An Improved Bat Algorithm for the Hybrid Flowshop Scheduling to Minimize Total Job Completion Time

In this paper, we present improved bat algorithm (BA) to solve hybrid flowshop scheduling (HFS) problem, which is a typi- cal NP-hard combinatorial optimization problem with strong engineering production back- grounds. To make algorithms applicable in the HFS problem, we use smallest position value (SPV) rule to associate particles continuous property to discrete job order, greedy method to compute this job order to complete HFS schedule and rank selection rule for particles local search. Computation has three major outcomes: total iteration required to solve the problem, total computation time needed and total job completion time (JCT). Simulation results based on a variety of instances demonstrate the effectiveness, efficiency, and robust- ness of the algorithms. Comparison with particle swarm optimization (PSO) algorithm de- picts that BA gives better results and stable outcomes