Automated Methods and Systems for Construction Planning and Scheduling: Critical Review of Three Decades of Research
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 7
Abstract
Over the last 3 decades, a large body of research focused on automated construction planning and scheduling. Some of these efforts introduced methods to use design information to automatically develop the scope of work, establish work breakdown structures, and create optimal project sequences. Others introduced new techniques to formalize the sequencing relationships among schedule activities and project components. Despite these advancements, most construction projects—if not all—still are engaged fully in manual workflows of planning and scheduling. By offering a critical review of the literature, this manuscript examines the key issues that, to date, have hindered scaling and wide adoption of automated planning methods and systems. A close examination of how knowledge is formalized; scope quantification and project definition methods; and planning, scheduling, and schedule optimization techniques identified the following gaps in knowledge: (1) lack of flexibility in how construction knowledge is stored in existing construction method model templates for sequencing algorithms; (2) the dependency of current automated scheduling methods on manually formed and maintained work templates; (3) lack of learning methods to automate learning of construction knowledge from existing records without extensive human input; (4) limited validation on applicability of existing automated planning systems on real-life construction projects; and (5) the decoupled nature of research on automated planning versus schedule optimization. Building on the recent advancements in deep learning and natural language processing and the rise in adoption of lean construction theories, a discussion is offered on the path for research toward automatic generation of dynamic work templates and their inclusion in integrated planning, scheduling, and optimization systems.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Science Foundation through Grant No. CMMI-1446765. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Volume 147 • Issue 7 • July 2021
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© 2021 American Society of Civil Engineers.
History
Published online: May 8, 2021
Published in print: Jul 1, 2021
Discussion open until: Oct 8, 2021
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