A decision support system for alternative project choice based on fuzzy neutral networks

• Autorzy: Relich M.
• Języki publikacji: EN

Abstrakty

EN

The paper aims to present possibilities of management support by more precise estimates of critical tasks in projects through the use of intelligent techniques. In this paper a case is considered in which the client is forced to change the project specification after commencement of investment. To minimize the loss, the client may attempt to find other alternative solutions to complete the project. In view of expenditure and investment in progress, a group of alternative projects that fulfill the assumed constraints (e.g. financial and temporal) is sought. To support the choice of alternative projects, estimates of critical tasks within the project are calculated, using intelligent techniques as well as traditional statistical methods. The results are determined using the database of past projects that are found in the information systems of the enterprise.

Słowa kluczowe

EN

project evaluation; decision support; knowledge-based systems; fuzzy neural networks; investment salvage

PL

wspomaganie decyzji; systemy oparte na wiedzy; rozmyte sieci neuronowe

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2010
• Tom: Vol. 1, No. 4
• Strony: 46--54
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Relich M.

• University of Zielona Góra, Division of Controlling and Computer Applications in Economics, Podgórna 50, 65-246 Zielona Góra, Poland, phone: [phone],

Bibliografia

• [1] Meredith J.R., Mantel S.J., Project Management - a managerial approach, 3 rd ed., New York: John Wiley & Sons, 1995.
• [2] Nitithamyong P., Skibniewski M.J., Success/failure factors and performance measures of web-based construction project management systems: professionals’ viewpoint, Journal of Construction Engineering and Management, 132, 80-87, 2006.
• [3] Robertson S., Williams T., Understanding project failure: using cognitive mapping in an insurance project, Project Management Journal, 37, 55-71, 2006.
• [4] Shore B., Systematic biases and culture in project failures, Project Management Journal, 39, 5-16, 2008.
• [5] Yeo K.T., Critical failure factors in information system projects, International Journal of Project Management, 20, 241-246, 2002.
• [6] Cooke-Davies T.J., Arzymanow A., The maturity of project management in different industries: an investigation into variations between project management models, International Journal of Project Management, 21, 471-478, 2003.
• [7] Reichelt K., Lyneis J., The dynamics of project performance: benchmarking the drivers of cost and schedule overrun, European Management Journal, 17, 135-150, 1999.
• [8] Jorgensen M., Sjoberg D.I., The impact of customer expectation on software development effort estimates, International Journal of Project Management, 22, 317-325, 2004.
• [9] Gray R.J., Organisational climate and project success, International Journal of Project Management, 19, 103-109, 2001.
• [10] Journal of Management Development, Management’s role in project failure, The Journal of Management Development, vol. 20.
• [11] Belassi W., Tukel O.I., A new framework for determining critical success/failure factors in projects, International Journal of Project Management, 14, 141-151, 1996.
• [12] Armaneri O., Ozdagoglu G., Yalcinkaya O., An integrated decision support approach for project investors in risky and uncertain environments, Journal of Computational and Applied Mathematics, 234, 2530-2542, 2010.
• [13] Kutsch E., Hall M., Deliberate ignorance in project risk management, International Journal of Project Management, 28, 245-255, 2010.
• [14] Alessandri T.M., Ford D.N., Lander D.M., Leggio K.B., Taylor M., Managing risk and uncertainty in complex capital projects, The Quarterly Review of Economics and Finance, 44, 751-767, 2004.
• [15] Choobineh F., Behrens A., Use of intervals and possibility distributions in economic analysis, Journal of the Operational Research Society, 43, 907-918, 1992.
• [16] Zeng J., An M., Smith N.J., Application of a fuzzy based decision making methodology to construction project risk assessment, International Journal of Project Management, 25, 589-600, 2007.
• [17] Cheng M.Y, Tsai H.C., Sudjono E., Evolutionary fuzzy hybrid neural network for project cash flow control, Engineering Applications of Artificial Intelligence, 23, 604-613, 2010.
• [18] Chien S.C., Wang T.Y., Lin S.L., Application of neuro-fuzzy networks to forecast innovation performance, Expert Systems with Applications, 37, 1086-1095, 2010.
• [19] Kuo R.J., Chen J.A., 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, 26, 141-154, 2004.
• [20] Arain F.M., Pheng L.S., Knowlegde-based decision support system for management of variation orders for institutional building projects, Automation in Construction, 15, 272–291, 2006.
• [21] Gray C., Hughes W., Building design management, Oxford: Butterworth-Heinemann, 2001.
• [22] Ibbs C.W., Wong C.K.., Kwak Y.H., Project change management system, Journal of Management in Engineering, 17, 159-165, 2001.
• [23] 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 and Decision Making, 7, 639-682, 2008.
• [24] Smith K., Gupta J., Neural networks in business: techniques and application for the operations researcher, Computers and Operations Research, 27, 1023-1044, 2000.
• [25] Lin Ch., Li S., Kuo S., Intelligent physician segmentation and management based on KDD approach, Expert Systems with Applications, 34, 1963-1973, 2008.
• [26] Pal S., Mitra S., Neuro-fuzzy pattern recognition, New York: John Wiley & Sons, 1999.
• [27] Tan K., Lim Ch., Platts K., Koay H., An intelligent decision support system for manufacturing technology investment, International Journal of Production Economics, 104, 179-190, 2006.
• [28] Yager, R.R., Essentials of fuzzy modeling and control, New York: Wiley, 1994.
• [29] Chiu S., Fuzzy Model Identification Based on Cluster Estimation, Journal of Intelligent and Fuzzy Systems, 2, 267-278, 1994.
• [30] Takagi T., Sugeno M., Fuzzy identification of systems and its applications modeling and control, IEEE Transactions in Systems, Man and Cybernetics, 15, 116-132, 1985.