Multi-period project portfolio selection under risk considerations and stochastic income

Journal of Industrial Engineering International

دوره 14، شماره 3، آذر 2018   

نویسندگان

Ali Asghar Tofighian¹؛ Hamid Moezzi²؛ Morteza Khakzar Barfuei³؛ Mahmood Shafiee^{* 4}

¹Industrial Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran

²Industrial Engineering Department, Faculty of Engineering, University of Science and Culture, Tehran, Iran

³Industrial Engineering Research Group, Technology Development Institute, ACECR, Tehran, Iran

⁴Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK

چکیده

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.

کلیدواژه‌ها

Portfolio selection . Risk analysis  Investment . Genetic algorithm . Particle swarm optimization . Project interdependency