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Computer-based decision models for R&D project selection in public organizations

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Project selection is the most important problem concerning R&D management in public organizations, where weak heuristics are used for evaluating projects and making decisions about final portfolios. We propose here an integrated approach for analyzing projects and solving portfolio problems whose central parts are the use of decision tables as models of decision-maker's preferences and beliefs, and a mode! of R&D portfolio quality derived from Utility Theory and based on fuzzy sets to model some sources of imprecision. The resulting optimization problem is very complex in order to be solved by classical mathematical programming methods, so we propose an evolutionary algorithm able to achieve a strong improvement of the quality of solution. Some results are applicable in other problems outside the scope of this paper.
Rocznik
Strony
103--131
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
  • Autonomous University of Sinaloa, (52)-667-7161361. Escula de Informatica, Josefa Ortiz de Dominguez, Ciudad Universitaria, Culican, Sinaloa, Mexico
autor
  • Sinaloa Science Center. Ave. de las Americas No. 2771 nte. Culiacán, Sinaloa, Mexico
Bibliografia
  • [1] Bäck Т., Evolutionary Algorithms in theory and practice, Oxford University Press, New York, 1996.
  • [2] Bäck Т., Fogel D.B., Michalewicz Z., Evolutionary Computation 2, Advanced Algorithms and Operators, Institute of Physics Publishing, Bristol - Philadelphia, 2000.
  • [3] Coello C., Van Veldhuizen D., Lamont G., Evolutionary Algorithms for solving multiobjective problems, Kluwer Academic Publishers, New York, 2002.
  • [4] CONACYT, Private communication from several public officials, 2001.
  • [5] Cooper R.G., Edgett S.J., Kleinschmidt E.J., Best practices for managing R&D portfolios. Research & Technology Management, 41, 4, 1998, 20-25.
  • [6] Davis J., Fusfeld A., Scriven E., Tritle G., Determining a project’s probability of success. Research & Technology Management, 44, 3, 2001, 51-57.
  • [7] Fernandez E., Navarro J., A genetic search for exploiting a fuzzy preference model of portfolio problems with public projects. Annals of Operations Research, 177, 1, 2002, 191-213.
  • [8] French S., Decision Theory: an Introduction to the Mathematics of Rationality, Ellis Horwood, London, 1993.
  • [9] Goldberg D., Genetic algorithms in search, optimization and machine learning, Addison- Wesley, Reading, MA., 1989.
  • [10] Han J., Kamber M. Data Mining. Concepts and techniques. Academic Press, San Francisco- San Diego-NY-Boston-London-Sydney-Tokyo, 2001.
  • [11] Henriksen A.D., Traynor A.J., A practical R&D project selection scoring tool, IEEE Transactions on Engineering Management, 46, 2, 1999, 158-170.
  • [12] Jackson В., Decision methods for evaluating R&D projects, Research & Technology Management, 26, 4, 1983, 16-22.
  • [13] Krawiec F., Evaluating and selecting research projects by scoring, Research & Technology Management, 27, 2, 1984, 21-25.
  • [14] Leyva J.C., Fernandez E., A genetic algorithm for deriving final ranking from a fuzzy outranking relation, Foundations of Computing and Decision Sciences, 24, 1, 1999, 33-47.
  • [15] Markowitz H., Portfolio Selection (2nd. Ed.), Blackwell, Cambridge, MA, 1991.
  • [16] Martino J., Research and Development Project Selection, Wiley, NY - Chichester - Brisbane - Toronto - Singapore, 1995.
  • [17] Michalewicz Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, Berlin - Heidelberg - New York, 1996.
  • [18] Ostanello A., Outranking Methods, Proc. of the First Summer School on MCDA, Sicily, 1983, 41-60.
  • [19] Pawlak Z., Slowiński R., Rough Sets approach to multi-attribute decision analysis, European Journal of Operational Research, 72, 3, 1994, 443-459.
  • [20] Roy В., Multicriteria methodology for Decision Aiding, Kluwer Academic Publisher, Dordrecht- Boston- London, 1996.
  • [21] Roy В., The outranking approach and the foundations of ELECTRE methods, in: Bana e Costa, C.A., (eds.), Reading in multiple criteria decision aid. Springer-Verlag, Berlin, 1990, 155-183.
  • [22] Slowiński R., Rough sets learning of preferential attitude in multi-criteria decision making, in: Komorowski, J., Ras, Z.W (eds.). Methodologies for Intelligent Systems, LNAI 689, Springer Verlag, Berlin, 1993, 642-651.
  • [23] Slowiński R., Rough Set approach to Decision Analysis, AI Expert, 10, 3, 1995, 19-25.
  • [24] Slowiński R., Stefanowski J., Rough classification with valued closeness relation, in: Diday E., Lechevallier Y, Schader M., Bertrand P., Burtschy B. (eds.). New approaches in classification and data analysis. Springer Verlag, Berlin-Heidelberg-NY, 1994, 482-489.
  • [25] Tritle G., Scriven E, Fusfeld A.R., Resolving uncertainty in R&D portfolios. Research & Technology Management, 43, 6, 2000, 47-55.
  • [26] UNESCO, http://www.uis.unesco.org/en/stats/stats0.htm, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0049-0023
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