Proposing a fuzzy multi objective model for green project portfolio under inflation

Document Type : Research Paper

Authors

1 Department of industrial engineering, Payam-e-noor University, IRAN

2 Payame Noor University

3 Master of science student

Abstract

Correct selection of projects The first step is project-based organizations in the targeted management of project portfolios. This is a complex process selection that includes many factors and considerations. Market conditions, global rapid changes in various dimensions and other related issues in the real environment have increased the uncertainty and ignorance of these issues. It is therefore necessary to provide models for showing the real status of the organization and its goals and preferences. In this paper, the goal is to provide a fuzzy fuzzy multi-objective model for the portfolio of rail transport projects considering the uncertainties in variables; budget, time needed to complete the project, environmental pollution, risk, and quality. In this model, minimizing environmental pollution, maximizing quality, minimizing the risk and cost of projects under inflation is considered in the objectives of the problem. Due to the fact that the model was presented, a particle swarm algorithm was used to solve the problem, and finally, the results were compared with the genetic algorithm in order to measure the efficiency.

Keywords

Main Subjects


  1. Killen, C. P., Hunt, R. A., and Kleinschmidt E. J., (2007). “Managing the New Product Development Project Portfolio. In Management of Engineering and Technology”, Portland International, Vol. 25, No. 2, PP. 1864–1874.
  2. Depiante, A., and Jensen, A., (1999). “A Practical R and D Project-Selection Scoring Tool”, IEEE Trans Eng Manage, Vol. 46, No. PP. 158-170.
  3. Cristóbal, J. R. S., (2011). “Multi-Criteria Decision-Making in the Selection of a Renewable Energy Project in Spain: The Vikor Method”, Renewable Energy, Vol. 36, No. 2, PP. 498-502.
  4. Badri, M. A., and Davis, D., (2001). “A Comprehensive 0-1 Goal Programming Model For Project Selection”, International Journal of Project Management, Vol. 19, No. 4, PP. 243-252.
  5. Weber, R., Werners, B., and Zimmermann, H., (1990). “Scheduling Models for Research and Development”, European Journal of Operational Research, Vol. 48, No. 2, PP. 175-188.
  6. Kyparisis, G. J., Gupta, S. K., and LP, C. M., (1996). “Project Selection with Discounted Returns and Multiple Constraints”, European Journal of Operational Research, Vol. 94, No. 1, PP. 87-96.
  7. Santhanam, R., and Kyparisis, G. J., (1996). “A Decision Model for Interdependent Information System Project Selection”, European Journal of Operational Research, Vol. 89, No. 2, PP. 380-399.
  8. Vazhayil, J. P., and Balasubramanian, R., (2014). “Optimization of India’s Electricity Generation Portfolio Using Intelligent Pareto-Search Genetic Algorithm”, International Journal of Electrical Power and Energy Systems, Vol. 55, No. 1, PP. 13-20.
  9. Ahadi, S. et al., (2016). “Annual Tehran Air Quality Report”, City Publishing Center, Vol. 1, No. 2, PP.15-23.
  10. Archer, N. P., and Ghasemzadeh, F., (1999). “An Integrated Framework for Project Portfolio Selection”, International Journal of Project Management, Vol. 17, No. 4, PP. 207-216.
  11. Liu, S. T., (2011). “A Fuzzy Modeling for Fuzzy Portfolio Optimization”, Expert Systems with Applications, Vol. 38, No. 5, PP. 13803–13809.
  12. Purnus, A., and Bodea, C. N., (2014). “Project Prioritization and Portfolio Performance Measurement in Project Oriented Organizations”, Procedia-Social and Behavioral Sciences, Vol. 119, No. 1, PP. 339-348.
  13. Reginaldo, F., (2015). “Portfolio Management in Brazil and a Proposal for Evaluation and Balancing of Portfolio Projects with ELECTRE TRI and IRIS”, Procedia Computer Science, Vol. 55, No. 1, PP. 1265-1274.
  14. Mehlawat, M. K., (2016). “Credibilistic Mean-Entropy Models for Multi-Period Portfolio Selection with Multi-Choice Aspiration Levels”, Information Sciences, Vol. 345, No. 1, PP. 9-26.
  15. Gupta, P., Mehlawat, M. K., and Saxena, A., (2008). “Asset Portfolio Optimization Using Fuzzy Mathematical Programming”, Information Sciences, Vol. 178, No. 6, PP. 1734-1755.
  16. Yu, J. R., and Lee, W. Y., (2011). “Portfolio Rebalancing Model Using Multiple Criteria”, European Journal of Operational Research, Vol. 209, No. 2, PP. 166–175.
  17. Rahmani, N., Talebpour, A., and Ahmadi, T., (2012). “Developing Amulti Criteria Model for Stochastic IT Portfolio Selection by AHP Method”, Procedia-Social and Behavioral Sciences, Vol. 62, No. 1, PP. 1041–1045.
  18. Nassif, L. N., Santiago Filho, J. C., and Nogueira, J. M., (2013). “Project Portfolio Selection in Public Administration Using Fuzzy Logic”, Procedia-Social and Behavioral Sciences, Vol. 74, No. 1, PP. 41-50.
  19. Vazhayil, J. P., and Balasubramanian, R., (2014). “Optimization of India’s Electricity Generation Portfolio Using Intelligent Pareto-Search Genetic Algorithm”, International Journal of Electrical Power and Energy Systems, Vol. 55, No. 1, PP. 13-20.
  20. Khosravi, A., (2014). “Making Multi-Objective Dynamic Model of Portfolio Management Under Uncertainty”, Master's Thesis, Payame Noor University of Assaluye.
  21. Zeng, Z.  et al., (2015). “A Multiple Objective Decision Making Model for Energy Generation Portfolio Under Fuzzy Uncertainty: Case Study of Large Scale Investor-Owned Utilities in Florida”, Renewable Energy, Vol. 75, No. 2, PP. 224-242.
  22. Liu, Y., Liu, Y.K, (2017). “Distributionally Robust Fuzzy Project Portfolio Optimization Problem with Interactive Returns”, Applied Soft Computing, Vol. 56, No. 3, PP. 655-668.
  23. Pérez, F. et al., (2018). “Project Portfolio Selection and Planning with Fuzzy Constraints”, Technological Forecasting and Social Change, Vol. 131, No. 1, PP. 117-129.
  24. Zhai, P, and Bai, M., (2018). “Mean-Risk Model for Uncertain Portfolio Selection with Background Risk”, Journal of Computational and Applied Mathematics, Vol. 330, No. 1, PP. 59-69.
  25. Shahraki, A., and Moradi, M., (2013). “Risk Assessment in the Workplace with Job Safety Analysis, Nominal Group Technique and Fuzzy TOPSIS”, Iranian Health Workbook, Vol. 10, No. 4, PP. 43-54.
  26. Khaje Golestane, A., (2015). “Comparison of Numerical Solutions for Intervals Linear Programming Based on Coverage and Validity”, Master's Thesis, University Of Sistan-Baluchestan.
  27. Hladık, M., (2012). “Interval Linear Programming: A Survey”, Linear Programming-New Frontiers in Theory and Applications, PP. 85-120.
  28. Shahrozi, M., and Salehi, A., (2017). Functional Indicators for Comparison of Fractronic Algorithms with Basic Differences in Operators. 1th International Soft Computing Conference, Gilan, Iran.
  29. Mizban, H. et al., (2012). “Optimization of Stock Basket Using Particle Swarm Algorithm in Different Risk Measurement Definitions”, Economics Quarterly, Vol. 19, No. 2, PP. 205-224.
  30. Sherafatman, SH., (2012). “Solving Fuzzy Linear Programming and Bi-Level Linear Programming Problem Using Genetic Algorithms”, Master's Thesis, University Of Sistan-Baluchestan.