| 1. |
- Li, Junjie, et al.
(författare)
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A Novel Dry Selective Isotropic Atomic Layer Etching of SiGe for Manufacturing Vertical Nanowire Array with Diameter Less than 20 nm
- 2020
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Ingår i: Materials. - : MDPI AG. - 1996-1944. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- Semiconductor nanowires have great application prospects in field effect transistors and sensors. In this study, the process and challenges of manufacturing vertical SiGe/Si nanowire array by using the conventional lithography and novel dry atomic layer etching technology. The final results demonstrate that vertical nanowires with a diameter less than 20 nm can be obtained. The diameter of nanowires is adjustable with an accuracy error less than 0.3 nm. This technology provides a new way for advanced 3D transistors and sensors.
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| 2. |
- Liu, Yongkui, et al.
(författare)
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Logistics-involved service composition in a dynamic cloud manufacturing environment : A DDPG-based approach
- 2022
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 76, s. 102323-
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Tidskriftsartikel (refereegranskat)abstract
- Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm -Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.
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| 3. |
- Liang, Huagang, et al.
(författare)
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Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning
- 2021
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0736-5845 .- 1879-2537. ; 67
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers' complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep re-inforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service compo-sition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prior-itized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.
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| 4. |
- Liu, Yongkui, et al.
(författare)
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A framework for scheduling in cloud manufacturing with deep reinforcement learning
- 2019
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Ingår i: 2019 IEEE 17th International Conference on Industrial Informatics Aalto University (INDIN). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1775-1780
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Konferensbidrag (refereegranskat)abstract
- Cloud manufacturing is a novel service-oriented networked manufacturing paradigm that aims to provide on-demand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted meta-heuristic algorithm-based approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRL-based framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online single-task scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.
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| 5. |
- Liu, Yongkui, et al.
(författare)
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Industrial Internet for Manufacturing
- 2021
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 70
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Liu, Yongkui, et al.
(författare)
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Multi-agent-based scheduling in cloud manufacturing with dynamic task arrivals
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; , s. 953-960
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Konferensbidrag (refereegranskat)abstract
- Scheduling is a critical means for providing on-demand manufacturing services in cloud manufacturing. Multi-agent technologies provide an effective approach for addressing scheduling issues in cloud manufacturing, which, however, have rarely been used for solving the issue. This paper addresses scheduling issues in cloud manufacturing using multi-agent technologies. A multi-agent architecture for scheduling in cloud manufacturing is proposed firstly. Then, a corresponding multi-agent model is presented, which incorporates many-to-many negotiations based on an extended contract net protocol and takes into account dynamic task arrivals. Simulation results indicate the feasibility of the model and approach proposed.
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| 7. |
- Liu, Yongkui, et al.
(författare)
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Scheduling in cloud manufacturing : state-of-the-art and research challenges
- 2019
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Ingår i: International Journal of Production Research. - : TAYLOR & FRANCIS LTD. - 0020-7543 .- 1366-588X. ; 57:15-16, s. 4854-4879
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Forskningsöversikt (refereegranskat)abstract
- For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.
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| 8. |
- Liu, Yongkui, et al.
(författare)
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Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning
- 2023
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 80, s. 102454-
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distrib-uted robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algo-rithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.
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| 9. |
- Ping, Yaoyao, et al.
(författare)
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Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing Under Different Task Arrival Modes
- 2023
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Ingår i: Journal of manufacturing science and engineering. - : ASME International. - 1087-1357 .- 1528-8935. ; 145:8
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is a critical problem in cloud manufacturing. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to address the issue, which, however, either are incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) as the combination of deep learning (DL) and reinforcement learning (RL) provides an effective technique in this regard. In view of this, we employ a typical DRL algorithm-Deep Q-network (DQN)-and propose a DQN-based approach for multitask scheduling in cloud manufacturing. Three different task arrival modes-arriving at the same time, arriving in random batches, and arriving one by one sequentially-are considered. Four baseline methods including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time (min-time) scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is effective and performs best among all approaches in addressing the multitask scheduling problem in cloud manufacturing.
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| 10. |
- Ping, Yaoyao, et al.
(författare)
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Enterprise and service−level scheduling of robot production services in cloud manufacturing with deep reinforcement learning
- 2023
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Ingår i: Journal of Intelligent Manufacturing. - : Springer Nature. - 0956-5515 .- 1572-8145.
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Tidskriftsartikel (refereegranskat)abstract
- Cloud manufacturing is a manufacturing paradigm that integrates wide-area distributed manufacturing resources for distributed services over the Internet. Scheduling is a critical technique that determines the overall performance of a cloud manufacturing system. Robots are an important type of manufacturing resource in cloud manufacturing. Scheduling of robot production services is therefore an important research issue in cloud manufacturing. In cloud manufacturing, services can be selected at an enterprise level or a service level, which represents two types of ways of scheduling. Which way is better and how to select the optimal robot production services are issues that have rarely been considered. Recently, deep reinforcement learning (DRL) has been successfully applied to solving various scheduling problems from different fields. Given this, this paper investigates enterprise and service-level scheduling of robot production services in cloud manufacturing and explores the optimal ways and methods of scheduling with DRL. Deep Q-Networks (DQN) and its three modified algorithms, including Double DQN, Dueling DQN, and Average-DQN based on scheduling approaches are proposed. Effects of enterprise- and service-level robot production services selection methods in cloud manufacturing are studied. Comparative results indicate that overall the service-level selection method outperforms the enterprise-level method. The performance of the above-mentioned scheduling algorithms is further studied with the service-level selection method. Results indicate that the Average-DQN-based approach is able to generate scheduling solutions more efficiently and performs the best with respect to each metric.
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