Dynamic optimization of scheduling for tunnel construction projects

Jianying Wei

doi:10.1117/12.3034821

7 August 2024 Dynamic optimization of scheduling for tunnel construction projects

Jianying Wei

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132241E (2024) https://doi.org/10.1117/12.3034821
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

The tunnel project as an important part of urban transportation, its intelligent construction management is crucial to the development of the smart city. Tunnel construction scheduling is an important issue, as the construction conditions of tunnels often change due to construction techniques, geological conditions, and so on. Although previous studies have made great contributions to this, most of them usually ignore the changes in construction sequences and resource supplies, and do not establish a complete dynamic optimization model to make dynamic adjustments. In order to cope with the changes of construction conditions, a dynamic optimization model for construction scheduling is constructed by adopting the idea of dynamic planning and combining the W-RBS and S-LSM methods. The scope and resourcing of construction activities were defined and analyzed for logical, temporal, spatial, work continuity and objective constraints between activities, subject to allowable resource changes. The Genetic Algorithm is compiled by python, and its effectiveness is verified by combining with the specific arithmetic example. The results prove that through the model has the capability to optimize the duration for the changing construction conditions and developing a feasible optimization strategy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianying Wei "Dynamic optimization of scheduling for tunnel construction projects", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132241E (7 August 2024); https://doi.org/10.1117/12.3034821

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