Multi-period project portfolio selection under risk considerations and stochastic income
الموضوعات :
Ali Asghar Tofighian
1
(Industrial Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran)
Hamid Moezzi
2
(Industrial Engineering Department, Faculty of Engineering, University of Science and Culture, Tehran, Iran)
Morteza Khakzar Barfuei
3
(Industrial Engineering Research Group, Technology Development Institute, ACECR, Tehran, Iran)
Mahmood Shafiee
4
(Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK)
الکلمات المفتاحية: Portfolio selection . Risk analysis Investment . Genetic algorithm . Particle swarm optimization . Project interdependency,
ملخص المقالة :
This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.
