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Joint optimization of production loading and maintenance for production-storage systems

Reliability engineering and System Safety 2025年 262卷

作者： Liang, Jianyu Zhang, Xiaohong Zeng, Jianchao Shi, Guannan Niu, Huifang Institute for Big Data and Visual Computing North University of China Taiyuan030051 China School of Economics and Management Taiyuan University of Science and Technology Taiyuan030024 China Division of Industrial and System Engineering Taiyuan University of Science and Technology Taiyuan030024 China School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan030024 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

Production-storage systems with adjustable loads are widely used in industrial production applications;however, system failures will affect mission success probability (MSP). Most existing studies on optimal production loading for such systems normally focus on the effects of maintenance activities conducted after production system failures. By incorporating preventive maintenance (PM) activities into the production optimization process, sudden failures can be avoided, thereby enhancing the MSP. To address this challenge above problems, this study proposes a joint optimization strategy for production loading and PM to maximize the system MSP. At any decision-making point, the optimal PM schedule and load level for the next production cycle are determined based on the amount of product in storage. A mathematical model was developed to calculate the MSP while considering the effects of PM. The proposed strategy and model were validated using a cooling water supply system as a case study. The results showed that integrating PM improved the MSP of the load-adjustable system, achieving a maximum increase of 2.81 %. © 2025 Elsevier Ltd

关键词： Loading

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Viewing Pattern Assisted Proactive Partial Caching for 360-degree Videos in MEC Networks

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IEEE Internet of Things Journal 2025年

作者： Yin, Guoxiao Zhou, Xiaotian Zhang, Haixia Li, Dongyang Yuan, Dongfeng Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan China College of Oceanography and Space Informatics Qingdao266580 China Shandong University School of Qilu Transportation Jinan250002 China

Caching 360-degree videos at the network edge can reduce user content request latency and mitigate transmission congestion in backbone networks. Given the fact that user only views a part of content of 360-degree scope at any time, caching the entire video is resource inefficient. To address this, we focus a Multi-access Edge computing (MEC)-based 360-degree video service system, where the edge server only caches a portion of each video that is most likely falling in the Field of View (FoV) of users. To minimize the average video request latency of all users in the system, we formulate a large-scale 0-1 knapsack problem, which is NP-hard. To tackle it, we proposed a heuristic algorithm where the user viewing patterns extracted from the historical request information are taken into account. Specifically, we first design a cascading cache space allocation method to assign the total cache space of edge server to each segment of videos. After that, the original problem is decomposed into several small-scale yet individual tile caching subproblems with compressed solution space. Then, they are solved by using the dynamic programming algorithm with moderate complexity. To further enhance the caching performance, the PSO-based algorithm is designed to fine tune the parameters involved in the proposed caching algorithm. In addition, we introduce a content-based method to calculate the request probability of the newly generated videos. The effectiveness of the proposed algorithm is evaluated through simulations based on a real world dataset, where the results demonstrate a substantial improvement in both video request latency and cache hit rate compared to the benchmark methods. © 2014 IEEE.

关键词： Heuristic algorithms

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DSQN: Robust path planning of mobile robot based on deep spiking Q-network

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Neurocomputing 2025年 634卷

作者： Kumar, Aakash Zhang, Lei Bilal, Hazrat Wang, Shifeng Shaikh, Ali Muhammad Bo, Lu Rohra, Avinash Khalid, Alisha School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130013 China Zhongshan Institute of Changchun University of Science and Technology Zhongshan528437 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Department of Automation University of Science and Technology of China Hefei230026 China School of Computing Engineering and Physical Sciences University of the West of Scotland Scotland Paisley Campus United Kingdom

With the rapid advancement of science and technology, the field of mobile robot applications continues to expand, with path planning emerging as a fundamental yet challenging task. While traditional path planning techniques have developed into a relatively complete theoretical system, their performance in uncertain environments remains a critical area of research. To address this, we propose a novel Deep Spiking Q-Network (DSQN) algorithm that significantly enhances path planning performance by leveraging the unique advantages of spiking neural networks (SNNs). Unlike classic Q-learning and its contemporary variants, the DSQN algorithm integrates global and local information simultaneously, resulting in superior overall performance. As the third generation of neural networks, SNNs offer unparalleled robustness and energy efficiency by mimicking biological neural systems. By introducing spiking neurons into the conventional Deep Q-learning (DQN) framework, the DSQN algorithm overcomes key challenges in deep reinforcement learning (DRL), such as limited robustness and high energy consumption. The DSQN training process incorporates both surrogate gradient learning (SGL) and ANN-to-SNN conversion techniques, with SGL demonstrating remarkable effectiveness in mobile robot path planning tasks. Experimental results validate the practicality and efficiency of DSQN, showcasing improved performance across diverse test scenarios compared to the original DQN algorithm. These findings highlight the potential of DSQN to advance path planning in complex and uncertain environments, establishing it as a robust and energy-efficient solution for mobile robotics. © 2025 Elsevier B.V.

关键词： Deep reinforcement learning

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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IEEE Transactions on Services computing 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

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Adaptive ELM-Based Time-Constrained control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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Finite-time parameter self-learning disturbance rejection control of MSV: Methodology and validation

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Peng, Bo Gu, Nan Wang, Dan Peng, Zhouhua School of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Institute for Ocean Engineering Shenzhen lnternational Graduate School Tsinghua University Shenzhen518055 China School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia State Key Laboratory of Maritime Technology and Safety Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China

Dear Editor, This letter presents a finite-time online parameter self-learning disturbance rejection surge speed control method for a marine surface vehicle (MSV). Specifically, a finite-time parameter self-learning robust exact differentiator based (RED-based) observer is first employed for estimating the unknown hydrodynamic parameters and the unknown control input coefficient separately, ensuring the finitetime convergence for both. The environmental disturbances and the dynamic modeling errors are also estimated simultaneously. Subsequently, a self-learning disturbance rejection control law is designed based on the estimated information. Finally, Lyapunov method is applied to prove the stability of the system. Hardware-in-the-loop experiment verifies the effectiveness of the proposed control method. © 2014 Chinese Association of Automation.

关键词： Lyapunov methods

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PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport

arXiv

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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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Evolutionary dynamics of behavioral motivations for cooperation

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Nature Communications 2025年第1期16卷 1-12页

作者： Su, Qi Stewart, Alexander J. School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington IN United States

Human decision-making is shaped by underlying motivations, which reflect both subjective well-being and fundamental biological needs. Different needs are often prioritized and traded off against one another. Here we develop a theoretical framework to study the evolution of behavioral motivations, encompassing both philanthropic (cooperating after personal needs are met) and aspirational (cooperating to fulfill personal needs) motivations. Our findings show that when the ratio of benefits to costs for cooperation exceeds a critical threshold, individuals initially driven by aspirational motivations can transition to philanthropic motivations with a low reference point for cooperation, resulting in increased cooperation. Furthermore, the critical threshold depends on the structure of the underlying social network, with network modifications capable of reversing the evolutionary trajectory of motivations. Our results reveal the complex interplay between needs, motivations, social networks, and decision-making, offering insights into how evolution shapes not only cooperative behaviors but also the motivations behind them. © The Author(s) 2025.

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Study on the Construction of "One-Bill Coverage" Transport Service System for China-Laos Railway Multimodal Transport 11th

Study on the Construction of "One-Bill Coverage" Transport S...

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11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Du, Xinyi Zhang, Yaqin Wang, Li School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing andService Technologies Beijing100044 China Key Laboratory of Railway Industry for Operational Active Safety Assurance and Risk Prevention and Control Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

The "one-bill coverage" of China-Laos railway multimodal transport is an effective way to promote the growth of bilateral trade between China and Laos railways, and it is an inevitable requirement for the construction of a modern comprehensive transport system. In order to enhance the efficiency and economic benefits of cargo transport on China-Laos Railway, this paper combines the current situation of freight transport on China-Laos Railway and divides the life cycle of "one-bill coverage" of China-Laos Railway multimodal transport into five phases: order submission, domestic consolidation, cross-border transport, foreign evacuation and order signing, etc., and builds a "one-bill coverage" covering all the links, such as loading, transport, transshipment, warehousing, connecting, and customs clearance. This paper can provide a reference for the development of "one-bill coverage" transport service of China-Laos Railway multimodal transport. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Multimodal transportation

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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