Material Distribution Planning Method and Experimental Verification under Multinode and Multivehicle Scene
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 11
Abstract
In the construction process of construction engineering, there exist a complex construction site layout and multiequipment collaborative work. If a reasonable construction plan cannot be made quickly and accurately, the investment in construction costs will be increased. Aiming at the construction material distribution, this study proposed a construction plan planning method under the multinode and multivehicle scene. Firstly, a mathematical model of the material distribution planning problem in the multinode multivehicle scene was established. The basic assumptions, objective functions, and constraints were proposed. The triangular scalar variables and ‘unitization’ processing methods were incorporated into the mathematical model. In order to solve the problem of material distribution planning, a heuristic neural network algorithm was designed. A ‘gene-chromosome-individual’ representation method and a ‘two-step’ calculation concept were defined. The specific implementation steps of the algorithm were given. Fully considering the type and quantity of vehicles and the length of the path, a method of establishing the mapping relationship between the construction scheme and the construction cost was proposed. The feasibility of the proposed theoretical method was verified by a cable truss structure model experiment. A calculation method with high fitting degree and fast running speed was established. According to the experiment model, the best construction scheme was formed. The results showed that the optimal construction scheme had a high vehicle loading rate and a clear distribution path, which effectively saved the construction cost under the premise of ensuring smooth construction.
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Data Availability Statement
Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (No. 5217082614).
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Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 11 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 31, 2024
Accepted: May 23, 2024
Published online: Aug 29, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 29, 2025
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