다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The emergence of smart technologies (STs) is inducing significant transformation in logistics and transport nowadays. STs refer to the applications of artificial intelligence and data science technologies, such as machine learning, big data, to create cognitive awareness (autonomous) of an object with the support of information and communication technologies such as IoT and Blockchain. Currently, many applications of STs have demonstrated potential promise in enhancing the efficiency and effectiveness in various logistics operations and transportation systems. Further, these new advanced technologies create huge modelling challenges to traditional optimization approaches and thus create rich new research opportunities for developing new optimization methodologies in the field of logistics and transport studies. As such, our aim is to conduct a comprehensive review on noteworthy contributions made in the applications of STs in improving logistics operations and transportation network efficiency. More importantly, we explore and discuss the technical difficulties encountered by researchers in the development of optimization methodologies caused by the applications of STs. Finally, we conclude the studies with suggestions for future research.

Applications of smart technologies in logistics and transport: A review

To facilitate the personalized smart manufacturing paradigm with cognitive automation capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by offering an adaptive and flexible solution. DRL takes the advantages of both Deep Neural Networks (DNN) and Reinforcement Learning (RL), by embracing the power of representation learning, to make precise and fast decisions when facing dynamic and complex situations. Ever since the first paper of DRL was published in 2013, its applications have sprung up across the manufacturing field with exponential publication growth year by year. However, there still lacks any comprehensive review of the DRL in the field of smart manufacturing. To fill this gap, a systematic review process was conducted, with 261 relevant publications selected to date (20-Oct-2022), to gain a holistic understanding of the development, application, and challenges of DRL in smart manufacturing along the whole engineering lifecycle. First, the concept and development of DRL are summarized. Then, the typical DRL applications are analyzed in the four engineering lifecycle stages: design, manufacturing, distribution, and maintenance. Finally, the challenges and future directions are illustrated, especially emerging DRL-related technologies and solutions that can improve the manufacturing system’s deployment feasibility, cognitive capability, and learning efficiency, respectively. It is expected that this work can provide an insightful guide to the research of DRL in the smart manufacturing field and shed light on its future perspectives. 

Deep reinforcement learning in smart manufacturing: A review and prospects

A survey on decision-making based on system reliability in the context of Industry 4.0

Smart manufacturing scheduling: A literature review

Smart manufacturing that involves tight integration of the physical system and cyber system is a hot topic in both industry and academia in the era of the Internet and big data. However, the dynami...

Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing

A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

The fault diagnosis problem of prognostics and health management (PHM) is becoming more complicated and challenging due to the increasing demand for timeliness and adaptability under dynamic working conditions. This article studies a new adaptive fault diagnosis (AdaFD) scheme and proposes a deep learning-based model and algorithm to enhance both the timeliness of fault diagnosis and its adaptability to dynamic conditions. The primary AdaFD model is based on a convolutional neural network (CNN). The two key modules of AdaFD are: 1) adaptive and independent learning rate design for model training and 2) adaptive structure optimization approach for model application. During the training phase, we design an adaptive learning rate strategy by distinguishing the weight and bias parameters in the CNN model to accelerate the training process. In the application phase, the adaptive batch normalization (AdaBN) algorithm is adopted to improve the adaptability of the primary CNN model to dynamic working conditions and data noise. Finally, a case study is conducted to validate the superiority of AdaFD based on a benchmark rolling bearing dataset. The results show that the proposed fault diagnosis method has higher accuracy and better adaptability than other common methods and possesses advantages in terms of training efficiency.

A Novel Fault Diagnosis Method Under Dynamic Working Conditions Based on a CNN With an Adaptive Learning Rate

In today's digital and information-based manufacturing environment, data are basic elements for almost every production control and management activity. This paper focuses on production data processing based on attribute analysis. There are thousands of attributes in semiconductor manufacturing. However, some of them are irrelevant and/or redundant to some optimal production control and management issues. It is hard to decide which attributes should be considered as input references. Rational attribute selection may lead to an accurate scheduling strategy and finally exerts a positive impact on the performance of the whole production line. This is the motivation of this work. Its goal is to investigate which attributes play the key roles in the manufacturing scheduling according to a specific performance criterion. A genetic algorithm-based selection approach for feature production attributes is proposed. Its prediction accuracy is verified via a practical wafer production line.

Attribute selection algorithm of data-based scheduling strategy for semiconductor manufacturing

In the big data age, data-driven dynamic scheduling is an effective method for manufacturing systems. Through data mining, the acquired scheduling knowledge can guide production scheduling. But the knowledge may be invalid with the production scene change due to the scene relevance or timeliness of data. In order to ensure the validity of the knowledge, this paper studies the data-driven scheduling knowledge life-cycle management for the smart shop floor. It includes learning, application, online evaluation, and online updating of scheduling knowledge. Based on the combined scheduling rules, the extreme learning machine (ELM) is applied to learn knowledge. The quality control theory is used to evaluate the effectiveness of knowledge. And the online sequential extreme learning machine (OS-ELM) is adopted to update the knowledge. In the iterative update of the knowledge, the life-cycle management of the knowledge is implemented. This method is validated in minifab, a model of the semiconductor production line. The results show that the effectiveness of scheduling knowledge is improved and the performance of the smart shop floor is continuously optimized.

Data Driven Scheduling Knowledge Management for Smart Shop Floor

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