Minimizing durations in repetitive projects through adaptive large neighborhood search

Abstract

This study proposes two integer linear programming models based on line of balance, which takes the makespan as the optimization objective and characterizes various construction scenarios that may exist in different types of repetitive projects. The models are easy to solve, convenient for on-site use, and provide a solid foundation for exploring theoretical optimal solutions. Further, a novel matheuristic algorithm that integrates adaptive large neighborhood search with exact algorithms is proposed. Based on two different types of repetitive projects, the practicality of the model and the effectiveness of the algorithm are verified in ten different construction scenarios, and managerial insights are provided. A comparison with Gurobi’s results shows that in small-scale case scenarios, the matheuristic algorithm achieves solutions of the same quality with a shorter running time. In large-scale scenarios, the matheuristic algorithm outperforms Gurobi in terms of both solution quality and computational efficiency.