Solving the flow shop problem with limited buffers using differential evolution

This paper aims to minimize makspan for the flow shop scheduling problem with intermediate buffers using a discrete differential evolution (DDE) algorithm. In the algorithm, we apply job-permutation-based mutation and crossover operators to generate new candidate solutions, and employ an NEH-based initialization method to produce an initial population. Computational simulations and comparisons show that the proposed DDE algorithm generates better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization in terms of solution quality and robustness.