Title: A hybrid grey-based k-means and genetic algorithm for project selection

Authors: Abbas Toloie Eshlaghy; Farshad Faezy Razi

Addresses: Department of Industrial Management, Islamic Azad University, Science and Research Branch, Tehran, Iran ' Department of Industrial Management, Islamic Azad University, Semnan Branch, Semnan, Iran

Abstract: Research and development (R&D) project selection is an important function for organisations with R&D project management. Project portfolio managers are preferred a portfolio of projects with multiple attribute criteria. So, project portfolio selection problem is a decision making process. This paper presents an integrated framework for project selection and project management approach using grey-based k-means and genetic algorithms. The proposed approach of this paper first cluster different projects based on k-means algorithm and then ranks R&D projects by grey relational analysis (GRA) model. In this paper, project allocation is selected by genetic algorithm (GA). The proposed framework is tested in a case study to show its usefulness and applicability in practice.

Keywords: grey relational analysis; GRA; k-means clustering; grey-based k-means; genetic algorithms; GAs; project selection; research and development; R&D projects; project management; project allocation.

DOI: 10.1504/IJBIS.2015.067262

International Journal of Business Information Systems, 2015 Vol.18 No.2, pp.141 - 159

Published online: 28 Mar 2015 *

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