The collaborative organization of production scheduling and machine maintenance is crucial for achieving effective manufacturing. However, since these two activities are typically managed by different self-interested managers with distinct optimization needs in practice, their collaboration faces the challenge of simultaneous consideration and balanced optimization of both parties’ interests. To this end, this study investigates a novel game theory-based production-maintenance collaborative scheduling problem, wherein the typical objectives individually concerned by two activities are explicitly considered and the decision interactions between activities are modeled as a non-cooperative stochastic game. Nash equilibrium within the game model provides the ideal solution for balancing the interests of both parties. To solve this problem, an imitation-enhanced alternating-training reinforcement learning method is presented. In this method, an imitation learning-based initialization mechanism is designed to accelerate the training of two game agents, and an alternating training mechanism are developed to facilitate two agents efficiently learning the optimal responses to each other’s behaviors and ultimately achieving the decisions that converge to Nash equilibrium. The superiority of the proposed method is verified through comprehensive experiments.

Driven by the rapid progress of digital technology, manufacturing cells of the smart shop floor have achieved the ability to collect data, compute, reason, and make autonomous decisions. By utilizing such cell intelligence, production schedules can be flexibly adjusted and production performance can be improved. Against this backdrop, this study proposes a novel distributed scheduling method based on data-augmented QMIX for smart shop floors to fully exploit the potential of cell intelligence. In the proposed method, QMIX is adopted to construct the scheduling agents for manufacturing cells and collaboratively optimize the decisions of agents. Furthermore, a data augmentation mechanism based on denoising diffusion probabilistic model is developed to improve the training efficiency of the QMIX algorithm. Finally, experiments conducted on the semiconductor production shop floor MiniFab verified the performance of the proposed method.

The precise and prompt estimation of remaining useful life (RUL) for equipment under diverse operating conditions can assist in proactive equipment maintenance and prevent failures that may lead to financial loss and casualties. This article proposes a novel task-incremental RUL prediction method based on Wasserstein GAN with gradient penalty and Knowledge Distillation (WGAN-KD) to achieve high-precision and rapid prediction. WGAN-KD develops a dual old task retention model to ensure the retention of old tasks while facilitating the acquisition of new ones. To evaluate the performance of WGAN-KD, several experiments were conducted on rolling bearings under diverse operating conditions. The experimental results demonstrated that WGAN-KD outperforms the compared incremental learning methods in accuracy under different operating conditions. Furthermore, it maintains high-precision prediction while enhancing training efficiency compared to batch learning methods.

Production scheduling and logistics scheduling are vital means for organizing manufacturing activities in flexible job shops. Given the intricate coupling relationship between them, corresponding collaborative scheduling becomes urgent need and challenging. Meanwhile, the actual manufacturing process is inevitably affected by disturbances, necessitating the consideration of dynamic environments. To this end, this study investigates a new production-logistics collaborative scheduling problem in dynamic flexible job shops (PLCSP-DFJS). The high-frequency disturbance of new job arrivals is incorporated into the PLCSP-DFJS, and two objectives, namely makespan and total logistics cost, are optimized. A multi-objective deep reinforcement learning (MODRL) method is presented to solve PLCSP-DFJS. In MODRL, a weight-decomposition and neighborhood-inheritance training mechanism is devised to obtain the near-optimal Pareto front, and a dual-level channel-driven framework capable of achieving decentralized decision-making of production and logistics is designed. The performance of MODRL is verified through experiments conducted in an aviation component production shop.

As a promising method for organizing remanufacturing production activities, remanufacturing scheduling has attracted increasing attention in recent years. However, extant studies have primarily focused on solving remanufacturing scheduling problems in a deterministic environment, while neglecting the impact of uncertainties on remanufacturing. Therefore, a new scenario-based robust remanufacturing scheduling problem was investigated in this study, and a robust optimization model for this problem was established. In the proposed model, a discrete scenario set is used to describe the uncertain arrival time and uncertain processing time of end-of-life products, and the variable start-up batch size constraint is considered to improve the practicality and flexibility of the model. To solve this model, an improved biogeography-based optimization algorithm with a new three-dimensional unequal-length representation scheme is proposed, in which, new migration and mutation operators, a local search strategy, and a new batch promotion mechanism are designed to improve the algorithmic performance. The results of the experiments demonstrate the feasibility of the proposed model and the superiority of the presented algorithm in solving the proposed model.