Construction Project Scheduling Optimization with Time-Cost Trade-Off Based on Genetic Algorithm in Python

Construction project delays frequently occur due to discrepancies between planned and actual implementation schedules under field conditions, where time and cost factors represent two primary obstacles that must be optimally managed. This study aims to develop a construction project scheduling optimization model based on Genetic Algorithms using Python programming to provide effective, efficient, and measurable solutions that achieve a balance between implementation time and project costs. The research method involves developing a construction project scheduling optimization model that considers the time-cost trade-off using a Python-based genetic algorithm, with a case study of the Sei Baru 1 Water Bridge Replacement Project in Belitung Regency. Data were obtained from PT. Billiton Hero Sukses Cemerlang in the form of wage and material prices, AHSP (Unit Price Analysis), RAB (Budget Cost Plan), as well as information onA1 duration and acceleration costs through interviews. The research results demonstrate that the implementation of the NetworkX-CPM and Genetic Algorithm hybrid effectively optimizes construction project scheduling, achieving a cost reduction of 3.49% and duration reduction of 34.82% compared to normal conditions. The optimal GA parameters for this case were: population 50, generation 100, tournament 5, crossover 0.7, and mutation 0.2. The resource leveling process successfully balanced the distribution of daily labor requirements. All optimization results were validated through manual CPM calculations, remained free from constraint violations, and were supported by data visualization and automation integrated with Excel and Microsoft Project.