Overall Schedule OPTIMIZATION USING GENETIC ALGORITHMS

Multi-objective optimization is getting more developed day by day to support the need of the construction industry, as it allows construction practitioners to have an inclusive solution that can take into consideration multi-aspects. Using genetic algorithms (GA) and goal programming (GP), this research is an attempt toward a more inclusive and wider multi-objective optimization model that can consider different aspects such as profit, time, resource usage, and quality, with different weights for each to aspect to reach a near-optimum solution according to the users' priorities. The model was developed to work with three different construction methods for each activity. The developed model first optimizes each aspect independently, then provides a near-optimum solution considering all aspects together by maximizing profit and quality while minimizing the time and resource fluctuation with respect to the relative importance weights defined in the inputs. The model was applied to a case study where its data were inputted into the model. Several runs were performed first to find the optimum solution considering each aspect individually, then a final run to consider all aspects simultaneously. The results of the multi-optimization run were compared to the results of the individual runs, where variances were realized in the output of the multi-objective optimization from that of the optimum case of each individual aspect to achieve the optimum solutions that consider all of them simultaneously.