Automated look-ahead schedule generation and optimization for the finishing phase of complex construction projects

In the project this dissertation describes, I have developed an integrated approach to quickly generate accurate and close-to-optimum LASs in the finishing phase of complex construction projects. In the finishing phase of such projects, project planners, site engineers, and construction engineers struggle to use look-ahead schedules (LASs) to effectively organize and allocate limited project resources such as crews and spaces on a daily basis for three main reasons. First, the LASs created are error-prone because site engineers and project planners need to consider constraints including precedence constraints, spatial and crew availabilities, and engineering constraints, such as zone and blocking constraints; second, the LAS generation process is time-consuming, even with the help of the existing commercial tools; and third, there is no way to tell whether the LASs created are the best means by which to achieve specific project goals, such as shortest construction duration and lowest construction cost. The approach I have developed builds on two theoretical foundations: automated schedule generation and project schedule optimization. This approach consists of an automated LAS generation (ALASG) method and an optimization method based on a genetic algorithm (GA). The ALASG method can quickly generate one accurate LAS. The ALASG method is composed of an information model that integrates the project data sources at the appropriate level of detail to facilitate the formation of operations and the accommodation of constraints, and an LAS generation process model that sequences operations without violating any constraints. The GA-based optimization method interacts with the ALASG method to quickly discover nearly optimum LASs. I have also developed a prototype based on this approach. The results from the use of this prototype in student and engineer design charrettes and from comparison studies provide evidence for the power of this approach in rapidly generating accurate and close-to-optimum LASs. Because of this unique capability, I claim the ALASG method as a contribution to the field of automated schedule generation and the GA-based method as a contribution to the field of project schedule optimization. This research lays the foundation for tools that can guide project planners, site engineers, and construction managers to effectively and efficiently conduct work assignments by (1) eliminating work conflict and rework, (2) always looking towards optimum project goals, and (3) quickly adjusting project actions according to the most up-to-date project status.