Multi-equipment collaborative optimization scheduling for intelligent construction scene
Year: 2024
Type: article
Abstract: How to realize the efficient scheduling of construction equipment and ensure the construction quality is the key problem that restricts the development of intelligent construction technology. This paper proposes a multi-equipment collaborative optimization scheduling method for intelligent construction scene. Firstly, a logical model of intelligent construction scene is proposed, and the characteristics and requirements of construction in intelligent construction scene are clarified. Considering the relationship between construction processes and the control requirements of construction quality, an intelligent planning model of multi-equipment collaborative scheduling scheme is established. Aiming at the problem of equipment scheduling analysis, an improved non-dominant classification genetic algorithm (NSGA-II) is proposed. According to the solution results of the improved NSGA-II, the data mapping relationship between the scheduling scheme and the construction completion time and construction energy consumption is established. The verification and application of the proposed method are carried out by a cable truss structure experimental model. • A logical model of intelligent construction scene is proposed, which clarifies the relationship between components, processes and equipment. • Considering the relationship between construction processes and the control requirements of construction quality, an intelligent planning model of multi-equipment collaborative scheduling scheme is established. • A systematic multi-equipment collaborative optimization scheduling calculation method is proposed, and the corresponding calculation program is designed. • A complete experimental model is designed to verify the proposed method and provide a reference for engineering practice.
Source: Automation in Construction
Authors: Zhansheng Liu, Guoliang Shi, Dechun Lu, Xiuli Du, Qingwen Zhang
Institution: Beijing University of Technology
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,850
OpenAlex ID: W4402732673