Increasing the Accuracy of Credit Risk Assessment of Contractors' Control System Using Fuzzy Logic Concepts and Multi-Objective Optimization Algorithm

Abstract

The credit risk of construction contractors is considered as a risk which arises from neglecting the contractor of the contracting party. This risk stems from the fact that the contractor of the contracting party cannot or will not fulfil the contract’s obligations. The impact of this risk is measured by the financial and temporal burden caused by the contract of the construction contractor. Accurately measuring the credit risk of the contractors is considered as one of the most important factors for the survival and continuing the activities of construction contractor companies. This study aimed to introduce an expert model based on using fuzzy logic concepts and genetic optimization algorithm to challenge the credit risk assessment of contractors in the construction engineering contractors. The opinions of specialized managers at construction contractor companies were used after simulation to analyze the proposed genetic fuzzy system and the rate of error measurement of the credit risk of construction contractors was estimated by the fuzzy genetic system compared to other models and hierarchical analysis of the opinion of experts were calculated and compared in the construction area. The opinions of human factor were used to measure the credit risk of construction contractors, leading to a reduction in the accuracy of this risk assessment. There is a need for an optimal system for measuring credit risk due to the uncertain nature of each contractor's characteristics to measure the credit risk using the characteristics of each construction contractor at any time. The results indicated the success of the genetic fuzzy system compared to other models.