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 have 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 in this chapter 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 (GA) 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 discards worse solutions and re-introduces 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 balance between the 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.