategically selecting projects and managing the project portfolio enables firms to enhance their comprehension of project risks and benefits. The capacity to choose a suitable combination of projects is a considerable benefit in the project selection process when confronted with budgetary and other limitations. The selection of projects via an effective methodology is rare, as numerous approaches are considered useless due to constraints on the number of projects available and the inability to identify cost-efficient initiatives. The study presents integrated models of the Analytic Hierarchy Process, Goal Programming, and Genetic Algorithm (AHP-GP-GA) by removing the bias of each model for Public PPSP and building a relationship between the developed models.The AHP method was utilized to establish project selection criteria, allocate relative priority values to stakeholders, and calculate the overall weighting of project alternatives. The GP employed a computational approach to address numerous goals and limitations. The GA is a suitable mechanism for an efficient and versatile algorithm for optimisation that yields the best possible results. The empirical study demonstrates the fact the integrated approach effectively tackles wide ranging or multiplex issues involving various decision factors, yielding more optimal solutions and exhibiting resilience and adaptability in decision-making problems. The AHP-GP-GA model gives project selection criteria that are more significant in societal sustainability and the Public Project Portfolio Selection Problem (PPSP), has been used to more than 200 large projects, and has been contributed to the literature to improve the current approaches. It also removes the bias in selecting projects.http://www.sciencedirect.com/science/article/pii/S2090447925003855Analytic hierarchy processGoal programmingGenetic algorithmsMulti-criteria decision makingProject selection criteriaPublic project portfolio selection problem
spellingShingle A.O. Mogbojuri
O.A. Olanrewaju
An integrated AHP-GP-GA approach for public project portfolio selection problem
Ain Shams Engineering Journal
Analytic hierarchy process
Goal programming
Genetic algorithms
Multi-criteria decision making
Project selection criteria
Public project portfolio selection problem
title An integrated AHP-GP-GA approach for public project portfolio selection problem
title_full An integrated AHP-GP-GA approach for public project portfolio selection problem
title_fullStr An integrated AHP-GP-GA approach for public project portfolio selection problem
title_full_unstemmed An integrated AHP-GP-GA approach for public project portfolio selection problem
title_short An integrated AHP-GP-GA approach for public project portfolio selection problem
title_sort integrated ahp gp ga approach for public project portfolio selection problem
topic Analytic hierarchy process
Goal programming
Genetic algorithms
Multi-criteria decision making
Project selection criteria
Public project portfolio selection problem
url http://www.sciencedirect.com/science/article/pii/S2090447925003855
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