Multi-objective Static-dynamic Scheduling for Dual-tunnel Construction under Spatiotemporal Constraints

Abstract

This study develops a high-fidelity multi-objective static and dynamic scheduling model for dual-tunnel construction. To overcome topological deadlocks in tightly restricted spaces, complemented by a fine-grained time-slice mapping mechanism to eliminate resource fragmentation. At the static level, the optimization aims to minimize both the total make-span and the Weighted Resource Fluctuation Standard Deviation. A Memetic Algorithm-based Hybrid Genetic Algorithm (HGA) is proposed to solve the NP-hard problem. The algorithmic engine is fundamentally upgraded by incorporating an unbiased topological sequence initialization to expand the early exploration space, a dynamic continuity penalty function to ensure intra-cycle operational fluidity, and an elite local search strategy to overcome genetic hardening. Furthermore, the ε-constraint method is utilized to extract the exact Pareto front. An application to a 100-meter dual-tunnel engineering case demonstrates that the proposed HGA possess significant global optimization capabilities, while the rolling-horizon dynamic scheduling exhibits superior computational efficiency. The static optimization reduced the construction duration by 13.3% compared to the actual schedule, while the dynamic optimization achieved a 11.5% reduction under ideal conditions. Furthermore, disturbance simulation experiments confirm that this dynamic scheduling mechanism maintains a linear and stable increase in predicted duration across various disturbance scenarios, demonstrating excellent stability and robustness.