Investigation of decision-making processes in construction projects and development of a software program that can manage these processes

In the optimization of construction projects, multi-objective planning problems have generally focused on developing metaheuristic algorithms; however, insufficient attention has been given to developing multi-objective selection and elimination operators that directly affect algorithm performance. In this thesis study, to improve the performance of metaheuristic algorithms, reference-based non-dominated sorting (NDSIII) and strength pareto (SP) operators were integrated with different metaheuristic algorithms, and a modified strengthened Pareto (MSP) operator was developed for four and more objective problems. Using Grey Wolf Optimization, Whale Optimization Algorithm and Rao series for seven different problem classes involving time, cost, quality, environmental impact, and safety risk objectives, higher quality solutions were obtained with significantly fewer function evaluations compared to existing algorithms in the literature. A software with Matlab and MS Project integration was developed to transform theoretical studies into practice, thus providing an innovative solution for project optimization in the construction industry.