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.

This paper aims to predict future optimal control inputs for unknown continuous-time linear systems. In contrast to most existing approaches that generate control input without observation loss, the proposed control scheme analyzes causality between observation and sequence feedback from the time-delay series model, allowing for the existence of observation loss. These time-delay series can be viewed as multiplayer games over a temporal scale, then a temporal game-theoretic approach ensures system stability and performance. By integrating the Bellman principle, a data-driven adaptive dynamic programming algorithm is proposed to avoid system knowledge. Furthermore, the designed parallel data-driven predictive algorithm reduces the computational complexity. Finally, the applicability and effectiveness of the methodology are demonstrated through numerical simulations and practical experiments. Note to Practitioners—This paper mainly concerns the predictive control of unknown continuous systems, which suffer from unknown dynamics and state observation loss. The proposed methods are suitable for weak information feedback and dynamically changing scenarios, such as autonomous driving in low visibility scenarios and endoscopic surgical robots. Most of the current processing methods are model-driven, which makes them unsuitable for unknown system dynamics changes. To address this issue, we propose a time-delay switched strategy for control prediction with stability and optimality guarantees. The practical application can be divided into three parts: i) Data collection: Collect historical multi-intervals accumulated inputs and outputs data, the amount of data should reach the requirement of the full rank of the data matrix; ii) Iterative learning: the optimal control strategy is learned from the historical data through an adaptive dynamic programming method; iii) Deployment: Control intervals are extended through temporal time-delay feedback on historical state trajectory, and length trigger conditions will switch these feedbacks logically for actuators. Finally, the Quanser QBot 2e robot is used as a demonstration example.

This paper addresses the multi-objective distributed permutation flowshop scheduling problem with sequence-dependent setup times (MODPFSP_SDST), whose optimization objectives are the makespan, the total energy consumption and the noise emission. An effective hybrid variable neighborhood search (HVNS) algorithm is proposed for solving this problem. HVNS utilizes a probabilistic matrix to store structural information of the promising solution, and then employs matrix-based search operators to execute variable neighborhood search. Additionally, the objective-oriented local intensification search is integrated to further enhance solution quality. Simulations and comparisons demonstrate the effectiveness of HVNS in solving the MODPFSP_SDST.

Aerospace components (ACs) are an important part of the aerospace equipment. Because of process specialty, AC production scheduling has always been seen a challenging work. In this paper, an improved dual-resource-constrained flexible job shop scheduling model is constructed to formulate the problem. The characteristics of AC manufacturing process are fully considered by analyzing the relationship among processes, machines, and workers. Meanwhile, the effect of machine type, worker skill level and worker labor efficiency are also incorporated into the model. To solve the model, we design an evolutionary strategy combined with TOPSIS and used Metropolis guidelines and perturbation operators to ameliorate the search process. A two-stage search-enhanced multi-objective evolutionary algorithm is proposed, which aims to minimize the completion time, production cost and worker load imbalance. Finally, experiments are conducted based on real AC production data. Experimental results verify the proposed algorithm is effective and competitive, which provides certain application value for relevant enterprises.

Timely delivery of materials is essential in ensuring the smooth operation of aircraft pulsating assembly lines. According to the production characteristic of aircraft pulse-like movement in pulsating assembly lines, a material delivery model is established to minimize the time window constraint penalties and delivery costs. Due to the problem characteristics of large scale, long decision cycle and uneven distribution of tasks in time, a phased scheduling method based on an improved genetic algorithm is proposed. Firstly, the decision cycle can be divided into the aircraft moving phase and the assembling phase. Secondly, the assembling phase is subdivided into several small phases to adjust the number of used AGVs and the delivery starting time of each phase. Finally, a genetic algorithm is used within each phase to decide the delivery tasks, delivery starting time and driving paths for every AGV, which has been improved for solving the large-scale problem by optimizing the generation of initial populations and adding a local search operator. The effectiveness of the proposed algorithm is verified in a practical case of an aircraft pulsating assembly line.

A connected vehicle platoon with unknown input delays is studied in this article. The control objective is to stabilize the connected vehicles, ensuring all vehicles are traveling at the same speed while maintaining a safety spacing. A decentralized control law using only onboard sensors is designed for the connected vehicle platoon. A novel switching-type delay-adaptive predictor is proposed to estimate the unknown input delays. By using the estimated unknown input delays, the control law can guarantee the stability of the successive vehicles. The platoon control adopts a one-vehicle look-ahead topology structure and a constant time headway (CTH) policy, which makes the desired spacing between vehicles vary with time. In this framework, the stability of the connected vehicles can be derived through the analysis of each pair of two successive vehicles in the platoon. Finally, an example is presented to illustrate the applicability of the obtained results.

In the field of dynamic scheduling, workers and scheduling models (SMs) play a crucial role in decision-making. Workers are able to help SM training by sample labeling, thereby enhancing the decision-making ability of SMs. However, existing supervised learning methods require a large number of labeled samples to train SMs, which limits the learning efficiency between workers and SMs. In this article, a human-machine interactive learning method based on active learning (HMILM/AL) is proposed. The method introduces active learning (AL) techniques to reduce labeling costs and improve learning efficiency. Referring to the AL framework, only a small subset of samples are selected from an unlabeled dataset and are labeled by workers, to train SMs. To further reduce labeling costs, sample selection, the key to the HMILM/AL, is improved by two strategies. First, a novel hybrid selection strategy (NHSS) is developed. By identifying and selecting more useful samples in an unlabeled dataset, the NHSS promotes efficient use of workers, and reduces labeling costs. Second, an enhanced NHSS (E-NHSS) is proposed, which considers both the difficulty of labeling samples and the usefulness of the samples. It reduces labeling costs by selecting easily labeled samples as much as possible. Finally, the proposed method is evaluated through experiments conducted in a real smart workshop. The results demonstrate that the HMILM/AL is very competitive compared with existing supervised learning methods. Moreover, both the NHSS and the E-NHSS can reduce labeling costs efficiently.

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results.

With the increasing demand of personalized products and the application of emerging technologies, substantial unexpected events appears in smart factories. Machine learning based adaptive scheduling shows significant appeal in smart shop floors, yet still has limitations in accommodating unexpected events. This paper presents a novel framework of HCPS (Human Cyber Physical System) based on the conventional CPS. A human-machine cooperative mechanism is proposed to coordinate task allocation between human and machine. Meanwhile, in order to integrate human intelligence and machine intelligence within scheduling decision making, a novel human-machine cooperative approach for adaptive scheduling is put forward. In the process of online scheduling, human operators adjust the deviation of production indicators on the basis of current condition. Subsequently, an enhanced fuzzy inference system combining with human intelligence is designed to obtain optimal dispatching rules, in which parameters are reduced by a K-means algorithm and optimized by a PSO algorithm. Finally, a case study is performed on the Minifab model. The simulation results validate the superiority of the proposed framework and approaches, and show good potential in efficiency and stability.

Sintering is recognized as one of the most energy-intensive process in the iron and steel enterprise. Energy-aware optimization introduces energy indicators into the sintering proportioning process, so that a reduction in energy consumption could be achieved without affecting normal production on the sintering shop floor. This paper proposes a novel cascade multi-objective optimization model (CMOM) that dynamically produces optimal dosing schemes. Firstly, an improved back-propagation neural network (IBPNN) with appending momentum and adaptive variable learning rate is proposed to better mimic solid energy consumption (SEC) and other indictors of sinter. Then, a cost based proportioning model (CBPM) is established with the consideration of chemical and physical characteristics of the resulting sinter. An improved immune-simulated annealing algorithm (ISAA) is designed to fast converge toward optimal solutions. Subsequently, by achieving feasible dosing scheme with initial chemical and physical requirements, energy-aware indicator is introduced into the cascade optimization framework. The CBPM would be repeatedly optimized by adjusting the expected performance until the predicted SEC and other indicators would have been in accord with the expected intervals. A case study of an actual sintering process in steel enterprise demonstrates the effectiveness of the framework and models. Meanwhile, results show that energy consumption is reduced by 5.28% on an optimal scenario.