  Flow Shop Scheduling (FSS) has been widely researched due to its application
in many types of fields, while the human participant brings great challenges to
this problem. Manpower scheduling captures attention for assigning workers with
diverse proficiency to the appropriate stages, which is of great significance
to production efficiency.
  In this paper, we present a novel algorithm called Self-encoding Barnacle
Mating Optimizer (SBMO), which solves the FSS problem considering worker
proficiency, defined as a new problem, Flow Shop Manpower Scheduling Problem
(FSMSP). The highlight of the SBMO algorithm is the combination with the
encoding method, crossover and mutation operators. Moreover, in order to solve
the local optimum problem, we design a neighborhood search scheme. Finally, the
extensive comparison simulations are conducted to demonstrate the superiority
of the proposed SBMO. The results indicate the effectiveness of SBMO in
approximate ratio, powerful stability, and execution time, compared with the
classic and popular counterparts.
