Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 1
Abstract
Modular construction offers several advantages, such as safe working conditions, fast construction, and high-quality end products. The project scheduling of modular construction should consider the time involved in the factory production, transportation, and erection processes of the units given limited resources (e.g., the number of modular units, available transportation, and workers) and project restrictions (e.g., the sequences of manufacturing production and on-site installation). However, current modular construction project scheduling techniques and previous academic approaches cannot deal with the overall project processes and production/on-site installation sequences simultaneously. This is one reason few mass-production multiple projects exist in the manufacturing industry. Moreover, it can lead to inappropriate project management guidelines and fail to minimize the overall project duration. This paper suggests a model for modular construction projects scheduling using a genetic algorithm. The results show that implementing both overall project processes and project requirements is an efficient way to develop a modular construction project schedule. The proposed optimization tool will help project managers allocate resources efficiently and reduce the completion time of multiple projects.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was supported by a Grant No. 18RERP-B082884-05 from the Housing Environment Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
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©2018 American Society of Civil Engineers.
History
Received: Feb 22, 2018
Accepted: Jul 12, 2018
Published online: Oct 31, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 31, 2019
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