  In order to solve the limited buffer scheduling problems in flexible flow
shops with setup times, this paper proposes an improved whale optimization
algorithm (IWOA) as a global optimization algorithm. Firstly, this paper
presents a mathematic programming model for limited buffer in flexible flow
shops with setup times, and applies the IWOA algorithm as the global
optimization algorithm. Based on the whale optimization algorithm (WOA), the
improved algorithm uses Levy flight, opposition-based learning strategy and
simulated annealing to expand the search range, enhance the ability for jumping
out of local extremum, and improve the continuous evolution of the algorithm.
To verify the improvement of the proposed algorithm on the optimization ability
of the standard WOA algorithm, the IWOA algorithm is tested by verification
examples of small-scale and large-scale flexible flow shop scheduling problems,
and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale
optimization algorithm (WOA) are used for comparision. Based on the instance
data of bus manufacturer, simulation tests are made on the four algorithms
under variouis of practical evalucation scenarios. The simulation results show
that the IWOA algorithm can better solve this type of limited buffer scheduling
problem in flexible flow shops with setup times compared with the state of the
art algorithms.
