Hybrid Multi-population Genetic Algorithm for Multi Criteria Project Selection

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.

کلیدواژه ها:

Project Selection ، Genetic Algorithm ، Multi Criteria ، Genetic Operators ، Meta-Heuristic