A Hybrid Whale Optimization Algorithm for Flow Shop Scheduling Problems

The permutation flow shop scheduling problem (PFSP) is a non-deterministic polynomial time (NP) hard combinatorial optimization problems and has been widely studied within the scheduling research community. In this paper, a memetic whale optimization algorithm (MWA) is proposed to minimize the makespan for the PFSP. The smallest position value (SPV) rule is used to convert the continuous number to the job permutations for determining the most suitable the proposed MWA for the PFSP. The proposed MWA uses a two Nawaz-Enscore-Ham (NEH)-based heuristic for population initialization. Finally, a simulated annealing based local variable neighborhood search (VNS) used to enhance the quality of the solution and balance exploitation and exploration. Computational results show that the MWA incorporated with the NEHLJP1 for the initialization algorithm can outperform the MGELS and the PSOVNS algorithms in terms of makespan.