Project Portfolio Selection with Considering Interaction Between Projects using Particle Swarm Optimization (PSO) & Chaotic Dynamic

Document Type : Original Article

Authors

1 Associate Professor, Shahid Beheshti University.

2 M.S, Shahid Beheshti University.

3 M.S, Allameh Tabataba'i University.

Abstract

Given the complexity of the project implementation and resource constraints, the project portfolio selection is important for organization-s. Hence, many researchers have attempted to provide methods for portfolio selection and often obtained interesting results. But most of them have not considered the interaction between projects. Considering the interactions between projects lead to complexity of portfolio selection problem and if these interactions be ignored, the decision making process maybe produce the undesired final results. In this paper, the portfolio selection problem with considering interactions between the projects is formulated. The portfolio selection problem with regard the interactions between projects using optimization algorithms particle swarm optimization (PSO) andchaotic particle swarm optimization (CPSO) was investigated. Interactions at the projects selection, final solution and its fitness show the important effects. It was shown that the PSO and CPSO methods are better in comparison with the genetic algorithm technique used before in such problems.

Keywords


1. Aaker, D. A., & Tyebjee, T. T. (1978). A model for the selection of interdependent R&D projects. IEEE Transactions on Engineering Management, 25, 30–36.
2. Abido, M. A. (2002). Optimal design of power system stabilizers using particle swarm optimization. IEEE Trans. Energy Conversion, Vol. 17, pp. 406 - 413.
3. Abiyev, R. H., & Menekay, M. (2007). Fuzzy portfolio selection using genetic algorithm. Soft Computing, 11, 1157–1163.
4. Beni, G., Wang, J. (1989). Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30.
5. Bo Liu, Ling Wang, Yi-Hui Jin, Fang Tang, De-Xian Huang (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals, 25, 1261–1271.
6. Bouyssou, D., Marchant, T., Pirlot, M., Tsoukias, A., & Vincke, P. (2006). Evaluation and decision models with multiple criteria: stepping stones for the analyst. New York: Springer.
7. Carlsson, C., & Fuller, R. (1995). Multiple criteria decision making: the case for interdependence. Computers& Operations Research, 22, 251–260.
8. Carraway, R. L., & Schmidt, R. L. (1991). An improved discrete dynamic programming algorithm for allocating resources among interdependent projects. Management Science, 37, 1195–1200.
9. Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (1999). New product portfolio management: practices and performance. Journal of Product Innovation Management, 16, 333–351.
10. De Castro, L. N. & Von Zuben, F. J. (2001). Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems.
11. Devaney & Robert L. (2003). An Introduction to Chaotic Dynamical Systems. 2nd ed. Westview Press, ISBN 0-8133-4085-3, (2003).
12. Dorigo, M. and Stützle, T. (2004). Ant Colony Optimization by, MIT Press. ISBN 0-262-04219-3.
13. Ewing, P. L. Jr., Tarantino, W., & Parnell, G. S. (2006). Use of decision analysis in the army base realignment and closure (BRAC) 2005 military value analysis. Decision Analysis, 3, 33–49.
14. Fox, G. E., Baker, N. R., & Bryant, J. L. (1984). Economic models for R and D project selection in the presence of project interactions. Management Science, 30, 890–902.
15. Fukuyama, Y. (2000). A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst., Vol. 15, pp. 1232 - 1239.
16. Golabi, K. (1987). Selecting a group of dissimilar projects for funding. IEEE Transactions on EngineeringManagement, 34, 138–145.
17. Golabi, K., Kirkwood, C. W., & Sicherman, A. (1981). Selecting a portfolio of solar energy projects using multi-attribute preference theory. Management Science, 27, 174–189.
18. Henriksen, A. D. P., & Palocsay, S. W. (2008). An Excel-based decision support system for scoring and ranking proposed R&D projects. International Journal of Information Technology and Decision Making, 7(3), 529–546.
19. Karaboga & Dervis (2010). Artificial bee colony algorithm, Scholarpedia, 5(3): 6915.
20. Kennedi, J., & Eberhart, R. (1995). A Discrete Binary of the Particle Swarm Algorithm’ , in IEEE Int . Conf. Vol. 4, No.2, PP 1942-1948.
21. Kleinmuntz, C. E., & Kleinmuntz, D. N. (1999). Strategic approaches for allocating capital in healthcare organizations. Healthcare Financial Management, 53, 52–58.
22. Kleinmuntz, D. N. (2007). Resource allocation decisions. In W. Edwards, R. F. Miles, & D. von Winter- feldt (Eds.), Advances in decision analysis: from foundations to applications. New York: Cambridge University Press.
23. Krishnanand, K.N. & Ghose, D. (2009). Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Computational Intelligence Studies, Vol. 1, No. 1.
24. Liesio, J. (2006). Robust portfolio optimization in multi-criteria project selection. Licentiate’s Thesis, Helsinki University of Technology.
25. Liesio, J., Mild, P., & Salo, A. (2007). Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, 181, 1488–1505.
26. Mavrotas, G., Diakoulaki, D., & Caloghirou, Y. (2006). Project prioritization under policy restrictions: a com- bination of MCDA with 0–1 programming. European Journal of Operational Research, 171, 296–308.
27. Medaglia, A. L., Graves, S. B., & Ringuest, L. J. (2007). A multiobjective evolutionary approach for linearly constrained project selection under uncertainty. European Journal of Operational Research, 179, 869– 894.
28. Medaglia, A. L., Hueth, D., Mendieta, J. C., & Sefair, J. A. (2007). Multiobjective model for the selection and timing of public enterprise projects. Socio-Economic Planning Sciences, 41, 31–45.
29. Peng, Y., Kou, G., Shi, Y., & Chen, Z. (2008). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology and Decision Making, 7(4), 639–682.
30. Peng, Y., Kou, G.,