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Tytuł artykułu

The support of alternative project choice with using intelligence systems

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper aims to make an approach that supports the process of taking investment decision in case if the primary project cannot be completed. The factors, which occur in investment decision-making, often have an imprecise and uncertain form. In this case may be used a fuzzy neural system that supports the choice of alternative project by improvement in the estimates precision for requested resources. The paper contains an example with the using of different approaches in the estimation of time for project critical tasks that commit the substantial enterprise resources.
Rocznik
Strony
7--18
Opis fizyczny
Bibliogr. 15 poz., fig., tab.
Twórcy
autor
  • Faculty of Economics and Management, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
Bibliografia
  • [1] ARAIN F., PHENG L.: Knowledge-based decision support system for management of variation orders for institutional building projects, Automation in Construction, vol. 15, 2006, pp. 272–291.
  • [2] BA S., LANG K., WHINSTON A.: Handbook on Decision Support Systems, Springer, 2008.
  • [3] BLACKWELL P., SHEHAB E., KAY J.: An effective decision-support framework for implementing enterprise information systems within SMEs, International Journal of Production Research, vol. 44, no. 17, 2006, pp. 3533–3552.
  • [4] CHIEN S., WANG T., LIN S.: Application of neuro-fuzzy networks to forecast innovation performance, Expert Systems with Applications, vol. 37, 2010, pp. 1086-1095.
  • [5] CHIU S.: Fuzzy Model Identification Based on Cluster Estimation, Journal of Intelligent & Fuzzy Systems, vol. 2, no. 3, 1994.
  • [6] KHOO B., FORGIONNE G., HARRIS P.: Enterprise decision support systems integration: an object request broker approach, Global Journal of Business Research, vol. 3, no. 2, 2009.
  • [7] KIM D., ABRAHAM A.: Optimal learning of fuzzy neural network using artificial immune algorithm, Neural Network World, vol. 18, 2008, pp. 147-170.
  • [8] KUO R., CHEN J.: A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm, Expert Systems with Applications, vol. 26, 2004, pp.141-154.
  • [9] LAURENT A.: A new approach for the generation of fuzzy summaries based on fuzzy multidimensional databases, Intelligent Data Analysis, vol. 7, 2003, pp. 155–177.
  • [10] LIN CH., LI S., KUO S.: Intelligent physician segmentation and management based on KDD approach, Expert Systems with Applications, vol. 34, 2008, pp. 1963–1973.
  • [11] PAL S., MITRA S.: Neuro-fuzzy pattern recognition, John Wiley & Sons, New York, 1999.
  • [12] PENG Y., KOU G., SHI Y., CHEN Z.: A descriptive framework for the field of data mining and knowledge discovery, International Journal of Information Technology & Decision Making, vol. 7, no. 4, 2008, pp. 639–682.
  • [13] SMITH K., GUPTA J.: Neural networks in business: techniques and application for the operations researcher, Computers&Operations Research, vol. 27, 2000, pp. 1023-1044.
  • [14] TAN K., LIM CH., PLATTS K., KOAY H.: An intelligent decision support system for manufacturing technology investment, International Journal of Production Economics, vol. 104, 2006, pp. 179-190.
  • [15] WEN W., CHEN Y., PAO H.: A mobile knowledge management decision support system for automatically conducting an electronic business, Knowledge-Based Systems, vol. 21, 2008, pp. 540-550.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7c5a5057-8ea2-419f-b199-ed54af7d172f
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