Dynamic Optimization of Tunnel Construction Scheduling in a Reverse Construction Scenario

Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team allocation. Minimizing the project duration is a primary objective in tunnel construction scheduling optimization. To optimize tunnel construction, we propose a duration-shortening method using additional working surfaces, adaptable to multi-segment and multi-team scenarios. A dynamic optimization model is developed for tunnel construction scheduling, integrating LSM, soft logic, Work Breakdown Structure (WBS), and Resource Breakdown Structure (RBS) within a dynamic scheduling framework. This model analyzes logical relationships, work continuity, temporal and spatial constraints, and resource variation, focusing on reverse construction. The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. Both algorithms perform well, with the GA excelling at handling complex constraints. Case studies confirm the method’s effectiveness in optimizing durations, devising flexible schedules, and improving efficiency and practicality. This research provides both theoretical insights and practical guidance for tunnel construction scheduling optimization in railway engineering.