The lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic Q-learning (IDQL) algorithm is proposed, utilizing makespan as feedback. To prevent blind search, a dynamic $\epsilon$-greedy search strategy is introduced. Additionally, the Nawaz-Enscore-Ham (NEH) algorithm is employed to diversify solution sets, enhancing local optimality. Addressing the limitations of the dynamic $\epsilon$-greedy strategy, the Glover operator complements local search efforts. Simulation experiments, comparing the IDQL algorithm with other intelligent algorithms, validate its effectiveness. The performance of the IDQL algorithm surpasses that of its counterparts, as evidenced by the experimental analysis. Overall, the proposed approach offers a promising solution to the complex ELFSP, showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.

Automated Guided Vehicle (AGV) scheduling problem is an emerging research topic in the recent literature. This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs. To reduce the transportation cost of AGVs, this work also proposes an optimization method consisting of the total running distance, total delay time, and machine loss cost of AGVs. A mathematical model is formulated for the problem at hand, along with an improved Discrete Invasive Weed Optimization algorithm (DIWO). In the proposed DIWO algorithm, an insertion-based local search operator is developed to improve the local search ability of the algorithm. A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning. Comprehensive experiments are conducted, and 100 instances from actual factories have proven the effectiveness of the optimization method.

To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.