Cash Flow Optimization of Portfolio Considering Market Indices Using Genetic Algorithm and Particle Swarm Optimization

One of the main concerns of project based organizations in construction projects is to manage multiple projects in a portfolio simultaneously. Construction Portfolio management determines the best time and condition of starting and selling each project according to the financial need of the stakeholders. This paper aims to optimize the cash flow in construction portfolios considering market indices using meta-heuristic algorithms from the organization’s point of view. In this research, a model was presented to simulate the conditions of the project portfolio and was optimized using genetic algorithm (GA) and particle swarm optimization (PSO). The inflation rate of construction and real estate during recession and expansion was considered to make the situation more real as well. To evaluate the model, a portfolio including five real selected projects in advance by an organization is optimized by the model. The results showed that the model and the optimization algorithms used in the model had an effective role in the arrangement and balancing of the portfolio. Moreover, conditions of the organization such as cash flow and market indices such as project construction inflation and real estate inflation during periods of recession and boom were also considered, which is almost impossible in traditional project portfolio management. Both algorithms came up with the same solution which showed that the result is valid, but the performance of the particle swarm optimization algorithm was better than the genetic algorithm in terms of program execution speed and speed of the final result. In comparison to an expert decision on balancing the portfolio, the algorithms found the solution with 33% better objective function values.