  There have been extensive works dealing with genetic algorithms (GAs) for
seeking optimal solutions of shop scheduling problems. Due to the NP hardness,
the time cost is always heavy. With the development of high performance
computing (HPC) in last decades, the interest has been focused on parallel GAs
for shop scheduling problems. In this paper, we present the state of the art
with respect to the recent works on solving shop scheduling problems using
parallel GAs. It showcases the most representative publications in this field
by the categorization of parallel GAs and analyzes their designs based on the
frameworks.
