Science Journal of Business and Management

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Project Selection: Artificial Neural Network Approach

Received: 07 August 2013    Accepted:     Published: 20 October 2013
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Abstract

To prioritize projects and satisfy both the investors and the society from benefitting from the projects, a mathematical tool which has the characteristics of prediction and evaluation is required. If a dependable forecasting model could be achieved, it will be very valuable for the assessment and selection of projects. This paper employs artificial neural network (ANN) technique in the selection of projects. To demonstrate this technique, the ANN modelis illustrated using Oral, Kettani and Lang’s data on 37 R&D projects for its success. From the validation analysis, it was discovered that artificial neural network displayed a high potential to deciding how projects should be ranked and selected.

DOI 10.11648/j.sjbm.20130103.11
Published in Science Journal of Business and Management (Volume 1, Issue 3, October 2013)
Page(s) 37-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Project Selection, Regression Analysis, Artificial Neural Network

References
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[6] Liesio, J., P. Mild, and A. Salo, Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, 2007. 181: p. 1488-1505.
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[8] Bard, J., R. Balachandra, and P.E. Kaufmann, AN interactive approach to R&D project selection and termination. IEEE Transactions on Engineering Mnagement, 1988. 35(3): p. 139-146.
[9] Al-Rashdan, D., B. Al-Kloub, A. Dean, and T. Al-Shemmeri, Environmental impact assessment and ranking the environmental projects in Jordan. European Journal of Operational Research, 1999. 118: p. 30-45.
[10] Amina, M., V.S. Kodogiannis, I.P. Prtrounias, J.N. Lygouras, and G.-J.E. Nychas, Identification of the Listeria monocytogenes survival in UHT whole milk utilising local linear wavelet neural networks. Expert system with Applications, 2012. 39: p. 1435-1450.
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[12] Wei, S., J. Zhang, and Z. Li. A Supplier-selecting system using a neural network. in IEEE International Conference on Intelligent Processing systems. 1997.
[13] Oral, M., O. Kettani, and P. Lang, A methodology for collective evaluation and selection of industrial R&D projects. Management Science, 1991. 37(7): p. 871-83.
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[15] Hsu, C.-C. and C.-Y. Chen, Regional load forecasting in Taiwan-applications of artificial neural networks. Energy conversion and Management, 2003. 44: p. 1941-1949.
[16] Rumelhart, E., G.E. Hinton, and R.J. Williams, eds. Learning Internal Representations by Error Propagation in Parallel Distributed Processing 1. ed. J.L.M.a.t.P.R.G. D.E Rumelhart, Eds. 1986, MIT Press: Cambridge, MA. 318 - 362.
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Author Information
  • Industrial Engineering Department, Tshwane University of Technology, Pretoria, South Africa

  • Electrical Engineering Department, Tshwane University of Technology, Pretoria, South Africa

  • Industrial Engineering Department, University of Johannesburg, Johannesburg, South Africa

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    Olanrewaju Oludolapo Akanni, Jimoh Abdul-Ganiyu Adisa, Kholopane Pule. (2013). Project Selection: Artificial Neural Network Approach. Science Journal of Business and Management, 1(3), 37-42. https://doi.org/10.11648/j.sjbm.20130103.11

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    ACS Style

    Olanrewaju Oludolapo Akanni; Jimoh Abdul-Ganiyu Adisa; Kholopane Pule. Project Selection: Artificial Neural Network Approach. Sci. J. Bus. Manag. 2013, 1(3), 37-42. doi: 10.11648/j.sjbm.20130103.11

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    AMA Style

    Olanrewaju Oludolapo Akanni, Jimoh Abdul-Ganiyu Adisa, Kholopane Pule. Project Selection: Artificial Neural Network Approach. Sci J Bus Manag. 2013;1(3):37-42. doi: 10.11648/j.sjbm.20130103.11

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  • @article{10.11648/j.sjbm.20130103.11,
      author = {Olanrewaju Oludolapo Akanni and Jimoh Abdul-Ganiyu Adisa and Kholopane Pule},
      title = {Project Selection: Artificial Neural Network Approach},
      journal = {Science Journal of Business and Management},
      volume = {1},
      number = {3},
      pages = {37-42},
      doi = {10.11648/j.sjbm.20130103.11},
      url = {https://doi.org/10.11648/j.sjbm.20130103.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjbm.20130103.11},
      abstract = {To prioritize projects and satisfy both the investors and the society from benefitting from the projects, a mathematical tool which has the characteristics of prediction and evaluation is required. If a dependable forecasting model could be achieved, it will be very valuable for the assessment and selection of projects. This paper employs artificial neural network (ANN) technique in the selection of projects. To demonstrate this technique, the ANN modelis illustrated using Oral, Kettani and Lang’s data on 37 R&D projects for its success. From the validation analysis, it was discovered that artificial neural network displayed a high potential to deciding how projects should be ranked and selected.},
     year = {2013}
    }
    

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    AB  - To prioritize projects and satisfy both the investors and the society from benefitting from the projects, a mathematical tool which has the characteristics of prediction and evaluation is required. If a dependable forecasting model could be achieved, it will be very valuable for the assessment and selection of projects. This paper employs artificial neural network (ANN) technique in the selection of projects. To demonstrate this technique, the ANN modelis illustrated using Oral, Kettani and Lang’s data on 37 R&D projects for its success. From the validation analysis, it was discovered that artificial neural network displayed a high potential to deciding how projects should be ranked and selected.
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