As an essential problem in construction management, the resource-constrained project scheduling problem (RCPSP) has been studied for decades; however, an integrated information model that fully supports the RCPSP solving process is still lacking. Though building information modeling (BIM) was proposed to meet the data requirements in the building life cycle, some scheduling and resource information are not considered in information transfers between the information model and the RCPSP mathematical model. This paper presents an integrated approach that enables fluent data flow from the information model to the RCPSP model for construction scheduling. Within this approach, a work package-based information model is proposed to capture all the required data of the RCPSP. Then, a semiautomatic method that integrates multisource data is introduced to form the proposed information model, and an adaptive data transmission method is used to support a designed multimode resource-constrained project scheduling problem (MRCPSP) model. The models and approaches are validated using the data of an actual project, demonstrating the feasibility and efficiency of this approach. This study contributes a novel integrated approach that covers the information requirement and enables fluent data flow in the RCPSP solving process by formalizing a construction information model with a semiautomatic data integration approach. Meanwhile, the work package-based information model is a successful attempt to introduce previously-gained knowledge into automatic schedule generation processes. Future work like extending the information model, creating new methods for RCPSP model generation, and data analytics could bring a new chance to apply more complex and intelligent methods in project scheduling and construction management.
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引用方式: Wang, H.W., Lin, J.R.*, and Zhang, J.P. (2020). "Workpackage-based Information Modeling for Resource-Constrained Scheduling of Construction Projects" Automation in Construction. 109: 102958. doi: 10.1016/j.autcon.2019.102958
This work was funded by the National Key R&D Program of China (No. 2018YFD1100900), the National High-tech R&D Program (863 Program) of China (No. 2013AA041307). Dr. Lin is supported by the Beijing Natural Science Foundation (No. 8194067), the Natural Science Foundation of China (No. 51908323) and the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (No. QNRC2016001).
Accession Number: WOS:000501617300005
IDS Number: JU3ZN