| dc.description.abstract | Construction project scheduling is one of the most important tools for project
managers in the Architecture, Engineering, and Construction (AEC) industry. The
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 dissertation, the author demonstrates 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 and receiving more specific information about the project
and its resources from the user, the algorithm generates different construction schedules.
The output Pareto Frontier graphs, 4D animations, and schedule wellness scores will
help the user to find the most suitable construction schedule for the given project. | en |
