Abstract

Heavy industrial projects, especially oil refineries, are constructed by modules
prefabricated in factories, transported to sites and installed by mobile cranes. Due to a large number
of lifts on the congested and dynamic site layouts in heavy industrial projects, the lift path planning
has been attention for not only safe and efficient mobile crane operation but also better project
productivity and safety. Although the path planning algorithms have been introduced over the
years, they have not been used actively in practice since the comparison of these algorithms has
not been examined yet based on the realistic mobility of mobile cranes and real site environment.
Therefore, this thesis compares the path planning algorithms including A* search, rapidly
exploring random tree (RRT), genetic algorithms (GA) and 3D visualization-based mathematical
algorithm (3DVMA) under the same site environment in order to find a competent method using
measurement metrics considering collision-free and optimal lift paths with the lower crane
operation cost and less computation time. The proposed comparison is implemented in a case study
that includes a series of modules lifted by a mobile crane on various site conditions. This
comparison shows the advantages and disadvantages of each algorithm for the crane path planning
in heavy industrial projects and suggests the direction of further research in this field.