Project Selection: Artificial Neural Network Approach

Olanrewaju Oludolapo Akanni; Jimoh Abdul-Ganiyu Adisa; Kholopane Pule

doi:doi:10.11648/j.sjbm.20130103.11

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Project Selection: Artificial Neural Network Approach

Olanrewaju Oludolapo Akanni, Jimoh Abdul-Ganiyu Adisa, Kholopane Pule

Published in Science Journal of Business and Management (Volume 1, Issue 3)

Received: 7 August 2013 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.

Published in	Science Journal of Business and Management (Volume 1, Issue 3)
DOI	10.11648/j.sjbm.20130103.11
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), 2013. Published by Science Publishing Group

Keywords

Project Selection, Regression Analysis, Artificial Neural Network

References

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[4]	Lawson, C.P., P.J. Longhurst, and P.C. Ivey, The application of a new research and development project selection model in SMEs. Technovation, 2006. 26: p. 242-250.
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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.
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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.
[14]	Hayati, M. and Y. Shirvany, Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region. 2007, World Academy of Science, Engineering and Technology.
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[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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APA Style

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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Project Selection: Artificial Neural Network Approach
AU  - Olanrewaju Oludolapo Akanni
AU  - Jimoh Abdul-Ganiyu Adisa
AU  - Kholopane Pule
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.sjbm.20130103.11
DO  - 10.11648/j.sjbm.20130103.11
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 37
EP  - 42
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20130103.11
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.
VL  - 1
IS  - 3
ER  -

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Author Information

Olanrewaju Oludolapo Akanni

Industrial Engineering Department, Tshwane University of Technology, Pretoria, South Africa
Jimoh Abdul-Ganiyu Adisa

Electrical Engineering Department, Tshwane University of Technology, Pretoria, South Africa
Kholopane Pule

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

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APA Style

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Project Selection: Artificial Neural Network Approach
AU  - Olanrewaju Oludolapo Akanni
AU  - Jimoh Abdul-Ganiyu Adisa
AU  - Kholopane Pule
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.sjbm.20130103.11
DO  - 10.11648/j.sjbm.20130103.11
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 37
EP  - 42
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20130103.11
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.
VL  - 1
IS  - 3
ER  -

Copy | Download