Hybrid Multi-population Genetic Algorithm for Multi Criteria Project Selection

Document Type : Research Paper


1 Department of Industrial Engineering, Meybod University, Meybod, Iran

2 Department of Computer Engineering, Meybod University, Meybod, Iran


Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available project portfolio. Project selection process has a significant role in the success. Here the main problem is what projects must be selected and how manage simultaneous projects. Used approach to answer these questions must be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. The complexity of this problem increases as the number of projects and the number of objectives increase. Therefore, in this research we aim to present a heuristic based on genetic and simulates annealing to select and prioritize available projects based on economic and non-economic criteria. Considered issues are benefit, credit and risk (technical and financial). Presented method starts from multi population of generated solutions and moves toward the final solution. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of projects. Experimental results demonstrate that this method can be used for all kinds of projects basket.


[1] Afshar, M. Shahhosseini, V. Sabet, M. A genetic algorithm for subcontractors selection and allocation in multiple building projects, Soft computing, 25 (2021), pp. 11637{11652.
[2] Blum, C., Andrea, R. (2003). Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys, 35(3), pp. 268{308.
[3] Brester, C., Ryzhikov, I., Semenkin E., Multi-objective optimization algorithms with the island metaheuristic for e ective project management problem solving, Organizacija, 50 (4) (2017), pp. 364-373.
[4] Carazo, A.F., Gomez, T., Molina, J., Hernandez-Daz, A.J., Guerrero, F.M., Caballero, R. Solving a comprehensive model for multi-objective project portfolio selection, Comput Oper Res, 37 (4) (2010), pp. 630-639.
[5] Davoudabadi, R., Mousavi, S. M., Saparauskas, J., Gitinavard, H. (2019). Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach. Journal of Civil Engineering and Management, 25(3), 241-251.
[6] El-Ghazali, Talbi, (2009). Meta-heuristics, from design to implementation, published by John Wiley and Sons, Inc., Hoboken, New Jersey.
[7] Esfahani, H.N., hossein Sobhiyah, M., Youse , V.R., Project portfolio selection via harmony search algorithm and modern portfolio theory, Procedia-Soc Behav Sci, 226 (2016), pp. 51-58.
[8] Fernandez, E., Gomez, C., Rivera, G., Cruz-Reyes L., Hybrid metaheuristic approach for handling many objectives and decisions on partial support in project portfolio optimization, Inf Sci, 315 (2015), pp. 102-122.
[9] Kumar, M., Mittal, M., Soni, G., Joshi, D., A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects, Comput Indus Eng, 119 (2018), pp. 121-130.
[10] Kumar, M., Mittal, M., Soni, G., Joshi, D. Selection and Scheduling of Interdependent Projects using a Modi ed Genetic Algorithm, f the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, (2019).
[11] Mohagheghi, V., Mousavi, M., Antucheviciene, J., Mojtahedi, M., (2019). Project portfolio selection problem: a review of model, uncertainty approaches, solution techniques and case studies, Technological and Economic Development of Economy, 25(6), pp. 1380-1412.
[12] Nikkhahnasaba, M., Najaf, A., Project Portfolio Selection with the Maximization of Net Present Value, Journal of Optimization in Industrial Engineering 12 (2013), pp. 85-92.
[13] Nowak, M., Project Portfolio Selection Using Interactive Approach, Procedia Engineering, 57, (2013), PP. 814-822.
[14] Osman, I.H., Potts, C.N. (1989). Simulated annealing for permutation ow-shop scheduling. OMEGA, The International Journal of Management Science, 17, pp. 551{557.
[15] Panadero, J., Doering, J., Kizys, R., Juan, A.A., Fito, A., A variable neighborhood search simheuristic for project portfolio selection under uncertainty, J Heuristics (2018), pp. 1-23.
[16] To ghian, A.A., Moezzi, H., Barfuei, M.K., Sha ee, M., Multi-period project portfolio selection under risk considerations and stochastic income, J Indus Eng Int, 14 (3) (2018),pp. 571-584.
  • Receive Date: 17 December 2021
  • Revise Date: 02 March 2022
  • Accept Date: 10 March 2022
  • First Publish Date: 08 April 2022