Providing Optimal Decision-Making Function for Efficient Selection of A Contractor Using PSO Algorithm in MAPNA

Document Type : Research Paper


1 Management Group, Faculty of Literature, Islamic Azad University of Kerman, Kerman, Iran

2 Management Group,Faculty of Literature, Islamic Azad University of Kerman, Kerman, Iran

3 Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran


Careful decision-making to select contractors to manage a project is one of the most important factors in the success and efficiency of a project. It is such that extracting a decision function from the set of factors influencing the selection of contractors can play an important role in improving project performance; therefore, providing a decision function is the main goal of this research. In this paper, the decision model is estimated in the form of linear and exponential equations using the Particle Swarm Optimization Algorithm (PSO) technique. The superior function is selected through the selection of the best estimation model based on the selection criteria of the competing function. Then, it is attempted to predict the values of the decision function. In this paper, for the optimal decision making function, a function of contractors' technical capability, contractors' behavioral capability, company capacity and facilities and project outsourcing goals are considered. The research results show that the linear function explains the efficient selection of a contractor with higher accuracy. The optimal decision-making function shows that MAPNA managers place more emphasis on MAPNA's resources, facilities, and capabilities to efficiently select a contractor. It can also be predicted that in future selections, managers will focus on the ability of MAPNA to cooperate with the contractor, and based on this, MAPNA group subsidiaries will be given priority in the selection.


Volume 11, Issue 2
December 2020
Pages 29-38
  • Receive Date: 06 November 2019
  • Revise Date: 19 February 2020
  • Accept Date: 15 May 2020