Containerization has revolutionized software deployment, with Docker leading the way due to its ease of use and consistent runtime environment. As Docker usage grows, optimizing Dockerfile performance, particularly by reducing rebuild time, has become essential for maintaining efficient CI/CD pipelines. However, existing optimization approaches primarily address single builds without considering the recurring rebuild costs associated with modifications and evolution, limiting long-term efficiency gains. To bridge this gap, we present Doctor, a method for improving Dockerfile build efficiency through instruction re-ordering that addresses key challenges: identifying instruction dependencies, predicting future modifications, ensuring behavioral equivalence, and managing the optimization’s computational complexity. We developed a comprehensive dependency taxonomy based on Dockerfile syntax and a historical modification analysis to prioritize frequently modified instructions. Using a weighted topological sorting algorithm, Doctor optimizes instruction order to reduce future rebuild time while preserving functionality. Experimental results on 1,000 popular GitHub repositories demonstrate that Doctor improves 88.66% of Dockerfiles, achieving an average 24.5% reduction in rebuild time, with 14.39% of files experiencing over 50% reduction, all while preserving functional equivalence in 86.2% of cases. These findings highlight best practices for Dockerfile management, enabling developers to enhance Docker efficiency through informed optimization strategies.