Construction progress optimization scheduling model based on integration of A* algorithm and genetic algorithm

Abstract

In the face of increasingly complex and multi-phase construction projects, traditional scheduling methods struggle to account for uncertainties commonly encountered on construction sites—such as sudden weather changes, delayed material deliveries, and fluctuating labor availability. These unpredictable factors often lead to significant discrepancies between actual progress and the original schedule. To address this challenge, this study proposes a hybrid construction progress optimization scheduling model that combines the A* algorithm with the genetic algorithm (GA), referred to as the A*-GA model. This approach leverages the heuristic search capability of the A* algorithm and the global optimization power of GA. The A*-GA model first employs the A* algorithm to generate an initial construction schedule, prioritizing candidate solutions to identify the most efficient task execution path and ensure rational allocation of time and resources. This preliminary plan is then further refined using GA. Through customized encoding schemes, fitness functions, and genetic operations such as selection, crossover, and mutation, the model iteratively evolves better scheduling solutions. Experimental results demonstrate that the A*-GA model significantly improves project efficiency, reducing the overall construction period by 59 days and cutting costs by 1.501 million yuan. The proposed model proves effective in optimizing both time and cost, offering a more intelligent and adaptive solution for construction progress management in complex engineering environments.