Differential evolution algorithm for hybrid flow-shop scheduling problems

Aiming at the hybrid flow-shop (HFS) scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution (DE) is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.