Construction Schedule Optimization Based on Genetic Algorithm

DOI

10.2991/978-94-6463-447-1_15How to use a DOI?

Keywords

Genetic algorithm; Construction work; Digitize; Project Progress; Optimization solutions

Abstract

With the continuous growth of the global economy and the rise of information technology, construction progress has become the focus of digital analysis in the construction industry. This paper studies the optimization of digital technology in construction progress, constructs a schedule-cost optimization model, and uses genetic algorithm to assist digital technology to analyze progress optimization. Taking an apartment project as an example, the genetic algorithm is written based on Python software, and the optimized construction schedule is compiled after comprehensive analysis, which shortens the duration and reduces the cost. The full integration of big data technology and computer technology improves the calculation efficiency, realizes the intelligent management optimization of the system, provides intelligent analysis for the project progress management, sets the project progress scientifically, and provides ideas and methods for the project progress management.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Binghui Zu
AU  - Xiang Liu
PY  - 2024
DA  - 2024/07/14
TI  - Construction Schedule Optimization Based on Genetic Algorithm
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 122
EP  - 132
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_15
DO  - 10.2991/978-94-6463-447-1_15
ID  - Zu2024
ER  -