An Iterative Greedy Algorithm With $Q$-Learning Mechanism for the Multiobjective Distributed No-Idle Permutation Flowshop Scheduling

The distributed no-idle permutation flowshop scheduling problem (DNIPFSP) has widely existed in various manufacturing systems. The makespan and total tardiness are optimized simultaneously considering the variety of scales of the problems with introducing an improved iterative greedy (IIG) algorithm. The variable neighborhood descent (VND) algorithm is applied to the local search method of the iterative greedy algorithm. Two perturbation operators based on the critical factory are proposed as the neighborhood structure of VND. In the destruction phase, the scale of the destruction varies with the size of the problem. An insertion operator-based perturbation strategy sorts the undeleted jobs after the destruction phase. The $Q$ -learning mechanism for selecting the weighting coefficients is introduced to obtain a relatively small objective value. Finally, the proposed algorithm is tested on a benchmark suite and compared with other existing algorithms. The experiments show that the IIG algorithm obtained more satisfactory results.