Share This Article:

Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

Abstract Full-Text HTML Download Download as PDF (Size:148KB) PP. 502-508
DOI: 10.4236/ajor.2012.24059    4,160 Downloads   6,484 Views   Citations

ABSTRACT

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

K. Zaras, J. Marin and B. Boudreau-Trude, "Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects," American Journal of Operations Research, Vol. 2 No. 4, 2012, pp. 502-508. doi: 10.4236/ajor.2012.24059.

References

[1] R. S. Kaplan, “Strategic Performance Measurement and Management in Non-profit Organizations,” Non-Profit Management and Leadership, Vol. 11, No. 3, 2001, pp. 353-370.
[2] R. S. Kaplan and D. Norton, “The Balanced Scorecard: Translating Strategy into Action,” Harvard Business School Publishing, Boston, 1996.
[3] Y.-C. L. Chan, “Performance Measurement and Adoption of Balanced Scorecards: A Survey of Municipal Governments in the USA and Canada,” International Journal of Public Sector Management, Vol. 17, No. 3, 2004, pp. 204-221. doi:10.1108/09513550410530144
[4] Z. Pawlak, “Rough Set,” International Journal of Parallel Programming, Vol. 11, No. 5, 1982, pp. 341-356.
[5] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data,” Kluwer Academic Publishing, Dordrecht, 1991.
[6] Z. Pawlak and R. Slowinski, “Rough Set Approach to Multi-attribute Decision Analysis,” European Journal Operational Research, Vol. 72, No. 3, 1994, pp. 443-459. doi:10.1016/0377-2217(94)90415-4
[7] Z. Pawlak, “Rough Set Theory and Its Applications,’’ Journal of Telecommunications and Information Theory, Vol. 3, 2002, pp. 7-10.
[8] S. Greco, B. Matarazzo and R. Slowinski, “Rough Sets Theory for Multi-Criteria Decision Analysis,” European Journal of Operational Research, Vol. 129, No. 1, 2001, pp. 1-47. doi:10.1016/S0377-2217(00)00167-3
[9] SADC, “Annual Report 2010-2011,” 2012.
[10] K. Zaras, “Rough Approximation of a Preference Relation by a Multi-Attribute Dominance for Deterministic, Stochastic and Fuzzy Decision Problems,” European Journal of Operational Research, Vol. 159, No. 1, 2004, pp. 196-206. doi:10.1016/S0377-2217(03)00391-6
[11] R. Slowinski, “Multi-Criteria Intelligent Decision Support Systems—Inteligentne Systemy Wielokryterialnego Wspomagania Decyzji,” Rapport RB-004/97, Institute of Computing Science, Poznan, 1997.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.