Yılmaz, Mehmet; Dede, Tayfun; Rao, Ravipudi Venkata

This study aims to investigate the relationship between time, cost, quality, and environmental impact in construction projects using arithmetic optimization algorithm. Project managers have prioritized time and cost concepts in traditional project planning. It is seen that other factors affecting the success of the project are ignored. However, with the impact of technological and sociocultural changes, awareness of sustainability and environmental factors has increased. For this reason, there are studies showing that environmental impact is taken into account at least as much as time, cost, and quality in project management. Therefore, this study evaluates the performance of the arithmetic optimization algorithm (AOA) on the many-objective time–cost–quality–environmental impact problem. The performance of the AOA was compared with the adaptive selection slime mold algorithm (ASSMA), which is known to give good results in the literature. The results show that the AOA outperforms the ASSMA in terms of cost, environmental impact, and quality with 25% less function evaluation.

Yılmaz, Mehmet; Dede, Tayfun

Multi-objective optimization problems are an important research area widely used to solve complex and competitive problems in various industries. In the construction sector, time, cost, and quality factors hold critical importance for the success of projects. Minimizing time, managing costs, and enhancing quality are essential elements for achieving success in many application areas. grey wolf optimization (GWO) is a natural search algorithm that has been effectively utilized in optimization problems in recent years. GWO offers an optimization approach that mimics the interaction between leader wolves, sub-leader wolves, and hunting wolves. The aim of this study is to enhance the performance of GWO algorithm in solving multi-objective construction time–cost–quality problems by integrating archive and grid mechanisms. The archive serves as a data structure to preserve diversity within the solution population and store better solutions. On the other hand, the grid mechanism aims to improve the search process by dividing the solution space. This study evaluates the performance of the GWO algorithm by addressing multi-objective construction time–cost–quality problems with 7 and 13 activities. The results obtained demonstrate that GWO algorithm outperforms genetic algorithm (NSGA-II). These findings highlight the impact and advantages of the GWO algorithm in solving multi-objective construction time–cost–quality optimization problems.