Flowshop scheduling via bi-objective parallel genetic algorithm

This paper extends the parallel genetic algorithm to solve the bi-objective flowshop scheduling problems with consideration of two criteria, namely makespan, and sum of the earliness and tardiness, simultaneously. In the proposed algorithm, the population will be decomposed into several sub-populations in parallel and each subpopulation is designed for a scalar bi-objective. Multiple objectives are combined with min-max method then each subpopulation evolves separately until to obtain a good approximation of the Pareto-front. After unifying the obtained results till now, we propose a variable neighborhood search algorithm to improve the Pareto-front. The non-dominated sets obtained from our algorithm and genetic local search algorithm are compared. It is found that the proposed algorithm yielded most of the solutions in the net non-dominated front