Automated construction schedule optimization using Genetic Algorithm

Abstract Construction project management is a complex process that involves numerous resources and activities that challenges the scheduling of each activity. Effective scheduling is essential for the success of any construction project, but resource allocation conflicts pose a significant challenge for construction managers. This paper proposes a Genetic Algorithm (GA) based model to optimize construction schedules, considering construction resource constraints such as construction activity, construction site, labour, machine, and work timing for labour and machines. The random schedule created with many constraints and parameters will have conflicts in their schedule and cannot be used directly. In this paper, the genetic algorithm uses selection, mutation, and crossover processes to create a new conflict-free schedule until the desired fitness level is reached or the maximum number of iterations is completed. The proposed model is implemented in Python, and the conflict-free schedule is printed as a result. The novelty of this paper is the attempt to apply the job shop scheduling technique for day-to-day construction schedules without conflict using GA. This algorithm can be adapted to optimize the construction schedule for any project and can include additional factors that impact the construction schedule. Overall, this paper provides a valuable contribution to construction project management by presenting an effective optimization model for scheduling construction projects.