A robust multi-objective optimization approach for construction project portfolio by considering sustainability

Document Type : Research Paper


1 Department of Industrial Engineering, Shahed University, Tehran, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran


Macroeconomic investments in recent years has grown dramatically. Since the number of sources are usually less than the number of proposing projects to the organization, project selection and decision-making in this regard is considered as an inevitable issue. Wrong selection, will have negative consequences, such as wasting resources and also eliminate resources which can be properly used in a more appropriate project results in benefits for the organization. Therefore, a method for selecting project portfolio using a mathematical model and focusing on sustainability factors is proposed. In this paper, we present a multi-objective mathematical programming model that is a comprehensive and also a practical model for portfolio selection of construction projects because it uses sustainability criteria to evaluate projects as one of the objective functions. Multi-objective models can also be used to contrast the objectives with each other in project portfolio selection. Other innovations of the proposed model in this paper are multi-period modeling that specifies the precise timing of the selection of selected projects over 10 defined periods. A robust model is then proposed in order to considering the uncertainty, in this paper contains the uncertainty is the duration of the project. The results show that the robust model in terms of mean objective function under different realizations performs better than the deterministic model and may be because the robust model unlike the deterministic model considers the uncertainties caused by the disturbances.


Main Subjects

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Volume 14, Issue 1 - Serial Number 1
January 2021
Pages 163-186
  • Receive Date: 26 August 2021
  • Revise Date: 11 September 2021
  • Accept Date: 29 September 2021
  • First Publish Date: 29 September 2021