Decision Support NSGA-II Optimization Method for Resource-Constrained Schedule Compression with Allowed Activity Splitting

Abstract

In the course of a construction project, the project manager’s task is to ensure timely and cost-effective execution of the job. However, it is common that delays and over-budgeting to be experienced during the project execution. This schedule acceleration requires resource planning to account for the project’s limited resources. Therefore, this study proposes an integrated method that allows for joint consideration of project scheduling and resource planning while accounting for activity splitting. The objective is to determine the project’s optimal cost and duration while considering some input parameters such as the crew’s size and project’s activities’ cost and duration. The proposed method utilized the Genetic Algorithm (GA) to optimize the project duration and cost. Accordingly, the Weighted Sum was used as a multi-criteria decision support method to choose an optimal solution from the optimization results. The developed scheduling and optimization method is coded in Python as a stand-alone, automated, computerized tool to facilitate its application. A numerical example, utilizing the developed method, is employed to show the method’s robustness and assess its performance against other previously developed methods. Results indicated the developed method’s dominance in finding optimal solutions in a reasonable time avoiding local minima entrapment.