Multi-objective Genetic Algorithm for the Time, Cost, and Quality Trade-Off Analysis in Construction Projects

Project management methods and practice address project success, and the well-known “iron triangle” targeting time, cost and quality trade-off has a great importance in this process. Quality optimization, including safety and sustainability, plays a key role in construction project management choices. Since the relationship between quality, time and cost can be different from case to case, an application of artificial intelligence (AI) has been proposed for this purpose. The objective of the research work under this paper is to demonstrate that AI applications can help project managers the trade-off between time, cost and quality objectives. A comprehensive approach concerning three estimates of time, cost and quality of project activities is proposed to optimize project performance in construction. The proposed approach implements a genetic algorithm to optimize project performances, with the aim of creating a decision support system for construction project managers. Genetic Algorithm is an AI application that creates a learning optimization process that discard worse solutions and re-introduce better solutions to search for an optimal or sub-optimal solution. Therefore, time, cost and quality trade-off can be performed by a Multi-Objective Genetic Algorithm that evaluates the effectiveness of various combinations, selecting better solutions with an iterative process. Therefore, the most suitable balancing between three project targets can be achieved. A simple case study of a deep renovation project of two residential is presented to evaluate the proposed approach with a sample application. This study contributes to the understanding of AI applications for construction management.