Faghihi, V.; Reinschmidt, K.; and Kang, J. (2018) "Extended Genetic Algorithm for Optimized BIM-based Construction Scheduling"
Extended Genetic Algorithm for Optimized BIM-based Construction Scheduling
Faghihi, V.PhD Student in Construction Engineering and Management, Texas A&M University; Reinschmidt, K.; and Kang, J.
Construction project scheduling is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry. Construction schedules allow project managers to track and manage the time, cost, and quality (i.e. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners’ knowledge of work packages, on-the-job-experience, planning capability, and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a three-dimensional (3D) representation of a project (a.k.a. Building Information Model or BIM) has recently drawn the attention of the construction industry.
In this paper, the authors demonstrate how to develop and extend the usage of the Genetic Algorithm (GA) not only to generate construction schedules, but to optimize the outcome for different objectives (i.e. cost, time, and job-site movements). The basis for the GA calculations is the embedded data available in BIM of the project that should be provided as an input to the algorithm. By reading through the geometry information in the 3D model, the proposed algorithm gets more specific information about the project and resources from the user and results in different construction schedules. The output 4D animations and schedule wellness scores will help the user to find the most suitable construction schedule for the given project .
Keywords: Construction Project Schedule, Building Information Model, Genetic Algorithm, Optimization