Enhanced Subcontractors Allocation for Apartment Construction Project Applying Conceptual 4D Digital Twin Framework
The problem of optimal allocation of resources in limited circumstances to handle assigned tasks has been dealt with in a wide variety of research fields. Various research methodologies have been proposed to address uncertainties such as waiting and waste in construction projects, but they do not ta...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2c372b053b55431cbee0c56e13cb9b1f |
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| Sumario: | The problem of optimal allocation of resources in limited circumstances to handle assigned tasks has been dealt with in a wide variety of research fields. Various research methodologies have been proposed to address uncertainties such as waiting and waste in construction projects, but they do not take into account the complexity of construction production systems. In this study, a research approach was proposed that simplified the construction production system into a work package to be serviced and a work group to provide services. In addition, a conceptual 4D digital twin framework considering the uncertainty of the construction production system was proposed. This framework includes BIM as an information model and a queuing model as a decision-making model. Through case projects, we have presented how this framework can be used for decision making in several statuses. As a result of the analysis using the performance index of the queuing model, it was possible to monitor the status of the system according to the allocation of resources. In addition, it was possible to confirm the improvement of the performance index according to the additional arrangement of the work group and the activity cycle of the work package. The framework presented in this study helps to quantitatively analyze the state of the system according to the input data based on empirical knowledge, but it has a limitation in that it cannot present an optimized resource allocation solution. Therefore, in future research, it is necessary to consider the grafting of machine learning technology that can provide optimal solutions by solving complex decision-making problems. |
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