Skip to main content

Multi-period Project Portfolio Selection under Risk considerations and Stochastic Income

Tofighian, Ali Asghar, Moezzi, Hamid, Khakzar Barfuei, Morteza, Shafiee, Mahmood (2018) Multi-period Project Portfolio Selection under Risk considerations and Stochastic Income. Journal of Industrial Engineering International, 14 (3). pp. 571-584. ISSN 2251-712X. (doi:10.1007/s40092-017-0242-6) (KAR id:79740)

PDF Publisher pdf
Language: English

Download (1MB) Preview
[thumbnail of Tofighian2018_Article_Multi-periodProjectPortfolioSe.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
PDF Pre-print
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of Proof.pdf]
Official URL


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.

Item Type: Article
DOI/Identification number: 10.1007/s40092-017-0242-6
Uncontrolled keywords: Portfolio selection; Risk analysis; Investment; Genetic algorithm; Particle swarm optimization; Project interdependency
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 23 Jan 2020 19:26 UTC
Last Modified: 16 Feb 2021 14:11 UTC
Resource URI: (The current URI for this page, for reference purposes)
Shafiee, Mahmood:
  • Depositors only (login required):


Downloads per month over past year