The flexible job shop scheduling problem with sequence-dependent setup times (FJSP-SDST) is a critical issue in intelligent manufacturing industries. Existing research on FJSP-SDST typically assumes that setup times are autonomously managed by machines, which limits its applicability. This paper addresses this gap by exploring the resource-constrained FJSP-SDST (RFJSP-SDST), which considers setup times that are conducted by external resources, such as robots or humans.  As an NP-hard problem, it consists of four subproblems: operation sequencing, machine selection and setup task assignment, and resource allocation, making it challenging to solve efficiently. To tackle this complexity, this paper proposes an imitation learning and constraint programming-assisted evolutionary algorithm (ILCPEA) to effectively solve the RFJSP-SDST, focusing on minimizing the makespan. The ILCPEA incorporates a hybrid decoding strategy with combining basic, matheuristic, and imitation learning-assisted methods, which ensures diversity, optimal resource allocation, and a balance between computational resources and performance during the evolution process. To enhance the local search effectively, the disjunctive graph model is used to identify critical paths and four neighborhood structures are designed to improve algorithm convergence. Additionally, a CP-based mathematical evolution operator is introduced to explore the full solution space. Experimental results demonstrate that ILCPEA efficiently generates competitive solutions, outperforms other existing advanced algorithms and demonstrates its practicality in addressing real workshop problems. 

With the popularization of industrial intelligence, Automated Guided Vehicles (AGVs) have gradually become an efficient means of transportation in manufacturing workshops. Previous studies on this issue mainly considered the transportation cost of AGVs, while ignoring the optimization of customer satisfaction. This paper studies the AGV scheduling problem with time and capacity constraints for material handling in an intelligent manufacturing workshop. To better reflect real production conditions and simultaneously minimize AGV carbon emissions while maximizing customer satisfaction, a Mixed-Integer Linear Programming (MILP) model is developed. A Multi-objective Discrete Artificial Bee Colony algorithm (MDABC) is proposed, which employs an adaptive selection strategy to ensure that different neighborhoods of solutions are fully explored. The reference search strategy is introduced to carry out in-depth search according to the effective information carried by high quality solutions. In addition, in order to avoid the algorithm falling into local optimality, a high-quality generation strategy is proposed. Comprehensive comparisons with state-of-the-art algorithms and statistical analyses demonstrate that the proposed MDABC achieves superior performance.

The distributed permutation flowshop scheduling problem (DPFSP) has received increasing attention in recent years, which always assumes that the machine can process without restrictions. However, in practical production, machine preventive maintenance is required to prevent machine breakdowns. Therefore, this paper studies the DPFSP with preventive maintenance (PM/DPFSP) aiming at minimizing the total flowtime. For solving the problem, a discrete gray wolf optimization algorithm with restart mechanism (DGWO_RM) is proposed. In the initialization phase, a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution. Next, four local search strategies are proposed for further enhancing the exploitation capability. Furthermore, a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution, thereby ensuring a broader exploration of potential solutions. Finally, comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM. The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.

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.