A hybrid grey based artificial neural network and C&R tree for project portfolio selection

Abstract

Purpose The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm. Design/methodology/approach In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated. Findings The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk. Originality/value This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.

A system dynamics investigation of project portfolio management evolution in the energy sector

“…There are different tools and techniques in a vast amount of literature that are applicable for PPM, such as scoring model, mathematical programming model, economic or financial model, metaheuristic algorithms, robust optimization, stochastic programming and decision-analysis technique (Samuelson, 1969;Mulvey and Vladimirou, 1992;Neuneier, 1996;Pinto and Millet, 1999;Cooper et al, 2000;Detemple et al, 2003;Brandt et al, 2005;Banerjee, 2008;Morris and Pinto, 2010;Chen et al, 2013;Lorca and Prina, 2014;Solimanpur et al, 2015;Moghadam et al, 2015;Tavanaa et al, 2015;Nasr-Esfahani et al, 2016;Faezy-Razi and Shariat, 2017;Jafarzadeh et al, 2018;Montajabiha et al, 2017;Nayebpur and Nazem-Bokaei, 2017;Gokgoz and Atmaca, 2017;Costa et al, 2017;Péreza et al, 2018;Odeh et al, 2018;) .…”

PMBOK, IPMA and fuzzy-AHP based novel framework for leadership competencies development in megaprojects

“…Various tools, techniques and methods are used in literature to comprehend the importance of leadership in the selection of a project manager. Statistical techniques like correlation, simple regression (Shao et al, 2012), hierarchical regression (Tseng, 2017;Larsson et al, 2015), structural equation models (Anantatmula and Thomas, 2010) and qualitative approach (Razi and Shariat, 2017;Anantatmula, 2010) is used to select a project manager based on various criteria for complex projects. When it comes to selection of project manager or the supplier for a particular project (mega or regular), the majority of the studies frame it as a MCDM problem and tools like Fuzzy-AHP are widely used in the literature (Sarfaraz et al, 2015;Yousefi and Hadi-Vencheh, 2016).…”

“…, 2012), hierarchical regression (Tseng, 2017; Larsson et al. , 2015), structural equation models (Anantatmula and Thomas, 2010) and qualitative approach (Razi and Shariat, 2017; Anantatmula, 2010) is used to select a project manager based on various criteria for complex projects. When it comes to selection of project manager or the supplier for a particular project (mega or regular), the majority of the studies frame it as a MCDM problem and tools like Fuzzy-AHP are widely used in the literature (Sarfaraz et al.…”

“…, 2021; Sandhu and Khan, 2017; Varajão and Cruz-Cunha, 2013), there is a research void related to project manager's competencies. Research to date suggests that project manager competencies and the success of a project are closely related variables (Razi and Shariat, 2017; Patanakul, 2014).…”

“…Although the literature extensively examined the importance of project managers for the successful implementation of projects (Nixon et al, 2012;Serrador and Turner, 2015;Ahmed et al, 2021;Sandhu and Khan, 2017;Varajão and Cruz-Cunha, 2013), there is a research void related to project manager's competencies. Research to date suggests that project manager competencies and the success of a project are closely related variables (Razi and Shariat, 2017;Patanakul, 2014).…”