The finite resources of automated guided vehicles (AGVs) and machines in a flexible manufacturing system necessitate the integrated scheduling of production and transportation tasks to minimize delays in the production process. Constraint programming (CP) has demonstrated strong solving capabilities in complex shop scheduling problems. However, existing CP models exhibit significant limitations, typically yielding suboptimal solutions in specific scenarios. To address these challenges, this paper introduces a novel CP model that consistently delivers correct optimal solutions across all scenarios. First, the interrelationships among the four key decision sub-problems in AGV and machine integrated scheduling for flexible manufacturing systems are thoroughly analyzed. Next, based on the above analysis and leveraging the presence of transportation tasks, a new CP model is proposed to efficiently handle special cases where jobs do not require transportation. Finally, the model is benchmarked against state-of-the-art methods across three benchmarks and validated through a real-world case study. The results show that the proposed model outperforms existing approaches in both solution quality and efficiency. Notably, the proposed model updates the best-known solutions for the EX72 and EX84 instances, and proves the optimality of all EX instances for the first time.

Most research on flexible job shop scheduling assumes constant processing speeds. However, in real production, machines need to operate at variable speeds to achieve energy-efficient scheduling, which requires balancing multiobjective between production efficiency and green development. Such tradeoffs thus trigger the phenomenon in which massive solutions converge to identical objective values (i.e., the multimodal property), which is often neglected in scheduling problems. To address the above challenges, this work introduces a knowledge-enhanced evolutionary multitasking memetic algorithm (KEMMA) to solve the multimodal multiobjective flexible job shop scheduling problem considering speed (MMFJSP-S). First, self-paced learning motivated us to construct a simple auxiliary task and employ an evolutionary multitasking (EMT) framework to tackle the complex MMFJSP-S. Moreover, a knowledge enhancement and explicit transfer strategy is designed to reduce the effects of negative transfer by reinforcing and sharing beneficial knowledge across tasks. Finally, a mapping transformation mechanism is proposed to handle the multimodal property of the MMFJSP-S in the decision space. By comparing with ten advanced algorithms, the experimental results verify the remarkable superiority of the proposed KEMMA in solving MMFJSP-S and reveal the significance of studying the multimodal property.

Heuristic dispatching rules (HDRs) are widely used for solving the dynamic fuzzy job shop scheduling problem (DFJSSP). However, their performance is highly sensitive to specific scenarios and often necessitates expert customization. To overcome this, automated design methods like genetic programming (GP) and gene expression programming (GEP) have been proposed. Despite their success, these methods face challenges, such as high randomness in the search process. Recently, the combination of large language models (LLMs) with evolutionary algorithms has opened new possibilities for prompt engineering and automated algorithm design. To improve the ability of LLMs in automatic HDR design, this paper introduces a novel population self-evolutionary (SeEvo) framework, which draws inspiration from the self-reflective design strategies employed by human experts. Notably, this framework employs a novel teacher-student learning mechanism, allowing the LLM (student) to generate robust HDRs. Guided by a teacher model with complete knowledge of actual processing times, the student learns to infer fuzzy uncertainties from historical deviations, enabling it to effectively anticipate and adapt to fuzzy impacts. Experimental results demonstrate that SeEvo significantly outperforms GP, GEP, deep reinforcement learning (DRL) methods, and more than ten commonly used HDRs from the literature, particularly in previously unseen and dynamic scenarios.

Surface microdefect detection is a major challenge in advanced manufacturing, while the existing computer vision-based methods struggle to detect micrometer-scale defects, which are invisible to the naked eye. To overcome this problem, this article develops a novel system based on machine-tactile-sensation (MTS) to detect the microdefect. The MTS-based inspection system uses vision-based tactile sensor to convert the tactile signals into visual image, which can capture the fine geometric morphology of the microdefects. Furthermore, considering the limited resolution of the transformed signal, TouchNet, which integrates a label-guided diversity contrastive learning method with an adaptive receptive field selection module, is introduced for defect recognition, which can leverage prior knowledge to guide hyperspherical clustering and employs Gram regularization to prevent feature degradation. The experimental results on the HUST-Tactile dataset indicate that the proposed method can detect the microdefects as small as 0.01 mm with 97.06% accuracy. It also outperforms the state-of-the-art models by 1.33% and 8.46% on the public NEU-CLS and RSW-C datasets, respectively.

Zero-shot anomaly detection (ZSAD) aims to identify anomalies in new classes of images, and it’s vital in industry and other fields. Most current methods are based on the multimodal models CLIP and SAM, which have prior knowledge to assist model training, but they are highly dependent on the input of the prompts and their accuracy. We found that some diffusion model-based anomaly detection methods generate a large amount of semantic information and are very valuable for the ZSAD task. Therefore, we propose a diffusion model based zero-shot anomaly detection method, DZAD, and no additional prompt input is required. First, we propose the first diffusion-based zero-shot anomaly detection framework, which uses the proposed multi-timestep noise features extraction method to achieve anomaly detection in the denoising process of a latent space diffusion model with a semantic-guided (SG) network. Second, based on the detection results, we proposed a two-branch feature extractor for anomaly maps at different scales. Third, based on the difference between the anomaly detection task and other general image detection tasks, we propose a noise feature weight function for the diffusion model in the zero-shot anomaly detection task. Comparing with 7 recently state-of-the-art (SOTA) methods on MVTec AD and VisA datasets and analysis of the role of each component in ablation studies. The experiments demonstrate the validity of the method beyond the existing methods.

This paper studied the problem of wafer probing scheduling in wafer fabrication plants. As the final step in the front-end process of semiconductor manufacturing, wafer probing plays a critical role in wafer production. The wafer probing scheduling problem is modeled as a flexible job shop scheduling problem, considering resource constraints and batch processing. Firstly, a mixed-integer programming model is constructed to minimize the makespan. Then, an improved gray wolf optimizer is proposed to address the wafer probing scheduling problem. This improved gray wolf optimizer incorporates three improvement strategies within the framework of the original gray wolf algorithm: (1) an opposition-based learning approach is utilized to enhance the quality of the initial population; (2) leader agents mutation strategy is developed for local search; (3) a parameter adaptive adjustment is introduced to balance local and global search. In addition, an active decoding framework based on prior knowledge is designed to accelerate the convergence of the improved gray wolf optimizer. Finally, the effectiveness of the proposed improved gray wolf optimizer is verified on 15 instances of varying scales, and the results demonstrate that the proposed improved gray wolf optimizer outperforms other state-of-the-art algorithms.

Reconfigurable manufacturing systems offer enhanced flexibility to adapt to rapidly changing market demands. However, the reconfigurability of equipment introduces significant challenges to production scheduling, complicating optimization. This paper addresses the scheduling problem in reconfigurable manufacturing systems and proposes a discrete grey wolf optimizer algorithm with an active-decoding strategy (DGWO). A novel operation-configuration encoding scheme is proposed to comprehensively represent the solution space, accompanied by an active-decoding strategy that maximizes solution exploration and minimizes idle time. In the GWO, two crossover operators are introduced to enhance the search space, while the random walk strategy is introduced to prevent the algorithm from falling into premature convergence. Additionally, four neighborhood structures are defined based on the encoding space, and an efficient randomized enhanced local search is developed based on these structures to improve the algorithm's exploitation capability. The proposed DGWO algorithm is evaluated on 60 benchmark instances and compared with several related algorithms, demonstrating superior effectiveness and convergence performance.

This paper proposes an iterative branch-and-bound (IB&B) approach for complex product assembly station scheduling problems (CPASSP) to minimize the total weighted tardiness (TWT). First, an iterative adjusting mechanism for upper bounds is designed to improve the efficiency of the algorithm. The upper bound in the proposed algorithm is set directly without a method to obtain a feasible solution. If no feasible solution is found, the upper bound is adjusted and the branch-and-bound component is executed again. Second, a station-based branching strategy is developed to decompose the CPASSP problem, which is able to avoid the many-to-one situations typically encountered with task-based branching strategies. Moreover, a disjunctive graph for CPASSP is formulated to represent and evaluate a schedule. The proposed IB&B is compared with alternative branch-and-bound approaches and a mixed integer linear programming (MILP) model implemented by CPLEX solver on four test instances of different scales. Experimental results demonstrate that the proposed IB&B is more efficient and obtains optimal solutions with less computation time.

Additive manufacturing has revolutionized the way to design and manufacture products by enabling complex geometries and on-demand production. However, 3D printing without scheduling is time-consuming and space-inefficient. To address these issues, a dense pixel-based genetic algorithm (DPGA) is proposed. To fast characterize 3D parts, a tolerant pixel matrix (TPM) is adopted for abstraction. Based on the TPM, a double-layer encoding scheme is designed to represent the solutions. An active decoding strategy is designed to maximize space utilization, which can improve the quality of schemes decoded with the same encoding. In the section of operator design, an initialization strategy based on load balancing is developed, which can effectively improve the quality of initial population. Additionally, a population rebirth mechanism is designed to efficiently escape from local optima. Computational experiments demonstrate the effectiveness of DPGA in solving additive manufacturing scheduling problems with varying sizes and complexities. The proposed DPGA outperforms traditional genetic algorithm and other state-of-the-art methods in terms of processing time and packing density.

Due to the complexity of production scheduling and increasing demand, various methods, including solvers, are widely applied to the job shop scheduling problem. Among these, using machine learning techniques to enhance solver quality has attracted significant attention. However, beyond the model, the characteristics of problems greatly influence solver performance. This study focuses on the classic job shop scheduling problem and explores methods to improve constraint programming model efficiency through machine learning. An automatic branching strategy selection method based on machine learning is proposed, consisting of two components: the problem features extraction and strategy selection identification. For features extraction, three feature extraction approaches are designed. In the strategy selection phase, a classification method based on the graph neural network is used to incorporate the set of three types of features, and the problem-related loss function is designed. We conducted experiments on the proposed method on 3500 training sets and 70 test sets (benchmark), and compared the experiments with the automatic selection strategy that comes with OR-Tools. The results show that the proposed method can obtain equal or better solutions on 81.42% of the instances.

Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typically model MTS data in either Euclidean or Riemannian space, limiting their ability to capture the diverse geometric structures and complex Spatio-Temporal (ST) dependencies inherent in real-world data. To overcome this limitation, we propose the Hybird Symmetric Positive-Definite Manifold Graph Neural Network (HSMGNN), a novel graph neural network-based model that captures data geometry within a hybrid Euclidean–Riemannian framework. To the best of our knowledge, this is the first work to leverage hybrid geometric representations for MTS forecasting, enabling expressive and comprehensive modeling of geometric properties. Specifically, we introduce a Submanifold-Cross-Segment (SCS) embedding to project input MTS into both Euclidean and Riemannian spaces, thereby capturing ST variations across distinct geometric domains. To alleviate the high computational cost of Riemannian distance, we further design an Adaptive-Distance-Bank (ADB) layer with a trainable memory mechanism. Finally, a Fusion Graph Convolutional Network (FGCN) is devised to integrate features from the dual spaces via a learnable fusion operator for accurate prediction. Experiments on three benchmark datasets demonstrate that HSMGNN achieves up to 13.8% improvement over state-of-the-art baselines in forecasting accuracy.

With the accelerated development of Industry 4.0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi-robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally, enterprises have high privacy requirements for production scheduling data, which prevents the use of cloud-based large language models (LLMs) for solution development. To address these challenges, there is an urgent need for an automated modeling solution that meets data privacy requirements. This study proposes a knowledge-augmented mixed integer linear programming (MILP) automated formulation framework, integrating local LLMs with domain-specific knowledge bases to generate executable code from natural language descriptions automatically. The framework employs a knowledge-guided DeepSeek-R1-Distill-Qwen-32B model to extract complex spatiotemporal constraints (82% average accuracy) and leverages a supervised fine-tuned Qwen2.5-Coder-7B-Instruct model for efficient MILP code generation (90% average accuracy). Experimental results demonstrate that the framework successfully achieves automatic modeling in the aircraft skin manufacturing case while ensuring data privacy and computational efficiency. This research provides a low-barrier and highly reliable technical path for modeling in complex industrial scenarios.

Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental uncertainties, policies in MARL must remain robust to tackle the sim-to-real gap. We focus on robust two-player zero-sum Markov games (TZMGs) in offline settings, specifically on tabular robust TZMGs (RTZMGs). We propose a model-based algorithm (*RTZ-VI-LCB*) for offline RTZMGs, which is optimistic robust value iteration combined with a data-driven Bernstein-style penalty term for robust value estimation. By accounting for distribution shifts in the historical dataset, the proposed algorithm establishes near-optimal sample complexity guarantees under partial coverage and environmental uncertainty. An information-theoretic lower bound is developed to confirm the tightness of our algorithm's sample complexity, which is optimal regarding both state and action spaces. To the best of our knowledge, RTZ-VI-LCB is the first to attain this optimality, sets a new benchmark for offline RTZMGs, and is validated experimentally.

Variable lot-sizing is an effective approach to improve production efficiency by splitting an operation into several sublots, which has been widely applied in flexible manufacturing systems. However, the flexibility of lot-sizing will dramatically expand the solution space, leading to excessive computation time in converging to the relative optimum. To address this challenge, this paper introduces an end-to-end deep reinforcement learning framework based on heterogeneous graph attention mechanisms (HGADRL) for flexible job shop scheduling problem with variable sublots. Unlike traditional heuristic and rule-based methods, HGADRL dynamically learns the high-dimensional nature, providing a more generalizable solution in a very short time. In HGADRL, a modified heterogeneous disjunctive graph is designed to represent the dynamic scheduling status, including operation selection and sublot division. A dual-scale graph attention network combined with two interconnected attention modules is developed, enabling the precise capture of complex interdependencies between heterogeneous vertices. This approach can significantly enhance the agent's ability to self-learn and evolve optimal policies. By leveraging local and global features extracted through the graph attention network, an actor-critic network is employed for high-quality scheduling in different states. Experimental results demonstrate that the proposed method outperforms the 12 mixed priority dispatching rules, two meta-heuristic methods and two deep reinforcement learning methods in all 500 synthetic instances. Additionally, the proposed method outperforms all compared methods across 16 unseen scales of instances and four real-world instances, demonstrating its strong generalization capabilities.

Rolling bearings are the key components of various equipment and easily subject to failure, remaining useful life (RUL) prediction technology can grasp their health statuses to make a reasonable maintenance plan. Transfer learning methods for rolling bearing cross-domain RUL prediction focus on the direct alignment of the degradation process between the target domain and source domain. However, the working condition and degradation process of rolling bearing to be predicted are unknown, which are different from the target domain or source domain. The caused data distribution discrepancy has seriously affected the RUL prediction acceptance. To solve this problem, a multilevel feature alignment (MLFA) method for advancing RUL prediction without target domain data for training is proposed. Specifically, a proposed weighted TOPSIS is implemented for supervised label making to train a robust RUL prediction prior model. The three-level feature alignment (TLFA) strategy, which involves feature alignment by the proposed adaptive nonlinear state estimation, envelope spectrum (ES), ES-combined deep convolutional autoencoder, is developed to mitigate the data distribution discrepancy between the predicted and prior entities. TLFA transforms prediction tasks from cross-domain to different failure behaviors. Extensive experiments conducted on both public and industrial scene run-to-failure bearing datasets validated the superiority of the MLFA. These results from comparison experimental show that MLFA improves mean absolute error and root mean absolute error (RMSE) about 0.061 and 0.054 individually than some state-of-the-art methods.

The Distributed Job-shop Scheduling Problem (DJSP) is a significant issue in both academic and industrial fields. In real-world production, uncertain disturbances such as job arrivals are inevitable. In the paper, the DJSP with job arrivals is addressed with a Multi-action Deep Reinforcement Learning (MDRL) method. Firstly, a multi-action Markov Decision Process (MDP) is formulated, where a hierarchical multi-action space combining operation set and factory set is proposed. The reward function is related to the machine idle time. Additionally, the state transition is also elaborately designed, which includes four typical cases based on job arrival times. Then, a scheduling policy with two decision networks is proposed, where the Graph Neural Network (GNN) is applied to extract the intrinsic information of the scheduling scheme. A Proximal Policy Optimization (PPO) with two actor-critic frameworks is designed to train the model to achieve intelligent decision-making with hierarchical action selections. Extensive experiments are conducted based on 1350 instances. The comparison among 17 composite rules, 3 closely-rated DRL methods, and 2 metaheuristics has proven the outperformance of the proposed MDRL. The application of the MDRL in an automotive engine manufacturing company has demonstrated its engineering value in the industrial field.Note to Practitioners—The DJSP with job arrivals is a common challenge faced by equipment manufacturers, specifically in the electronic device manufacturing industry. These manufacturers are located in different areas and have varying facility configurations and operation trajectories. To address this challenge, a machine learning-based method can be applied for scheduling daily production tasks. This method divides the DJSP into two subproblems, namely job assigning and job sequencing, and uses two decision networks based on DRL to solve them. To address the uncertainty caused by job arrivals, the rescheduling process and the state update mechanism are carefully designed. A GNN is used for feature extraction at each decision point, and it feeds the decision networks with the extracted features to make the optimal selection. The proposed method has the ability of self-learning and self-adapting, and its effectiveness has been proven through experiments on 1350 test instances. Its practical application has been demonstrated in the production scenarios of an automotive engine manufacturing company. In the future, the method can be adopted to solve more complex distributed manufacturing problems that have constraints such as transportation costs and machine breakdowns.

With the rapid development of intelligent manufacturing, multi-robot collaborative systems are increasingly integrated into various production processes. In the flexible job shop environment of automotive stamping, achieving smooth operation and efficient manufacturing of production lines hinges on solving the critical issues of multi-robot task allocation and scheduling. However, for such fixed-type multi-robot collaboration problems, robots are constrained by specific areas or predetermined trajectories, and processing times can only be adjusted by varying the number of available robots. Therefore, the scheduling problem in multi-robot collaborative flexible job shop problems (MCFJSP) is divided into two sub-problems: FJSP with controllable processing times and multi-robot collaborative task balancing. To address these, we propose three distinct methods: mixed integer linear programming (MILP), constraint programming (CP), and a hybrid genetic algorithm-constraint programming (GA-CP). Finally, a set of 48 benchmark cases and two real-world cases are developed to test these methods. Comparative experiments demonstrate that the MILP model is superior in small-scale cases, while the GA-CP model exhibits the best overall performance in medium to large-scale cases. Furthermore, through comparisons with two advanced algorithms, the effectiveness and superiority of the GA-CP method in addressing real-world cases are confirmed.[8pt]Note to Practitioners—In modern manufacturing environments, particularly in industries like automotive manufacturing, multiple robots working together on complex tasks are increasingly common. This paper addresses the practical challenge of effectively scheduling these robots to maximize efficiency while reducing the number of robots assigned to each task. This study introduces and compares different methods, including MILP, CP, and GA-CP methods, that can help practitioners determine the best way to allocate tasks among robots and schedule them efficiently. For example, in small-scale tasks, the MILP model can quickly provide the best solution. However, as the complexity and scale of the task increase, the GA-CP method becomes more practical, offering high-quality solutions within a reasonable timeframe. The study provides actionable insights that can be applied directly to real-world production scenarios, helping practitioners in industries like automotive stamping to maximize job shop productivity while reducing energy consumption losses in robot processing.

The joint optimization of production scheduling and resource constraints is critical to modern manufacturing systems. The number of auxiliary resources (fixtures) is usually insufficient in customized manufacturing. Thus, on-site machining fixtures (Type II fixtures) should be prepared in the workshop to reduce the shortage. In this way, Type II fixtures are production tasks and resource constraints, while Type I fixtures are only resource constraints. The existing studies mainly concentrate on Type I fixtures, whereas the research on Type II fixtures is limited. Therefore, this paper focuses on a flexible job shop with on-site machining fixtures (FJSP-F). Firstly, a mathematical model is developed to minimize total weight tardiness (TWT). Secondly, a job-fixture-machine (JFM) encoding and novel decoding methods are presented to obtain a feasible schedule solution. Thirdly, an improved genetic algorithm (IGA4F) with problem-specific variable neighborhood search (PVNS) is proposed to balance the exploration and exploitation. Finally, the proposed algorithm is tested on 20 instances with comparison algorithms. The results demonstrate that IGA4F is a competitive algorithm in large-scale instances. From the case study results, the performance gains of the TWT and makespan obtained by IGA4F are 49.27% and 28.94% compared to the original schedule solution. Note to Practitioners—The integrated problem of fixture allocation and production scheduling is widespread in highly customized manufacturing enterprises, such as aerospace and shipbuilding. A well-balanced allocation between fixtures and machines can facilitate productivity and resource utilization. In general, Type I fixtures can be used directly if they are idle, and these fixtures are treated as resource constraints. However, due to the limited number of Type II fixtures, they are only available when finished in the workshop. Hence, Type II fixtures are considered production tasks and resource constraints, and the number of these fixtures is dynamic during the production cycle. Therefore, it is necessary for enterprise managers to investigate the effect of Type II fixtures on production scheduling. This paper proposes novel encoding and decoding methods to represent the solution and objective spaces. The evolutionary-based algorithm is proposed to solve the daily order of a real-world enterprise. The obtained results from the proposed algorithm can guide the managers to promote the workshop’s productivity.

Computer numerical control (CNC) machine tools consume a large amount of raw materials in its manufacturing process. With the emphasis on the economy of machine tools, reducing the material consumption becomes significant. The lightweight optimization is regarded an effective way for material saving, since it can reduce the cost while improving machining performance. However, existing lightweight studies of machine tool mainly depend on experience, which cannot ensure the accuracy and efficiency, and the design scheme is hard for manufacturing. On this basis, a novel data-driven lightweight optimization method with meta-structure integrating the topological and size optimization is proposed in this article. Meta-structures are modeled to form the topological configuration of machine tool for avoiding time-consuming design analysis. The surrogate models with a novel adaptive sequential sampling method are built to fit accurate relationships between structure and performances. A multi-objective size optimization frame is developed for further reducing the mass, deformation and enhancing natural frequency of machine tool. The gear honing machine tool is taken as the case study, where results indicate that the lightweight optimization can reduce mass by 13.72% under the premise of structure safety.

Additive manufacturing (AM) constructs complex products through the layer-by-layer deposition of materials. AM enables complex geometry fabrication but faces spatiotemporal optimization challenges: maximizing space utilization and minimizing printing time, requiring intelligent nesting of products and scheduling of resources. The integration of nesting and scheduling further expands the solution space, but existing methods frequently overlook essential geometric intricacies in irregular products. This paper proposes a novel pixel-based grey wolf optimizer algorithm (PGWO) to improve packing density and reduce time cost in stereolithography (SLA) printing. In the proposed PGWO, point-cloud pixelization is employed to simplify 3D irregular parts. Each part independently performs autonomous orientation to minimize local space occupancy at a low cost. Based on the time-frequency domain conversion, a convolutionally accelerated localization strategy (Cals) is proposed to improve the speed of nesting. For efficient scheduling, a bottleneck-balanced local search phase is designed with two operators. By targeting critical bottlenecks in printing, two operators can effectively reduce the frequency of layer changes, further optimizing local optimal solutions. PGWO is evaluated on 70 instances with diverse scales, and shows significant superiority over other state-of-the-art methods in over 88% of instances. The results demonstrate its superior performance in reducing printing time and enhancing packing density.

Automated Guided Vehicles (AGVs) have found widespread application in discrete manufacturing systems. In flexible job-shop environments, the integrated scheduling of machines and AGVs is a significant research direction to improve the productivity. However, the existing mathematical model assigns non-existent transport tasks to the corresponding AGVs, resulting in poor performance. To tackle this weakness, this paper proposes a novel mixed integer linear programming (MILP) model. Firstly, the flexible job-shop scheduling problem with limited AGVs (FJSPLA) is decomposed into four sub-problems, and the interactions and dependencies between the sub-problems are elaborated. Secondly, the existence of transport tasks is explained in detail based on the disjunctive graph model. Subsequently, a more efficient MILP model is proposed, leveraging insights from the four sub-problems and the disjunctive graph model. Finally, comparison experiments are conducted, encompassing two benchmarks (FJSPT and EX), along with a real-world case. The proposed model exhibits a more streamlined formulation with fewer decision variables and constraints in comparison to existing models. It successfully proves optimality for the most challenging instance FJSPT7 as well as 15 instances in EX benchmark. Compared with the existing model, the experimental results not only demonstrate the effectiveness and superior performance of the proposed model but also show the practicality in addressing real workshop problems.Note to Practitioners—Automated guided vehicles (AGVs) have been extensive application in various industries, prompting practitioners to integrate the scheduling of machines and AGVs during production planning. To address this realistic production problem, this study develops a novel MILP model. Through comprehensive analyses, integrated scheduling is decomposed into four sub-problems and the correlations between the four sub-problems are accurately presented. For each sub-problem, we establish the corresponding mathematical formulations. Practitioners can use the work in this paper to clearly understand the integrated scheduling problem, and can easily use the optimization software to solve the model. As in our case study, practitioners collate the production information according to their workshop, and the model can give the optimal solution for integrated scheduling in an acceptable time. The optimal solution obtained from the proposed model can guide the practitioners to maximize the productivity of the workshop.

The flexible job shop scheduling, as the most typical production mode in industrial manufacturing, aims to improve production efficiency. However, the proposal of energy-saving and emission-reduction policy implies that it is impossible to increase the processing speed to improve productivity, and energy consumption is also becoming another important optimization objective. For the multi-objective flexible job shop scheduling problem, the optimization process tends to converge faster in some regions. This is because different scheduling sequences obtain the same objective values, i.e. there is a multimodal characteristic, which is still hardly investigated. Therefore, optimizing the decision space and the objective space simultaneously has become an urgent challenge that needs to be solved. To overcome the above challenges, we model the multimodal multi-objective flexible job shop scheduling problem with variable speed (MMFJSP-S) and propose an affinity propagation hierarchical memetic algorithm (APHMA) to minimize makespan and total energy consumption. Firstly, four problem-specific neighborhood structures are employed to enhance the convergence; Then, an affinity propagation clustering combined with the random forests strategy is proposed to classify the global and local Pareto sets; Finally, a hierarchical environmental selection strategy is designed to ensure the convergence and diversity in the decision and objective spaces. Evaluations against seven advanced algorithms on MK and DP benchmarks demonstrate the competitive performance of APHMA in solving MMFJSP-S.

Intelligent transportation systems, incorporating multiple AGVs, are extensively utilized in manufacturing workshops in various industries. This widespread use has spurred significant research interest in the integrated production and transportation scheduling problem, particularly in job shop environments. However, current research often fails to adequately leverage domain knowledge, leading to algorithms that struggle to find high-quality solutions for large-scale problems. To address this issue, this paper proposes a knowledge-driven hybrid algorithm (KDHA). The domain knowledge incorporated in the KDHA includes: 1) three critical path-based neighborhood structures for comprehensive neighborhood solution searches, 2) three neighborhood cropping methods to avoid ineffective searches for poor solutions, and 3) a new fast evaluation method to enhance the efficiency of neighborhood solution searching. Additionally, a new encoding method is introduced to achieve a one-to-one mapping between the chromosome and the disjunctive graph, allowing valuable information from neighborhood solutions to contribute to the algorithm’s evolution. Comparative experiments between the proposed algorithm and other state-of-the-art approaches are conducted on the small-scale EX and large-scale SWV benchmarks. The results demonstrate that the proposed KDHA is able to output better solutions efficiently and consistently, and updates the best solutions of all 20 SWV instances.

Fault isolation, or fault location, aims to identify anomalous components at the start of the maintenance process. However, fault isolation within complex equipment can be challenging due to constraints on the scarcity of labeled data and the intricate interaction among various substructures. To overcome this challenge, an embedding-based Bayesian Network (BN) probability inference is proposed to locate the fault components, where the embedding, derived from semantic meanings, can approximate the actual fault distribution within BN. First, a Fault Graph (FG) is established based on the equipment's mechanical structure and its mechanisms. Then, a Multifield hyperbolic embedding is employed to vectorize the nodes in the FG, thereby preserving the inherent logic maximally. Following this, the FG is transformed into the BN, which facilitates the prediction of the faulty component based on available evidence, using the well-trained graph embedding. An empirical study on oil drilling equipment showcases the graph embedding properties and inference performance of the proposed method by comparing it with other cutting-edge methods and traditional scenarios.

Reconfigurable manufacturing systems root mean square (rms) offer high flexibility, enabling efficient adaptation to changing market demands. However, this reconfigurability significantly increases the complexity of production scheduling. This article addresses the rms scheduling problem (RMSSP) to minimize the makespan. A configuration-coupled heterogeneous disjunctive graph (CHDG) model is proposed to represent feasible solutions by incorporating machine-configuration arcs and reconfiguration nodes, capturing reconfiguration processes and operation statuses. Feasibility theorems for intramachine and intermachine movements are developed, and six solution-space clipping strategies are introduced to reduce invalid searches. Based on these, a dual-space artificial bee colony (DABC) algorithm is proposed, featuring a novel operation-configuration encoding scheme and configuration-associated active-decoding strategy to maximize the potential of encoding. A hierarchical crossover operator and CHDG-based neighborhood search operators collaboratively explore the encoding and disjunctive graph (DG) spaces for efficient optimization. Numerical experiment results on 60 benchmark instances show that integrating CHDG significantly improves DABC’s performance in solving RMSSP. In addition, the six clipping theorems reduce invalid intramachine neighborhood searches by 55.3%.

Human–machine hybrid reconfiguration manufacturing is an emerging paradigm in the field of precision equipment production and can greatly improve the production capability of the workshop. However, numerous complex constraints and a dynamic environment make reasonable scheduling very difficult. To this end, this article studies the dynamic human–machine hybrid reconfiguration manufacturing scheduling problem (DHMRSP) and proposes a novel deep reinforcement learning (DRL) scheduling method. Specifically, a dual-agent Markov decision process (MDP) is established, which can handle seven complex constraints and three disturbance events. Then, a heterogeneous competition graph attention network (HCGAN) is designed, where the meta-path-based subgraph conversion reflects the resource-operation competition, and three modules use node-level attention and semantic-level attention to realize important information embedding. Afterward, a dual proximal policy optimization (PPO) algorithm with HCGAN and mixed action space (HM-DPPO) is proposed, where the allocation agent and reconfiguration agent achieve collaborative learning by taking joint action and sharing graph embeddings and reward. Experimental results prove that the proposed approach outperforms rules, genetic programming (GP), and three DRL methods on different instances and can effectively handle various disturbance events.

The application of automated guided vehicle (AGV) greatly improves the production efficiency of workshop. However, machine flexibility and limited logistics equipment increase the complexity of collaborative scheduling, and frequent dynamic events bring uncertainty. Therefore, this article proposes a real-time scheduling method for dynamic flexible job shop scheduling problem with AGVs using multiagent reinforcement learning (MARL). Specifically, a real-time scheduling framework is proposed in which a multiagent scheduling architecture is designed for achieving task selection, machine allocation and AGV allocation. Then, an action space and an efficient action decoding algorithm are proposed, which enable agents to explore in the high-quality solution space and improve the learning efficiency. In addition, a state space with generalization, a reward function considering machine idle time and a strategy for handling four disturbance events are designed to minimize the total tardiness cost. Comparison experiments show that the proposed method outperforms the priority dispatching rules, genetic programming and four popular reinforcement learning (RL)-based methods, with performance improvements mostly exceeding 10%. Furthermore, experiments considering four disturbance events demonstrate that the proposed method has strong robustness, and it can provide appropriate scheme for uncertain manufacturing system.

Most industrial equipment works under variable conditions, variable conditions would increase the within-class difference and reduce the cross-class difference of fault samples. The existing methods mainly consider steady speed scenarios and the global domain adaptation while ignoring the within-class and cross-class distribution alignment, the fault distribution variation leads to the deterioration of diagnosis performance. In this article, a self-supervised pseudo-label learning-enabled (SPL) cross-domain diagnosis method is proposed for fault diagnosis under time-varying speeds. Specifically, the Cauthy maximum mean-square discrepancy is designed for global distribution-level feature alignment by reducing the domain discrepancy. The pseudo-label training and consistency regularization are established for decision boundary adjustment by optimizing the probability distribution difference between the target domain and its perturbed output. Besides, uncertainty-reweighted class confusion minimization is introduced in within-class and cross-class distribution alignment to decrease negative transfer caused by huge within-class discrepancies and small cross-class differences, which can effectively improve the diagnosis accuracy of the hard-to-identify confusion samples. Experiments on time-varying fault diagnosis tasks show the superior performance of the proposed method. The proposed SPL framework improves average diagnosis accuracies by at least 8%.

The flexible job scheduling problem with batch processing machines (FJSP-BPM) is an extension of the flexible job shop scheduling problem and batch scheduling problem in some engineering scenarios. It allows an operation to be processed by any usable machines, and multiple jobs can be processed on a batch in batch processing machines simultaneously. In this study, a mixed integer linear programming model is proposed to minimize the makespan. This study first designs a strategy of chromosome slicing based on the marginal cost to generate batches and a hybrid genetic algorithm (HGA) is proposed to solve the FJSP-BPM problem. Neighborhood structures are designed to search for better solutions. Finally, the experimental results demonstrate that the proposed HGA has obtained the best solutions for all instances and has effectively solved the FJSP-BPM problem.

Crossing different working conditions is a common scenario in rotating machinery fault diagnosis, which can be solved by cross-domain transfer learning. However, the existing diagnosis methods do not consider possibly new and unknown faults, i.e., open-set fault diagnosis scenarios, which would cause diagnosis performance degradation. To address this issue, in this article, the self-supervised-enabled open-set cross-domain (SEOC) approach is proposed for fault diagnosis of rotary machines under various working conditions. Specifically, open-set risk minimization and self-supervised contrastive learning are proposed to improve distinguishability and stability. A pseudolabel consistency self-training is designed to decrease the domain shift. A novel open-set identification strategy with the designed squeeze confidence rule is developed for unknown- and known-class fault detection. Experiments on three-phase motor and bearing datasets illustrate the superior and efficient performance of the proposed SEOC method. The proposed SEOC framework improves the overall classification accuracies by at least 9%, and the average accuracy of unknown fault identification is more than 97.68% in motor and bearing fault diagnosis.

Disassembly line balancing (DLB) is used for efficient task planning of large-scale end-of-life products, which is a key issue to realize resource recycling and reuse. Robot disassembly and U-shaped station layout can effectively improve disassembly efficiency. To accurately characterize the problem, a mixed-integer linear programming model of U-shaped robotic DLB is proposed. The aim is to minimize the cycle time to shorten the offline time of the product. Since there are many dynamic disturbances in the actual disassembly line, and traditional optimization methods are suitable for dealing with static problems, this article develops a deep reinforcement learning approach based on problem characteristics, namely deep Q network (DQN), to achieve a dynamic balancing of disassembly lines. Eight state features and ten heuristic action rules are designed in the proposed DQN to describe the disassembly environment completely. The effectiveness and superiority of the proposed DQN are verified by numerical experiments. In the case of a laptop disassembly line, not only the cycle time of the robots is reduced, but also intelligent decision-making and dynamic planning of disassembly tasks are realized.

Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.

Image anomaly detection and segmentation are important for the development of automatic product quality inspection in intelligent manufacturing. Because the normal data can be collected easily and abnormal ones are rarely existent, unsupervised methods based on reconstruction and embedding have been mainly studied for anomaly detection. But the detection performance and computing time require to be further improved. This article proposes a novel framework, named as pretrained feature mapping (PFM), for unsupervised image anomaly detection and segmentation. The proposed PFM maps the image from a pretrained feature space to another one to detect the anomalies effectively. The bidirectional and multihierarchical bidirectional PFM are further proposed and studied for improving the performance. The proposed framework achieves the better results on well-known MVTec AD dataset compared with state-of-the-art methods, with the area under the receiver operating characteristic curve of 97.5% for anomaly detection and of 97.3% for anomaly segmentation over all 15 categories. The proposed framework is also superior in terms of the computing time. The extensive experiments on ablation studies are also conducted to show the effectiveness and efficiency of the proposed framework.

The permutation flow shop scheduling problem (PFSP) is one of the most important scheduling types in the mass customization production with many real-world applications. It is also a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. However, most of the reported algorithms have not analyzed the solution space and may search many useless solution spaces, which impedes these algorithms from effectively optimizing the large-scale PFSPs in reasonable computation time. To address large-scale PFSPs with more than 100 jobs, this article proposes a solution space clipping-based improved simulated annealing (SA) algorithm. First, inspired by Johnson’s rule, this article explores its essential principle and generalizes it to the general situation. Before the optimization algorithm is used to find the optimal solution, a preordering combination is performed on the processed jobs according to this extended rule to considerably clip the solution space. Second, a hybrid release strategy based on the Palmer algorithm is developed for the proposed algorithm. Then, some key operators of the SA algorithm are also improved. Finally, to verify the performance of the proposed algorithm, this work performs a set of comparative experiments with the-state-of-art methods on the part of the TA benchmark and VRF benchmark with more than 100 jobs. The experimental results show that the proposed method can achieve superior results compared to other algorithms. Furthermore, the performance of the algorithm is comprehensively analyzed, which confirms the effectiveness of the proposed method.

The no-wait flowshop scheduling problem (NWFSP) has received widespread attention because of its wide application in the steel industry, food industry, and so on. This paper proposes a hyper-heuristic algorithm to solve it with the objective to minimize the makespan. Firstly, three Modified Nawaz-Enscore-Ham (MNEH) algorithms are designed to keep the diversity of initial values. Secondly, in order to fully search for potential solution domains, the Low-Level Heuristics (LLH) are constructed to speed up the search process for each neighborhood and the search order are decided by proposed High-Level Strategies (HLS). Thirdly, one taboo mechanism and two backtracking mechanisms are designed to promote the exploitation performance. No parameters are used in all stages. Therefore, it is not necessary to adjust parameters when it is used to solve any NWFSP problems and the equivalent Asymmetric Traveling Salesman Problems (ATSP). The test results of the Tailard benchmarks confirm the stability and effectiveness of the proposed algorithm.

Ultra-wideband (UWB) technology has been widely applied in the high-precision indoor positioning system. However, the complicated indoor environment makes signals propagate in non-line-of-sight (NLoS) situations, which seriously deteriorates the positioning accuracy. This work proposes an outlier-aware method to improve the positioning accuracy under NLoS scenarios. End-to-end optimization and positioning are achieved by combining the measurement error mitigation process with the positioning process. Experiments on public benchmarks illustrate that the proposed method enhances the performance of indoor positioning in NLoS situations.

Surface defect is a serious problem in real-world manufacturing system and it is important to use vision-based recognition to ensure the surface quality of products. Currently, due to the ability of automatic feature extraction, deep learning models, such as convolutional neural network (CNN), have been widely used in this area. However, these CNN-based models may not solve a problem well - inter-class similarities and intra-class variations (ISIV), which affect their ability of feature extraction and thus influence their recognition performance. To address this problem, this paper introduces a graph guidance mechanism into CNN to improve the ability of feature extraction, called Graph guided Convolutional Neural Network (GCNN). Firstly, GCNN defines a graph by computing the similarities between training samples. Secondly, the graph is introduced into VGG11, a popular CNN structure, to increase the inter-class distances and decrease the intra-class distances between defect samples. Meanwhile, a learnable coefficient is introduced into the training process to balance the effect of graph guidance automatically. The experimental results on four famous surface defect datasets demonstrate that the graph guidance helps CNN models have better ability of feature extraction and thus achieve better performance. Compared with state-of-the-art models, the proposed method can achieve the best performance. Furthermore, the final discussion shows that the learnable coefficient can help the proposed model to gain better performance, and that the proposed model increases a little computation cost compared to its original CNN model.Note to Practitioners—This paper is motivated by the problem in real-world manufacturing process – inter-class similarities and intra-class variations. Most of current CNN-based models may not solve this problem well, which limits their applications in real-world system. This paper proposes a graph guidance mechanism and introduces it to the training of CNN. The proposal can improve the ability of feature extraction, and thus have better performance than those without the graph guidance. By applying the proposal to four examples, the results show that the proposal is more feasible and effective than the state-of-the-art models in surface defect recognition. Furthermore, the mechanism assists the training of CNN and is removed in the process of recognition, so it will not increase time and occupied memory in recognition, and meanwhile, only limited computation cost is increased in training process.

End-of-life (EOL) products contain many valuable parts and materials. The timely disassembly and recycling of EOL products can bring considerable economic benefits and reduce environmental pollution. To reduce the cost of manual disassembly, robotic disassembly has become one of the main methods used to dismantle EOL products. In addition, parallel disassembly can shorten the makespan of EOL products. Therefore, this article focuses on a new problem involving robotic parallel disassembly sequence planning for EOL products and establishes a corresponding multiobjective model to minimize the makespan and energy consumption. To obtain high-quality disassembly schemes, a discrete artificial bee colony algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by solving 32 benchmark problems and a comparison with three well-known multiobjective algorithms. The proposed model and method are applied to a real-world LCD TV disassembly case, and multiple preferred disassembly schemes are obtained. The results show that the proposed model and method can effectively shorten the makespan and reduce energy consumption.Note to Practitioners—Parallel disassembly is one of the most efficient disassembly methods, and robotic disassembly will be one of the main disassembly methods in the future. Therefore, this article focuses on the robotic parallel disassembly planning to achieve an efficient and environmentally friendly treatment of end-of-life products. With the importance of production efficiency and energy saving, it is essential to simultaneously assess the makespan and energy consumption. The results demonstrate that the proposed artificial bee colony algorithm can provide multiple disassembly schemes that balance the makespan and energy consumption. The obtained disassembly schemes can provide references for the disassembly planning of the disassembly enterprise and expand the decision-making space for decision-makers.

Series elastic actuator (SEA) is a promising compliance device due to its lower output mechanical impedance, and it is widely applied to ensure safe human–robot interaction. Although some efforts have been made to achieve accurate stiffness tracking, the time-delay issue in SEA control has still not been well investigated. However, the time delay can cause an inaccurate response and increase the risk of injury. To overcome this problem, this article proposes a fast-response admittance control method for SEAs. First, an admittance control scheme considering the external force estimation is developed for a hydraulic SEA. Then, a parallel adaptive time-series (ATS) (P-ATS) compensator is proposed and further adopted in the admittance control scheme to compensate for the time delay and tracking error. The P-ATS compensator is a modification of the ATS compensator, which is enhanced with a unique parallel mechanism. Such a mechanism can save more computational resources on locating better parameters the for P-ATS compensator, thus improving its performance. Moreover, the parameter setting is converted to an optimization task, which is solved by the whale swarm algorithm (WSA) to achieve higher accuracy. The newly located parameters are compared to the current parameters based on a proposed evaluation criterion, thus guaranteeing the quality of the updated parameters. All the above strategies are employed to improve the SEA admittance control performance. The results obtained from both simulation and real-world experiments validate that, compared to conventional methods, the proposed method achieves a better performance in SEA stiffness tracking with lower time delay and tracking error.Note to Practitioners—Accurate stiffness tracking of SEAs can achieve safe human–robot interaction. However, the time delays introduced by the imprecise movement and estimation of external force can lead to inaccurate actuator response that may limit the capacity of safety insurance. To overcome this issue, a fast-response admittance control method is proposed for SEAs by adopting a novel P-ATS compensator. Thus, the time delays and errors from both load movement and external force estimation can be adaptively compensated. Several strategies have been adopted to enhance the compensator for parameter determination to achieve better performance. The proposed method requires no additional previous information about the system except load mass and spring stiffness, which makes it easy to implement for different types of SEAs. Experimental results show that the proposed method can achieve faster and more accurate stiffness tracking under different conditions. Future work aims to address the control problem under random disturbances and apply the proposed method to human–robot collaboration tasks to further test its performance.

To achieve sustainable manufacturing of large-scale end-of-life products, disassembly for recycling and remanufacturing has been widely adopted by industries. Disassembly line balancing becomes an important and challenging issue. The disassembly efficiencies of workers are different in the actual disassembly line due to some factors, including disassembly environment, skill level, work enthusiasm, etc. However, efficiency differences are often ignored in previous studies, which ultimately lead to unbalanced workloads among stations. Therefore, this article establishes a disassembly line balancing model that considers workers with different efficiencies and introduces the bucket brigade model into the disassembly line. Its optimization objectives include workload smoothness, cost of workers, disassembly risk, and disassembly demand. To obtain high-quality solutions, a discrete flower pollination algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by comparing it with 11 algorithms. Finally, the proposed model and algorithm are applied to an actual television disassembly case considering workers with different efficiencies, and provide decision makers with multiple disassembly schemes.

Disassembly lines are the most effective way to address large-scale value recovery from end-of-life (EOL) products. Disassembly line balancing (DLB) greatly affects the economics and throughput of EOL product processing. Complete disassembly is generally not suitable for disassembly enterprises; most often, the maximum profit is realized through partial disassembly. Thus, this article proposes a partial disassembly method and establishes a new DLB model that addresses both economic benefits and environmental impacts. The objective of the model is to maximize the effectiveness of workers, increase profit, reduce energy consumption, and balance the loads of workers. Moreover, the model considers the impact of disassembly face and tool changes on the disassembly process. A discrete multiobjective artificial bee colony (MOABC) algorithm is developed, and it takes the precedence constraints into account to obtain the Pareto solutions. The MOABC algorithm is applied to the disassembly lines of two real-world EOL products, including those of an LCD TV and a refrigerator. Experiments show that the performance of the MOABC algorithm is better than those of five well-known multiobjective algorithms. The proposed model and method can provide multiple disassembly schemes for decision makers of disassembly enterprises based on their preferences.

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

Complex equipment is important in industry and fault diagnosis is a key technology for maintenance. Convolutional neural network (CNN) is a common manner for fault diagnosis. But due to the imbalanced data, it cannot be applied to complex equipment directly. Generally, CNNs require balanced data, and imbalanced data will mislead the models and ignore actual faults. In complex equipment, since fault occurs rarely, the fault data are imbalanced. This impedes the application of CNNs greatly. To overcome this problem, a hierarchical training-CNN is proposed in this article. The proposed method uses an effective number-resampling to balance fault data, which avoids invalid samples, and introduces a magnet-loss pretraining to address the overlap of features between different faults. Based on these improvements, the proposed method achieves good diagnosis performances with an average accuracy of 96.56%, and has been developed into a real-world case successfully with an accuracy of 94.28%.

Surface defect detection is the essential process to ensure the quality of products. Surface defect classification (SDC) based on deep learning (DL) has shown its great potential. However, the well-trained SDC model usually requires large training data, and the small intraclass differences between the defect and normal samples also degrades the performance of SDC model. To overcome these drawbacks, this article proposed a new cycle-consistent adversarial networks with attention mechanism (AttenCGAN). First, AttenCGAN is used for synthesizing defect samples to enlarge the samples volume. Second, the attention mechanism is adopted for the feature enhancement by finding the discriminative parts of the samples and enlarging the differences among the samples. AttenCGAN is tested on KolektorSDD and DAGM2007 datasets, and its accuracies are 98.53% and 99.57% with only a few samples. The experiment results show that AttenCGAN outperforms other published SDC methods based on DL and machine learning, which validates its potential.

Evolutionary algorithms and swarm intelligence algorithms have been widely used for constrained optimization problems for decades and numerous techniques for constraint handling have been proposed. The${\varepsilon }$-constrained method is a very effective one. In the literature, the${\varepsilon }$value was usually controlled via an exponential function, which is not competent for solving certain types of constrained optimization problems, e.g., whose global optima are located near the boundary of the feasible and infeasible regions. To solve this problem, this article proposes a new adaptive${\varepsilon }$control method and incorporate it into a basic differential evolution (DE) algorithm: (DE/rand/1/exp). Based on the information of constraint violation in the current population, the adaptive method controls the value of${\varepsilon }$through a simple heuristic rule. Compared with the traditional exponential function-based control methods, the proposed adaptive method can prevent the algorithm from being trapped into local optima while retaining the obtained near-optimal candidate solutions in the infeasible region for generating promising searching paths. Besides, we set the crossover rate (CR) as a more reasonable value for DE/rand/1/exp, which can enhance the efficiency significantly. The well-known 2006 IEEE Congress on Evolutionary Computation (CEC 2006) competition on real-parameter single-objective constrained optimization benchmark is adopted to evaluate the effectiveness of the proposed adaptive${\varepsilon }$-constrained DE. Fifteen constrained engineering optimization problems are collected from the literature to test the proposed algorithm. Moreover, the adaptive${\varepsilon }$control method is extended to an adaptive algorithm to solve the benchmark problems from CEC 2017. The comparison results confirm the superiority of the proposed method.

A flexible job shop scheduling problem with lot-streaming (LSFJSP) is studied in this work. Lot-streaming means dividing all jobs in an order into several independent sublots and each sublot contains a certain number of jobs from the order. A discrete grey wolf optimizer (DGWO) is proposed for solving LSFJSP. In the algorithm, an encoding method with two strings including task string and sublot string is designed based on the characteristics of LSFJSP. And a positive decoding method combined with the rules of first come first served (FCFS) and shortest process time (SPT) is developed to reduce the solution space. Several strategies including population guide mechanism, critical task local search, and wolf random walk are adopted in the DGWO to make the algorithm suitable to solve LSFJSP. The results of computational experiments on 120 instances show that the DGWO is an competitive algorithm for LSFJSP.

Due to the complementarity of the process planning and shop scheduling, their integration can greatly facilitate the development of the intelligent manufacturing system. In the last decade, the integrated process planning and scheduling (IPPS) problem has become a research hotspot in the manufacturing system area. It is an NP-hard problem and is more complicated than the job shop scheduling problem. Although some progress has been obtained in the IPPS field, there are still many unsolved open problems. In this article, the novel integrated encoding and decoding methods are proposed by considering the OR-node of the process network graph. Moreover, a modified genetic algorithm (MGA) is designed based on the proposed coding methods. The process planning and the scheduling parts can be represented simultaneously in one individual. As for the precedence constraints between operations, the specifically designed operators are able to guarantee the feasibility of the operation sequence during the searching procedure. Then, the superiority of MGA is verified by updating nine new records on 37 well-known open problems, four of them reach their lower bounds. In addition, the proposed algorithm is also tested on a real-world case from a nonstandard equipment workshop in a Chinese machine tool company, which produces a common module of a packaging machine. The results show that the proposed MGA can solve the real-world case better than the comparative algorithms.

Evolutionary algorithms via decomposition, namely, DEAs, decompose the original challenging problem and evolve a number of subproblems/subspaces concurrently in a cooperative fashion. Adaptive computational resource allocation (CRA) strategy is able to identify the efficiency of different subspaces and invest search effort on them accordingly in an online manner. A crucial issue for CRA is to measure the efficiency of subspaces. Unfortunately, existing approaches for efficiency measurement are either fitness improvement oriented or contribution oriented, which struggle to capture the potentials of subspaces accurately. To mitigate such drawback, we present an ensemble method for CRA, based on the recent fitness contribution rates (FCRs) and fitness improvement rates (FIRs) of subspaces simultaneously. In order to dynamically track the potential of each subregion, we adopt two memory matrices to record FIR and FCR for multiple subspaces over recent generations, respectively. Afterward, an aptitude vector indicating the potentials of subspaces is defined by exploiting FCR and FIR with memory and decaying scheme. On the basis of above strategies, an ensemble CRA (ECRA) scheme is designed, which is then embedded into an adaptive objective space partition-based DEA, termed ECRA-DEA, for solving the multi/many-objective optimization. Extensive experimental studies for ECRA-DEA on various types of challenging problems have been carried out and the results confirm that ECRA is effective. Besides, the competence of ECRA-DEA is empirically validated in comparison with state-of-the-art designs. The proposed ECRA paves a new way to leverage the capability of DEAs on handling complex problems.

In vision-based defect recognition, deep learning (DL) is a research hotspot. However, DL is sensitive to image quality, and it is hard to collect enough high-quality defect images. The low-quality images usually lose some useful information and may mislead the DL methods into poor results. To overcome this problem, this article proposes a generative adversarial network (GAN)-based DL method for low-quality defect image recognition. A GAN is used to reconstruct the low-quality defect images, and a VGG16 network is built to recognize the reconstructed images. The experimental results under low-quality defect images show that the proposed method achieves very good performances, which has accuracies of 95.53-99.62% with different masks and noises, and they are improved greatly compared with the other methods. Furthermore, the results on PSNR, SSIM, cosine, and mutual information indicate that the quality of the reconstructed image is improved greatly, which is very helpful for defect analysis.

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

Engineering optimization problems usually involve computationally expensive simulations and many design variables. Solving such problems in an efficient manner is still a major challenge. In this paper, a generalized surrogate-assisted evolutionary algorithm is proposed to solve such high-dimensional expensive problems. The proposed algorithm is based on the optimization framework of the genetic algorithm (GA). This algorithm proposes to use a surrogate-based trust region local search method, a surrogate-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategy based on the expected improvement infilling criterion of a simplified Kriging in the optimization process. The SGA updating mechanism is a special characteristic of the proposed algorithm. This mechanism makes a fusion between surrogates and the evolutionary algorithm. The neighbor region partition strategy effectively retains the diversity of the population. Moreover, multiple surrogates used in the SGA updating mechanism make the proposed algorithm optimize robustly. The proposed algorithm is validated by testing several high-dimensional numerical benchmark problems with dimensions varying from 30 to 100, and an overall comparison is made between the proposed algorithm and other optimization algorithms. The results show that the proposed algorithm is very efficient and promising for optimizing high-dimensional expensive problems.

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance.

Differential evolution(DE) is a conventional algorithm with fast convergence speed. However, DE may be trapped in local optimal solution easily. Many researchers devote themselves to improving DE. In our previously work, whale swarm algorithm have shown its strong searching performance due to its niching based mutation strategy. Based on this fact, we propose a new DE algorithm called DE with Better and Nearest option (NbDE). In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms on nine classical benchmark test functions with different dimensions. The results show that NbDE outperforms other algorithms in convergence speed and accuracy.

Deep learning (DL) has been widely applied in the fault diagnosis field. However, the depth of DL models in fault diagnosis is very shallow compared with benchmark convolutional neural network (CNN) models for ImageNet. But it is hard to train a very deep CNN model without the large amount well-organized datasets like ImageNet. In this research, a new transfer learning based on pre-trained VGG-19 (TranVGG-19) is proposed for fault diagnosis. Firstly, a time-domain signals to RGB images conversion method is proposed. Then, the pre-trained VGG-19 is applied as feature extractor to obtained the features of converted images. Finally, a softmax classifier is trained on the features. The proposed TranVGG-19is tested on the famous motor bearing dataset from Case Western Reserve University. The final prediction accuracy of TCNN is 99.175% and the training time of TranVGG-19is only near 200 seconds. These results outperform many DL and machining learning methods.

Non-maximum suppression (NMS) is widely used in object detectors for removing imprecise detection boxes. However, NMS can easily discard a part of correct detection boxes when multiple objects are overlapped. To deal with this problem, some methods had been presented, but only for simple overlapping scenes. Therefore, this paper proposes an improved NMS approach to detect objects with high degree of overlap. This method divides all of detection boxes into different clusters to reduce the degree of overlap between boxes. These detection box scores in each cluster are decayed as a function of overlap and no boxes are discarded. The improved NMS is combined with two commonly used object detection networks, namely Faster Region-based Convolutional Neural Networks and Region-based Fully Convolutional Networks. A complex public dataset Microsoft Common Objects in Context is employed to evaluate the performance of the improved NMS. Experimental results show that two metrics average recall and localization performance are improved by the proposed method for these two famous detectors.

Distributed permutation flowshop scheduling problem (DPFSP) has become a very active research area in recent years. However, minimizing total flowtime in DPFSP, a very relevant and meaningful objective for today's dynamic manufacturing environment, has not captured much attention so far. In this paper, we address the DPFSP with total flowtime criterion. To suit the needs of different CPU time demands and solution quality, we present three constructive heuristics and four metaheuristics. The constructive heuristics are based on the well-known LR and NEH heuristics. The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems. We explore the problem-specific knowledge and accelerations to evaluate neighboring solutions for the considered problem. We introduce advanced and effective technologies like a referenced local search, a strategy to escape from local optima, and an enhanced intensive search method for the presented metaheuristics. A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out. The results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion.

Process planning and scheduling are modeled sequentially in the traditional manufacturing system. However, because of their complementarity, the increasing need to integrate them has emerged to enhance the manufacturing productivity significantly. Therefore, the integrated process planning and scheduling (IPPS) is becoming a hotspot in providing a blueprint for efficient manufacturing system. This paper proposes a novel algorithm hybridizing the genetic algorithm with strong global searching ability and variable neighborhood search with strong local searching ability for the IPPS problem. To improve the searching ability, a novel procedure, encoding method, and local search method have been designed. Effective operators have been adopted. Three experiments with totally 37 well-known benchmark problems are employed to evaluate the performance of the proposed method. Based on the results, the proposed algorithm outperforms the state-of-the-art methods and finds the new solutions (the best solutions found so far) for some problems. The proposed method has also been applied on a real-world case from a nonstandard equipment production workshop for the packaging machine of a machine tool company in China. The solution demonstrates that it can solve real-world cases very well.

Fault diagnosis plays an important role in modern industry. With the development of smart manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods have two shortcomings: 1) their performances depend on the good design of handcrafted features of data, but it is difficult to predesign these features and 2) they work well under a general assumption: the training data and testing data should be drawn from the same distribution, but this assumption fails in many engineering applications. Since deep learning (DL) can extract the hierarchical representation features of raw data, and transfer learning provides a good way to perform a learning task on the different but related distribution datasets, deep transfer learning (DTL) has been developed for fault diagnosis. In this paper, a new DTL method is proposed. It uses a three-layer sparse auto-encoder to extract the features of raw data, and applies the maximum mean discrepancy term to minimizing the discrepancy penalty between the features from training data and testing data. The proposed DTL is tested on the famous motor bearing dataset from the Case Western Reserve University. The results show a good improvement, and DTL achieves higher prediction accuracies on most experiments than DL. The prediction accuracy of DTL, which is as high as 99.82%, is better than the results of other algorithms, including deep belief network, sparse filter, artificial neural network, support vector machine and some other traditional methods. What is more, two additional analytical experiments are conducted. The results show that a good unlabeled third dataset may be helpful to DTL, and a good linear relationship between the final prediction accuracies and their standard deviations have been observed.

Welding, an irreplaceable process in the modern manufacturing industry, consumes enormous amounts of energy. The schedule in a welding shop greatly impacts both its energy consumption and productivity. Thus, it is of great significance to solve the welding shop scheduling problem (WSSP) considering both energy efficiency and productivity. In this paper, to solve a real-life WSSP, a multiobjective mathematical model is proposed and an effective multiobjective artificial bee colony algorithm (MOABC) is developed. The results of a designed numerical experiment indicate that the proposed MOABC performs better than Strength Pareto Evolutionary Algorithm 2 and Nondominated Sorting Genetic Algorithm II. Finally, the proposed model and MOABC algorithm are applied to solve a real-life girder WSSP of a Chinese crane company. The results also demonstrate that the proposed method can greatly reduce energy consumption and makespan compared to other algorithms.

This paper presents a path planning method based on Pythagorean Hodograph (PH) curves and harmony search algorithm for unmanned-aerial-vehicles (UAVs) in complex environments, especially in urban environment and mountainous circumstances. The flyable paths are tuned to meet spatial demands, satisfy kinematical and dynamic constraints of the UAVs, and guarantee no collision course between any two vehicles as well as obstacles. Then, a modified harmony search algorithm (MHS) is presented to solve the proposed path planning problem. Finally, two experiments are carried out to prove the feasibility.

In this paper, the combination of improved genetic algorithm (IGA) and rolling window technology is applied to solve the dynamic IPPS problem, and two kinds of disturbances are considered, which are the machine breakdown and new job arrival. To improve the search capability of IGA, special genetic operators are developed and adopted. To improve the local search performance of the proposed algorithm, the whole neighborhood based on random search is added in the mutation operation of IGA. The experiment which is adopted from some famous benchmark problems has been conducted to verify the performance of the proposed algorithm. The result show that the proposed method can solve the dynamic IPPS effectively and achieve satisfactory improvement.

Differential evolution (DE) is one of the best evolutionary algorithms (EAs). The effort of improving its performance has received great research attentions, such as adaptive DE (JADE). Based on the analysis on the aspects that may improve the performance of JADE, we introduce a modified JADE version with sorting crossover rate (CR). In JADE, CR values are generated based on mean value and Gaussian distribution. In the proposed algorithm, a smaller CR value is assigned to individual with better fitness value. Therefore, the components of the individuals, which have better fitness values, can appear in the offspring with higher possibility. In addition, the better offspring generated from last iteration are supposed to have better schemes, hence these schemes are preserved in next offspring generation procedure. This modified version is called as JADE algorithm with sorting CR (JADE_sort). The experiments results with several excellent algorithms show the effectiveness of JADE_sort.

Economic dispatch problem (EDP) is an important optimization problem in modern power system. When involved with valve-point loading effect, this problem becomes a complex non-convex and non-linear optimization problem. In this case, many researchers adopt evolutionary algorithms to solve it. An equality constraint, the power balance constraint, exists in this problem. This constraint leads some common constraint handling methods, such as feasibility rules and ε constraint method, to failure. In this paper, we propose a new constraint handling method which converts the power balance constraint into two boundary inequality constraints, and apply differential evolution (DE) combined with feasibility rules and ε constraint method to solve this problem. In order to prove its effectiveness, three benchmarks are tested. The numerical results show that with the help of the new transforming method, feasibility rules and ε constraint method perform very well in the three benchmarks. Compared with other algorithms in literatures, the simplest DE combined with ε constraint method also demonstrates competitiveness.

Hybrid flow shop scheduling problem (HFSP) is a complex combinatorial optimization problem in real world applications. Due to the complexity and importance, the HFSP has been studied intensively. This paper presents differential evolution algorithm with variable neighborhood (DEVNS) search to solve HFSP. Experiments have been conducted and comparison has been made with the state-of-the-art algorithms. Experimental results show that the DEVNS algorithm is highly competitive in solving the HFSP.

Photovoltaic (PV) and wind energies are renewable and clean, which can be alternative to fossil fuels. The PV/wind hybrid system optimal design problem is the hot topic nowadays. The aim of the optimal design is to develop an independent energy supply system with minimum total annual cost and loss power supply probability by determining the number of PV panels, wind turbines and backup batteries. This paper presents a discrete geometric selective harmony search (DGSHS) algorithm combined with Deb's constraint handling technique to tackle this problem. Optimal design under different requirements are investigated in this study and DGSHS are compared with 3 previous methods, namely discrete harmony search(DHS), discrete harmony search -based simulated annealing (DHSSA) and discrete chaotic harmony search-based simulated annealing (DCHSSA). The simulation results validate the superior performance of the DGSHS algorithm.

Process planning and scheduling are two important functions in modern manufacturing system. Considering their complementarity, integrating process planning and scheduling more tightly could improve the performance and productivity of the whole manufacturing system. Meanwhile, multi-objective optimization problem is widespread existing in practice. The decision maker always needs to make a trade-off between two or more objectives while determining a final schedule. In this paper, an improved genetic algorithm (IGA) with external archive maintenance is proposed to optimize the multi-objective integrated process planning and scheduling (IPPS) problem. IGA is utilized to search for the Pareto optimal solutions, while the external archive is used to store and maintain the generated non-dominated solutions during the optimization procedure. Three different scale instances have been employed to test the performance of the proposed algorithm. The experiment results show that the proposed algorithm has achieved satisfactory improvement.

In 5-axis flank milling, tool path planning is of great significance since the machining error can be systematically reduced by optimization of tool path planning. Therefore, various optimization methods for tool path planning have been developed. This paper proposes a simplified electromagnetism-like mechanism (EM) algorithm to optimize the tool path planning. Based on the experimental results, we can see that the proposed method owns excellent performance compared with original EM (OEM) algorithm and PSO algorithm. Moreover, we also study the parameter selection of EM algorithm according to different processing surface.

In multi-pass milling, the selection of machining parameters is of great significance since the parameters affect the production time, quality, cost, and some other process performance measures greatly. However, the parameters optimization of the multi-pass milling process is a nonlinear constrained optimization problem. It is very difficult to obtain the satisfactory solutions by the traditional optimization methods. Therefore, in this paper, a new optimization technique based on imperialist competitive algorithm (ICA) is proposed to solve the parameters optimization problem in multi-pass milling process. The ICA is a population based meta-heuristic algorithm for unconstrained optimization problems. To address the constraints efficiently, the proposed approach introduces two constraints handling techniques, which include the penalty function method and the constraints handling strategy of ICA. A case study is presented to verify the effectiveness of the proposed method. The results show that the proposed method is better than other algorithms and achieves significant improvement.

Process planning and scheduling are two important sub-systems in modern manufacturing system. In manufacturing system, the two sub-systems of process planning and scheduling have been treated sequentially or separately in traditional methods. To increase the effectiveness of system performance, there is an increasing need for deep research and application of integrated process planning and scheduling (IPPS) system. In this paper, a novel two-level genetic algorithm (TGA) is proposed to optimize the IPPS problem. Based on the previous work, deep research should be made on the IPPS problem. In this study, the flow chart of TGA based on the previous integrated optimization strategy has been proposed. Experiment studies have been conducted to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm for the IPPS is a promising and very effective method.

Process planning and scheduling are two important parts in intelligent manufacturing system and have great impacts on production efficiency. Integrate them can highly increase the production feasibility and optimality. Researchers have done a lot work on integration of process planning and scheduling (IPPS). But former researchers rarely focused on uncertain environment. In reality many factors can cause the uncertainty of production process time. This paper pioneers in choosing a better solution in uncertain manufacturing environment based on interval theory. The uncertain process time is modeled as interval number. And then, the completion time is also an interval number. Genetic Algorithm (GA) is used to solve this model. The feasibility and effectiveness of the solution have been taken into consideration. The experimental results obtained by increasing the scale of the problem illustrate the proposed method is stable and effective.

This paper proposes a novel Two-layer Hierarchical differential evolution (THDE) algorithm to improve the search ability of differential evolution (DE) algorithm. Individuals are separated into bottom layer and top layer. In the bottom layer, individuals are divided into several groups. Modified DE/current-best/1/bin strategy is conducted to produce offspring, where the best individual comes from top layer. In the top layer, modified DE/rand/1/bin strategy is used to update individuals. A set of famous benchmark functions has been used to test and evaluate the performance of the proposed THDE. The experimental results show that the proposed algorithm is better than DE/current-best/1/bin and DE/rand/1/bin and better than or at least comparable to the self-adaptive DE (JDE) and intersect mutation differential evolution algorithm (IMDE) for most functions.

Surface roughness has a great influence on the product properties. Predicting the surface roughness is an important work for modern manufacturing industry. In this paper, a novel prediction method called Free Pattern Search (FPS) is proposed to explicitly construct the surface roughness prediction model. FPS takes the advantage of the expression tree in gene expression programming (GEP) to encode the solution and to expresses a non-determinative tree using a fixed length individual. FPS is inspired by Pattern Search (PS) and hybrid a scatter manipulator to keep the diversity of the population. Three machining parameters, the spindle speed, feed rate and the depth of cut are used as the independent input variables when prediction the surface roughness in end milling. Experiments are conducted to verify the performance of FPS and FPS obtains good results compared with other algorithm. The predictive model found by FPS agrees with the experimental result. The variable relations are also showed in the predictive model, and the results shows that they are fit to the experiments well.

Cutting forces is one of the most fundamental elements that affect the performance of cutting operation. Finding the rules that how process and environment factors affect the values of cutting forces will help to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting forces is impacted by different machining parameters and the inherent uncertainties in the machining process, how to predict the cutting forces becomes a challengeable problem for the researchers and engineers. Gene Expression Programming (GEP) combines the advantages of the genetic algorithm (GA) and genetic programming (GP), and has been successfully applied in function mining and formula finding, so it should be suitable to solve the above problem. In this paper, a method based on GEP has been proposed to construct the prediction model of cutting forces in a face-milling operation. At the basis of defining a GEP environment for the problem and improving the method of constant creation, an explicit prediction model of cutting forces has been constructed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted to compare this approach with some previous works. The obtained results show that the constructed prediction model fits very well with the experimental data, and can be used to estimate the cutting forces and optimize the cutting parameters. The proposed method will lead to the reduction in production costs and production time, and improvement of product quality.

In 21st century, reducing energy consumptions of machining system is a developing trend in the manufacturing industry and is important for the implementation of “Green Manufacturing”. Dynamic scheduling problem in FMS have historically emphasized the schedule efficiency. This paper proposes an innovative approach to study the dynamic scheduling problem in FMS, taking the objectives of minimum or maximum energy consumption into account. A new goal programming mathematical model is presented for solving this problem. This model considers the energy consumption and the schedule efficiency simultaneously. Several scheduling case are designed to test the performance of the proposed model. The experimental results show that the proposed goal programming mathematical model can save the energy consumption significantly and solve the dynamic scheduling problem in FMS very well. These results also show that the minimum energy consumption and the minimum schedule efficiency can be obtained simultaneously on the appearance of the dynamic events.

Flexible job shop scheduling problem (FJSP) is a very important problem in the modern manufacturing system. It is an extension of the classical job shop scheduling problem. It allows an operation to be processed by any machine from a given set. It is also a NP-hard problem. This paper proposes a multi-swarm collaborative evolutionary algorithm (MSCEA) to solve FJSP. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of FJSP.

Flexible job shop scheduling problem (FJSP) is a very important problem in the modern manufacturing system. It is an extension of the classical job shop scheduling problem. Because of the importance of FJSP and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective FJSP. This paper proposes a collaborative evolutionary algorithm (CEA) based on Pareto optimality to solve the multi-objective FJSP. Experimental studies have been used to test the approach. And the experimental results show that the proposed approach is a promising and very effective method on the research of multi-objective FJSP.

Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, a Multi-agent-based approach has been developed to facilitate the integration of the two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, an experimental study has been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement.