The hot rolling scheduling problem involving dynamic order arrivals (HRSP-DOA) is pivotal in promoting Industry 4.0 initiatives in steel companies. This paper introduces a novel multi-objective ant colony optimization (mACO) algorithm enhanced with online learning strategies to address the HRSP-DOA. The problem is formulated as a variant of the prize-collecting dynamic vehicle routing problem (PC-DVRP). Its primary mission is to select high-reward orders to form a set of rolling rounds and sequence them within each rolling round to satisfy various technical constraints, thereby minimizing penalty costs and maximizing collected prizes. As new orders arrive, the problem’ s decision and objective spaces undergo unexpected changes, requiring the algorithm to make a quick response. To address these challenges, we propose an mACO with multiple heuristic matrices to search for the Pareto optimal set (POS) for the HRSP-DOA. Furthermore, we introduce the following online learning strategies to enhance mACO’s performance: 1) the upper confidence bound (UCB1) algorithm for selecting the most promising heuristic matrix, which is analogous to a multi-armed bandit problem; and 2) the K-means clustering method for transferring pheromone knowledge from the current event time to the following one. The well-designed comparative tests demonstrate that the proposed approach outperforms state-of-the-art algorithms on all synthetic instances. Component analysis further validates the effectiveness of the proposed online learning mechanisms. Finally, we present a real-world case study from an iron and steel company to verify its applicability. Note to Practitioners—In the context of Industry 4.0, the hot rolling scheduling problem involving dynamic order arrivals (HRSP-DOA) is pivotal for smart steel manufacturing, and it is formulated as a variant of the prize-collecting dynamic vehicle routing problem (PC-DVRP). By leveraging problem-specific properties, this paper develops a multi-objective ant colony optimization (mACO) algorithm with multiple heuristic matrices to address the challenges posed by changes in both decision-making and objective spaces caused by dynamic order arrivals. We also introduce two online learning strategies to enhance search efficiency: 1) the upper confidence bound (UCB1) algorithm for selecting the most promising heuristic matrix; and 2) the K-means clustering method for transferring pheromone knowledge in dynamic environments. The effectiveness of the proposed approach is validated through computational experiments. Furthermore, our dynamic multi-objective optimization algorithm can be implemented in real-world decision support systems and applied to other variants of the DVRP.

The remaining useful life (RUL) prediction is one of the most important tasks in the prognostics and health management of industrial equipment. The statistical model-based method is widely used for RUL prediction, but it depends on sufficient prior knowledge and appropriate degradation assumptions, which limits its use in the case of the complexity degradation process or insufficient prior knowledge. Motivated by this, we propose a hybrid model to describe the degradation process, which is composed of some given degradation models. This can be conducive to describing complex degradation trajectories and further improving the flexibility of the degradation model. Then, a sparsity mechanism is proposed to automatically fuse these candidate degradation models based on the sparse variational Bayesian method. As a result, we can find the most appropriate degradation mechanism for a given degradation process without sufficient prior knowledge. To the best of the authors' knowledge, it is the first time to use such a fusion mechanism to describe the complex degradation process. A numerical example, a practical example, and three public datasets are used to verify the effectiveness and merits of the proposed method.

Unsupervised domain adaptation is widely used for fault diagnosis under variable working conditions. However, loss oscillation and slow convergence, which are caused by the dynamically varying alignment of targets during domain adaptation, are ignored. Therefore, a Gaussian mixture variational based transformer domain adaptation (GMVTDA) fault diagnosis method is proposed. A feature extractor based on transformer layers is designed to capture long-term dependency information and local features. Subsequently, a domain alignment term is proposed to project the features learned from both working conditions into the common assistance distribution and make them follow the same distribution after the alignment process. Additionally, considering that fault diagnosis is a multiclassification process, a Gaussian mixture is utilized to build the common assistance distribution. Ultimately, the proposed GMVTDA is applied to bearing fault diagnosis under variable working conditions, and the experimental results prove its effectiveness.

Scheduling is one of the most important missions for plant-wide optimization in steelmaking manufacturing systems. In the context of dynamic scheduling, the decision-maker should simultaneously minimize economic objectives within the decision space and violation penalty out of the decision space. In this study, we introduce a resilient scheduling model in steelmaking plants, which provides flexible decisions, including buffering times in between stages and controllable processing speeds in the casting stage, to enable the solution to absorb random disturbances and recover quickly. We formulate the dynamic steelmaking scheduling problem with resilient responding strategies, which is a variant of dynamic multi-objective optimization problems (DMOP), and propose a resilient scheduling optimization framework to solve it over time. First, we employ a vector with problem-specific knowledge to map the whole decision space to sub-schedules in the casting stage, which contains casting priority, casting speed and scaling ratio. Next, we form a multi-objective linear programming model to evaluate these problem-specific vectors. Last but not least, we develop a self-learning based dynamic multi-objective differential evolutionary algorithm to solve the variant DMOP, in which a hypothesis-testing technique is used to detect and identify environmental changes. The sensitivity analysis and algorithm comparisons are performed on a wide range of problem instances under dynamic environments. Experimental evidence validates that the proposed resilient model and the optimization framework is effective to solve the dynamic scheduling problem in steelmaking plants. Note to Practitioners—This paper investigates a dynamic scheduling problem that comes from steelmaking manufacturing systems and is extensively studied in existing works. Because a decision-maker only has incomplete knowledge about the realistic environments, this paper simultaneously considers that both the decision and objective space of a scheduling problem dynamically changed over time. We develop a resilient scheduling model which can absorb the different types or scales of disturbances caused by unforeseen events, and recover rapidly to the original state. We formulate the resilience scheduling model and propose a dynamic multi-objective algorithm based on the self-learning differential evolutionary algorithm to solve it. The effectiveness of the proposed model and algorithm is validated via sensitivity analysis and comparison to other well-known algorithms. Furthermore, the resilient scheduling model and its optimization framework can also be applied to dynamic scheduling problems in other industries.

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

This paper is concerned with control of processes with uncertain delays for disturbance rejection. The effect of the uncertain delays on the stability is studied. First, the method to compute the maximum uncertain delay that a given controller can tolerate is described. Second, in the case of PI/PID controller, all of the admissible controller parameters stabilizing a system with uncertain but bounded delays are determined. Meanwhile, we propose a simple method to construct the parameter space satisfying a given robustness index for the nominal model. In the admissible regions satisfying various objectives, the global optimum controller is achieved for disturbance rejection in the presence of uncertain delay. As a result, the MIGO ( Ms-constrained Integral Gain Optimization) method is revisited in the case of uncertain delay, and the rule of selecting the value of maximum sensitivity function is proposed in terms of the bound on the uncertain delay. Two simulation examples and an experiment are given to demonstrate the effectiveness and advantage of the proposed method.