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A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain management

IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第9期23卷 16410-16420页

作者： Ren, Lei Fan, Xiaoyang Cui, Jin Shen, Zhen Lv, Yisheng Xiong, Gang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100191 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Beihang Univ Ningbo Inst Technol Ningbo 315800 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China

In the modern supply chain system, large-scale transportation tasks require the collab.rative work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle route planning was mainly implemented by heuristic algorithms. However, these algorithms face the dilemma of long computation time. In recent years, some machine learning-based methods are also proposed for vehicle route planning, but the existing algorithms can hardly solve multi-vehicle time-sensitive problems. To overcome this problem, we propose a novel multi-agent reinforcement learning model, which optimizes the route length and the vehicle's arrival time simultaneously. The model is based on the encoder-decoder framework. The encoder mines the relationship between the customer nodes in the problem, and the decoder generates the route of each vehicle iteratively. Specially, we design multiple route recorders to extract the route history information of vehicles and realize the communication between them. In the inferring phase, the model could immediately generate routes for all vehicles in a new instance. To further improve the performance of the model, we devise a multi-sampling strategy and obtain the balance boundary between computation time and performance improvement. In addition, we propose a simulation-based vehicle configuration method to select the optimal number of vehicles in real applications. For validation, we conduct a series of experiments on problems with different customer amounts and various vehicle numbers. The results show that the proposed model outperforms other typical algorithms in both performance and calculation time.

关键词： Reinforcement learning Costs Task analysis Transportation Optimization Computational modeling Vehicle routing Vehicle routing supply chain management multi-agent reinforcement learning (MARL) route recorder

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A Kind of Change management Method for Global Value Chain Optimization and Its Case Study

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第4期9卷 1060-1074页

作者： Xiong, Guangyu Wu, Huaiyu Helo, Petri Shang, Xiuqin Xiong, Gang Qin, Rui Wang, Fei-Yue Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Vaasa Sch Technol & Innovat Networked Value Syst Res Grp Vaasa 65280 Finland Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 100190 Peoples R China

Any successful change in an organization requires an appropriate change management method and a process for involved staff and department to accept the change and become engaged in order to achieve its success. It is even more important and difficult to adopt a novel change management method to bring multiple organizations across the business value chain into the change implementation. This research does not focused on change management within a single organization but rather emphasizes a change management method, including an appropriate change framework, well-defined critical success factors (CSFs), and related tools for implementing change in multiple organizations. This article introduces one kind of change management method to support a process change through global value chain (GVC) in multiple organizations, and the method is used in a case study to achieve a successful change. In order to succeed in optimizing GVC performance, this research applies the proposed change management method to the case GVC, to support technical change by obtaining the staff's full commitment and engagement. The achieved results from the case study prove that successful change comes not only through technical solutions implemented in the problem process throughout the GVC but also through strong support and engagement from all organizations and involved staff. The proposed change management method not only helped the case GVC to implement change successfully but also can help the relevant multiple organizations to improve the GVC performance and add value by optimizing their problem process.

关键词： Organizations Tools Automation Teamwork Manufacturing Leadership control systems Change framework change management critical success factors (CSFs) global value chain (GVC)

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Transportation 5.0: The DAO to Safe, Secure, and Sustainable intelligent Transportation systems

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2023年第10期24卷 10262-10278页

作者： Wang, Fei-Yue Lin, Yilun Ioannou, Petros A. Vlacic, Ljubo Liu, Xiaoming Eskandarian, Azim Lv, Yisheng Na, Xiaoxiang Cebon, David Ma, Jiaqi Li, Lingxi Olaverri-Monreal, Cristina Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Urban Comp Lab Shanghai 200232 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Univ Southern Calif Dept Elect Engn Syst Los Angeles CA 90007 USA Griffith Univ Inst Intelligent & Integrated Syst Nathan Qld 4111 Australia Griffith Univ Sch Engn & Built Environm Nathan Qld 4111 Australia Beijing Univ Technol Coll Metropolitan Transportat Beijing 100081 Peoples R China Dept Mech Engn Virginia Tech Blacksburg VA 24061 USA Chinese Acad Sci Inst Automat Beijing 100045 Peoples R China Univ Cambridge Dept Engn Cambridge CB2 1TN England Univ Calif Los Angeles Samueli Sch Engn Los Angeles CA 90095 USA Indiana Univ Purdue Univ Indianapolis IUPUI Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA Johannes Kepler Univ Linz Dept Intelligent Transportat Syst A-4040 Linz Austria

In 2014, IEEE intelligent Transportation systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

关键词： intelligent transportation systems cyber-physical-social systems parallel intelligence knowledge automation DAOs transportation 5.0

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Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model

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IEEE SIGNAL PROCESSING LETTERS 2020年第0期27卷 196-200页

作者： Shen, Tianyu Gou, Chao Wang, Jiangong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

Automatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-level annotations. The two tasks are usually studied separately despite the latter problem relies on the former. Motivated by the close correlation between them, we propose a mixed-supervision guided method and a residual-aided classification U-Net model (ResCU-Net) for joint segmentation and benign-malignant classification. By coupling the strong supervision in the form of segmentation mask and weak supervision in the form of benign-malignant lab.l through a simple annotation procedure, our method efficiently segments tumor regions while simultaneously predicting a discriminative map for identifying the benign-malignant types of tumors. Our network, ResCU-Net, extends U-Net by incorporating the residual module and the SegNet architecture to exploit multilevel information for achieving improved tissue identification. With experiments on a public mammogram database of INbreast, we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art models.

关键词： Mixed-supervision deep learning segmentation and classification mammogram

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Research and practice of lightweight digital twin speeding up the implementation of flexible manufacturing systems 1

Research and practice of lightweight digital twin speeding u...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhang, Xiaodong Hu, Bin Xiong, Gang Liu, Xinjie Dong, Xisong Li, Dongjia School of Information Engineering Yantai Institute of Technology Yantai China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Yantai University Department of Mechanical and Electrical Engineering Yantai China School of Artificial Intelligence Yantai Institute of Technology Yantai China

ISBN: (纸本)9781665433372

Parallel manufacturing in Industry 5.0 requires digital twin to digitize physical systems, building virtual models to open up channels connecting physical systems, information systems, and social systems, and transforming the physical models of the existing production environment to achieve two-way feedback of virtual and real is the current research direction. This paper proposes the modeling idea of lightweight digital twin, extracts core dimensions and performs digital virtual simulation, so as to quickly realize the complete process of two-way feedback, and realize a set of chess flexible parallel manufacturing production lines as a practice for the design of complete lightweight digital twin. © 2021 IEEE.

关键词： Industrial research

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Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

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ENERGY AND BUILDINGS 2022年 269卷

作者： Zhang, Hanyuan Li, Chengdong Wei, Qinglai Zhang, Yunchu Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the air handling unit (AHU) system's time-varying dynamic properties. However, since the derived slow features are the linear combinations of all original variables, the conventional SFA based methods may suffer from poor model interpretability and result in inaccurate fault detection and diagnosis (FDD) performance. To enhance the dynamic AHU system FDD effectiveness, this paper presents a novel feature sparse representation based SFA algorithm through imposing the sparsity on the slow features, which is called the sparse three-way data based dynamic SFA (STBDSFA). We make two contributions to propose the STBDSFA approach. One contribution is to infuse the feature sparse representation technique into the constructed SFA based monitoring model by performing the sparse restriction on the loading vector, which can effectively eliminate the meaningless variables' coupling and select the key variable responsible for the fault detection. Before building the STBDSFA algorithm, the other contribution is to construct a new three-way data based dynamic SFA (TBDSFA) monitoring model to handle the AHU system's two-directional dynamic properties. In the established TBDSFA model, the auto-regressive moving average exogenous (ARMAE) model is first adopted to reveal the variables' auto-correlation relationships. Then, the multiway data analysis is applied to figure out the batch-wise dynamic property in multiple batch runs. After that, the dynamic SFA model is further set up to sufficiently tackle the time-wise dynamic nature within a batch run. To effectively diagnose the pattern of detected fault, another innovative work is to develop a long short-term memory (LSTM) based fault identification approach to classify the sparse slow features of fault snapshot dataset, owning to the LSTM's superiority of coping with the dynamic time-varying nature of the sparse slow features. By comparing with so

关键词： fault detection and diagnosis air handling unit feature sparse representation slow feature analysis long short-term memory (LSTM) network

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization

Towards Integrated Traffic Control with Operating Decentrali...

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International Conference on intelligent Transportation

作者： Shengyue Yao Jingru Yu Yi Yu Jia Xu Xingyuan Dai Honghai Li Fei-Yue Wang Yilun Lin Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalab.lity simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization

arXiv

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arXiv 2023年

作者： Yao, Shengyue Yu, Jingru Yu, Yi Xu, Jia Dai, Xingyuan Li, Honghai Wang, Fei-Yue Lin, Yilun Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group The Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

关键词： Integrated control

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Compliant Peg-in-Hole Assembly for Components with Grooves Based on Attractive Region in Environment 6

Compliant Peg-in-Hole Assembly for Components with Grooves B...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Liu, Yang Chen, Ziyu Zhang, Xiaodong Gao, Jie Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Key Lab Intelligent Space Robot Syst Tech Beijing 100094 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9780738133645

The compliant peg-in-hole assembly method based on Attractive Region in Environment has achieved great performance in high-precision assembly for convex components. However, for nonconvex parts, there may be local minima in the constraint region, and jamming in the assembly process may arise by using the existing method. To solve the problem, this paper propose a compliant assembly method based on Attractive Region in Environment combined with geometric features. First, according to the geometric characteristics of the constraint region, sub-targets during the assembly process are designed. Then, low dimensional attractive regions are utilized to realize the phased directional movement and solve the jamming problem caused by the grooves. Furthermore, Impedance control is applied to guarantee the compliance of assembly. The experimental results of the nonconvex peg-in-hole assembly with clearance of 0.02 mm are presented and show the validity of the proposed method.

关键词： Mechatronics Shape Conferences Force Impedance Jamming Task analysis

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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