  This paper first presents a parallel solution for the Flowshop Scheduling
Problem in parallel environment, and then proposes a novel load balancing
strategy. The proposed Proportional Fairness Strategy (PFS) takes computational
performance of computing process sets into account, and assigns additional load
to computing nodes proportionally to their evaluated performance. In order to
efficiently utilize the power of parallel resource, we also discuss the data
structure used in communications among computational nodes and design an
optimized data transfer strategy. This data transfer strategy combined with the
proposed load balancing strategy have been implemented and tested on a super
computer consisted of 86 CPUs using MPI as the middleware. The results show
that the proposed PFS achieves better performance in terms of computing time
than the existing Adaptive Contracting Within Neighborhood Strategy. We also
show that the combination of both the Proportional Fairness Strategy and the
proposed data transferring strategy achieves additional 13~15% improvement in
efficiency of parallelism.
