Resource and Sequence Optimization Using Constraint Programming in Construction Projects

  • Kim, Junyoung (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Park, Moonseo (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Ahn, Changbum (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Jung, Minhyuk (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Joo, Seonu (Infra Technology Innovation Group, Samsung Electronics) ;
  • Yoon, Inseok (Department of Architecture and Architectural Engineering, Seoul National University)
  • Published : 2022.06.20

Abstract

Construction projects are large-scale projects that require extensive construction costs and resources. Especially, scheduling is considered as one of the essential issues for project success. However, the schedule and resource management are challenging to conduct in high-tech construction projects including complex design of MEP and architectural finishing which has to be constructed within a limited workspace and duration. In order to deal with such a problem, this study suggests resource and sequence optimization using constraint programming in construction projects. The optimization model consists of two modules. The first module is the data structure of the schedule model, which consists of parameters for optimization such as labor, task, workspace, and the work interference rate. The second module is the optimization module, which is for optimizing resources and sequences based on Constraint Programming (CP) methodology. For model validation, actual data of plumbing works were collected from a construction project using a five-minute rate (FMR) method. By comparing actual data and optimized results, this study shows the possibility of reducing the duration of plumbing works in construction projects. This study shows decreased overall project duration by eliminating work interference by optimizing resources and sequences within limited workspaces.

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Acknowledgement

This research was supported by Samsung Electronics and the BK21 FOUR(Fostering Outstanding Universities for Research) Project in 2022. (No.4120200113771). The authors express their gratitude for the support.