Computers in Industry最新文献_第2页

Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases 评估区块链技术在建筑供应链中的技术效用：通过用例的多kpi决策支持方法

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.compind.2025.104429

Timothy O. Olawumi , Stephen Ojo , Saheed Toyin Muftaudeen , Acheme Okolobia Odeh , Taiwo Amoo

Blockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the technical suitability of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments.

区块链技术（BCT）通过解决与透明度、效率和可追溯性相关的长期挑战，在改变建筑供应链（CSCs）方面具有巨大潜力。本研究调查并开发了一个严格的、以kpi为中心的框架，系统地将b区块链的使能能力（ECs）映射到对CSC绩效至关重要的关键绩效指标（kpi）。通过内容分析与设计科学研究（DSR）相结合的混合方法，本文引入了一个基于网络的决策支持工具（DST）来指导利益相关者评估区块链在建设项目中的技术适用性。DST分为两个阶段：首先，通过结构化诊断评估区块链的适用性；其次，通过将选定的kpi与相关用例和ec相匹配，推荐“最适合”的区块链堆栈。通过模拟案例场景的验证证明了DST在支持早期阶段、基于技术的决策方面的健壮性，并推荐了针对用户定义kpi和用例定制的区块链解决方案。研究结果表明，BCT通过自动化、不可变数据共享、去中心化治理等方式，可以显著提高CSCs的绩效。通过弥合概念承诺和实际应用之间的差距，本研究为建筑行业的数字化转型提供了理论进步和可操作的见解。它为复杂的多利益相关者供应链环境中的技术采用和性能优化提供了可复制的决策支持架构。

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引用次数: 0

TROPICCAL: Multi-perspective trace clustering for IoT-enhanced processes 热带：物联网增强过程的多角度轨迹聚类

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2025-12-02 DOI: 10.1016/j.compind.2025.104419

Yannis Bertrand, Jochen De Weerdt, Estefanía Serral

Business processes (BPs) that are enhanced with Internet of Things (IoT) technology, such as smart manufacturing processes, leverage IoT devices like sensors to monitor and capture contextual data from the physical environments where processes are executed. While the execution of BPs is typically recorded in information systems as event logs, IoT-enhanced BPs also produce IoT data that can offer valuable contextual insights. However, existing process mining techniques, which typically focus on the control-flow perspective, often miss key insights into the dynamic interplay of process activity sequences and IoT data—such as how certain IoT readings may trigger or affect specific process activities. To address this gap, we propose TROPICCAL, a new technique for multi-perspective trace clustering that integrates three key perspectives: the control-flow perspective, the trace attribute data perspective, and the time series (TS) sensor data perspective. The main novelty of TROPICCAL is the analysis of so-called context events as part of the TS data perspective. These events mark process-significant happenings detected in the TS sensor data. Furthermore, in order to unravel more insights from the output of our technique, we propose approaches for cluster explainability based on permutation feature importance. We demonstrate the efficacy of our approach and compare it with the most related and advanced approaches from the literature using a real-world manufacturing use case. Expert evaluation through in-depth interviews reveals that TROPICCAL offers better insights into the multi-perspective variants of the process.

通过物联网（IoT）技术增强的业务流程（bp），例如智能制造流程，利用传感器等物联网设备从执行流程的物理环境中监控和捕获上下文数据。虽然bp的执行通常作为事件日志记录在信息系统中，但物联网增强的bp也会产生物联网数据，可以提供有价值的上下文见解。然而，现有的流程挖掘技术通常侧重于控制流的角度，往往错过了对流程活动序列和物联网数据之间动态相互作用的关键见解，例如某些物联网读数如何触发或影响特定的流程活动。为了解决这一差距，我们提出了一种新的多视角跟踪聚类技术tropical，它集成了三个关键视角：控制流视角、跟踪属性数据视角和时间序列（TS）传感器数据视角。tropical的主要新颖之处在于将所谓的上下文事件作为TS数据视角的一部分进行分析。这些事件标志着在TS传感器数据中检测到的重要进程事件。此外，为了从我们的技术输出中揭示更多的见解，我们提出了基于排列特征重要性的聚类可解释性方法。我们展示了我们的方法的有效性，并将其与文献中使用现实世界制造用例的最相关和最先进的方法进行了比较。通过深入访谈的专家评估表明，tropical提供了对该过程的多视角变体的更好见解。

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引用次数: 0

Ensemble reinforcement learning for optimizing the energy efficiency index in the thickening–dewatering process 用于浓缩-脱水过程能效指标优化的集成强化学习

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.compind.2025.104431

Runda Jia , Fengyang Jiang , Ranmeng Lin , Jun Zheng , Dakuo He , Feng Yu

The thickening–dewatering process is an important stage in mineral industrial production, and improving its energy efficiency by optimizing energy consumption is a key research direction. However, there is a scarcity of studies on comprehensive optimization strategies for this process. To address this gap and reduce the energy efficiency index (EEI) in thickening–dewatering operations, this paper introduces reinforcement learning (RL) to the process. Since RL methods are prone to falling into local optima, we combine ensemble learning (EL) with RL. Based on the soft actor–critic (SAC) algorithm, which performs well in scheduling problems, we propose the ensemble SAC (ESAC) algorithm. In ESAC, each actor interacts with the environment using its own parameter set, and only the actions that yield the highest rewards are used to update the parameters of all actors. A weighted global loss function is also designed to prevent overestimation of the value network. Results show that the ESAC algorithm clearly outperforms benchmark RL algorithms, with EL effectively improving exploration efficiency and decision quality of RL. A multi-strategy ensemble helps to avoid local optima and optimize decision-making. Furthermore, when applied to the thickening–dewatering process of a gold hydrometallurgical plant, ESAC reduced the EEI by 44.77% compared to manual operation and increased the average underflow concentration by 9.57%.

浓缩—脱水过程是矿产工业生产的重要环节，通过优化能耗来提高浓缩—脱水过程的能源效率是一个重要的研究方向。然而，针对这一过程的综合优化策略研究较少。为了解决这一差距并降低浓缩脱水操作中的能源效率指数（EEI），本文将强化学习（RL）引入到该过程中。由于强化学习方法容易陷入局部最优，我们将集成学习（EL）与强化学习相结合。基于软行为者评价（SAC）算法在调度问题中表现良好的特点，提出了集成行为者评价（SAC）算法。在ESAC中，每个参与者使用自己的参数集与环境交互，并且只有产生最高奖励的操作才用于更新所有参与者的参数。还设计了加权全局损失函数，以防止对价值网络的高估。结果表明，ESAC算法明显优于基准RL算法，EL有效地提高了RL的探索效率和决策质量。多策略集成有助于避免局部最优和优化决策。应用于某金湿法冶炼厂的浓缩—脱水工艺，与人工操作相比，ESAC的EEI降低了44.77%，平均底流浓度提高了9.57%。

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引用次数: 0

Incremental learning strategies for improved detection of unknown defects in wafer maps with limited samples 有限样本晶圆图中未知缺陷检测的增量学习策略

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.compind.2025.104432

Tianming Ni , Wen Jiang , Huaguo Liang , Xiaoqing Wen , Mu Nie

Accurate detection of a wide range of defect patterns on wafers is crucial for enhancing chip yield and ensuring the reliability of semiconductor manufacturing systems. As this process becomes increasingly complex, new types of defects — referred to as unknown defects — emerge on wafers. Traditional pattern recognition methods struggle in this setting because limited samples are insufficient to effectively train deep learning models. Moreover, these models are prone to catastrophic forgetting when incrementally trained on new defect classes. To address these challenges, this paper proposes a method termed Few-Shot Class Contrastive Incremental Learning (FCCIL) for unknown wafer map defect detection. FCCIL integrates a contrastive learning network for distinguishing novel defect types and an incremental learning model for dynamic knowledge updating—both designed to mitigate catastrophic forgetting, thereby enabling the detection of unknown defects in wafer maps with limited data. Experimental results demonstrate a 4% improvement in forgetting resistance over state-of-the-art approaches, confirming the effectiveness of FCCIL in real-world semiconductor manufacturing scenarios.

准确检测晶圆上的各种缺陷模式对于提高芯片产量和确保半导体制造系统的可靠性至关重要。随着这个过程变得越来越复杂，新的缺陷类型-被称为未知缺陷-出现在晶圆上。传统的模式识别方法在这种情况下很困难，因为有限的样本不足以有效地训练深度学习模型。此外，当对新的缺陷类进行增量训练时，这些模型容易发生灾难性的遗忘。为了解决这些问题，本文提出了一种用于未知晶圆图缺陷检测的方法，称为少射类对比增量学习（FCCIL）。FCCIL集成了用于区分新缺陷类型的对比学习网络和用于动态知识更新的增量学习模型，两者都旨在减轻灾难性遗忘，从而能够在有限数据的晶圆图中检测未知缺陷。实验结果表明，与最先进的方法相比，遗忘电阻提高了4%，证实了FCCIL在实际半导体制造场景中的有效性。

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引用次数: 0

A short-term integrated wind speed prediction system based on fuzzy set feature extraction and intelligent optimization 基于模糊集特征提取和智能优化的短期综合风速预测系统

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2025-12-09 DOI: 10.1016/j.compind.2025.104418

Yijun Geng , Jianzhou Wang , Jinze Li , Zhiwu Li

Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This system employs a convex subset partitioning approach using a triangular affiliation function for fuzzy feature extraction. By applying soft clustering to the subsets, constructing an affiliation matrix, and identifying clustering centers, the system introduces the concepts of inner and boundary domains. It subsequently calculates the distances from data points to the clustering centers by measuring both interclass and intraclass distances. This method updates the cluster centers using the membership matrix, generating optimal feature values. Building on this foundation, we use multiple machine learning methods to input the fuzzy features into the prediction model and integrate learning techniques to predict feature values. Because different datasets require different modeling approaches, the integrated weight-updating module was used to dynamically adjust model weights by setting a dual objective function to ensure the accuracy and stability of the prediction. The effectiveness of the proposed model in terms of prediction performance and generalization ability is demonstrated through an empirical analysis of data from the Penglai wind farm in China.

由于风力发电的持续增长和技术的进步，风能具有巨大的潜力。然而，风速的演变受多种因素复杂的相互作用影响，具有很强的变异性。风速演化的非线性和非平稳性会对整个电力系统产生相当大的影响。为了解决这一问题，我们提出了一种基于模糊特征提取的集成多帧风速预测系统。该系统采用凸子集划分方法，利用三角关联函数进行模糊特征提取。通过对子集进行软聚类，构造隶属矩阵，识别聚类中心，引入内域和边界域的概念。然后通过测量类间和类内距离来计算数据点到聚类中心的距离。该方法利用隶属矩阵更新聚类中心，生成最优特征值。在此基础上，我们使用多种机器学习方法将模糊特征输入到预测模型中，并整合学习技术来预测特征值。由于不同的数据集需要不同的建模方法，因此采用集成权值更新模块，通过设置双目标函数来动态调整模型权值，以保证预测的准确性和稳定性。通过对蓬莱风电场数据的实证分析，验证了该模型在预测性能和泛化能力方面的有效性。

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引用次数: 0

A metrological approach for Augmented Reality tooltip tracking assessment 增强现实工具提示跟踪评估的计量方法

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.compind.2025.104430

Federico Salerno, Luca Ulrich, Giacomo Maculotti, Sandro Moos, Gianfranco Genta, Enrico Vezzetti, Maurizio Galetto

Tracking systems are essential in various fields, such as health and manufacturing industries, enabling mapping between the real and digital worlds. Amongst others, Augmented Reality Tracking Systems (ARTS) are more recent and less explored. This work proposes a quantitative metrological methodology to evaluate ARTS tooltip tracking performance, facilitating benchmarking, parameter optimization, and system selection for specific tasks. A specific 3D-printed measuring artifact is proposed to guide tooltip positioning. Tracking accuracy and precision are estimated, highlighting the effects of influence factors. The methodology was tested with two commercial state-of-the-art ARTSs using marker-based tooltips, i.e., a Microsoft HoloLens 2 and a stereo camera system equipped with Intel RealSense SR305 cameras. Metrological characteristics are evaluated, and the Euclidean distance expanded uncertainty at a conventional 95% confidence level is estimated as

5.071 mm

for the HoloLens 2 and

6.800 mm

for the stereo system, resulting in a superior metrological performance of HoloLens 2 under the specified conditions. This study provides a standardized approach for quantitatively comparing AR tracking systems, offering valuable insights for optimizing their use in specific applications and, innovatively in the context of ARTS, associates measurement uncertainty with tracked distance values.

跟踪系统在健康和制造业等各个领域至关重要，可以实现真实世界和数字世界之间的映射。其中，增强现实跟踪系统（ARTS）是较新的，探索较少。这项工作提出了一种定量计量方法来评估ARTS工具提示跟踪性能，促进基准测试、参数优化和特定任务的系统选择。提出了一种特定的3d打印测量工件来指导工具提示定位。对跟踪精度和精度进行了估计，突出了影响因素的影响。该方法在两个商用最先进的arts上进行了测试，使用基于标记的工具提示，即微软HoloLens 2和配备英特尔RealSense SR305相机的立体相机系统。在常规95%置信水平下，HoloLens 2的欧氏距离扩展不确定度估计为5.071mm，而HoloLens 2的立体系统为6.800mm，从而使HoloLens 2在特定条件下具有优越的计量性能。本研究为定量比较AR跟踪系统提供了一种标准化的方法，为优化其在特定应用中的使用提供了有价值的见解，并在ARTS的背景下创新地将测量不确定性与跟踪距离值联系起来。

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引用次数: 0

Intelligent design of dimensions of reinforced concrete frame structure components using diffusion models 基于扩散模型的钢筋混凝土框架结构构件尺寸智能设计

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.compind.2025.104428

Yi Gu , Sizhong Qin , Wenjie Liao , Xinzheng Lu

Designing the component dimensions of reinforced concrete (RC) frame structures is a crucial aspect of structural design. However, the reliance on manual expertise results in low design efficiency and unstable quality. The use of heuristic optimization and artificial intelligence algorithms such as generative adversarial networks (GANs) and graph neural networks (GNNs) can enhance design quality and efficiency. However, heuristic optimization algorithms are slow, and the accuracy of GANs and GNNs is insufficient. Therefore, this study proposes a diffusion model-based method called frame-dimension diffusion for predicting the component dimensions in RC frame structures. By integrating multichannel masking and gradient-weighted correction, this model enhances the precision and robustness of the component dimension predictions for beams, columns, and slabs. Furthermore, a new dataset construction method is introduced that captures the key standard story features and seismic conditions to facilitate the learning process of the diffusion model. Through comprehensive experimental evaluations and case studies, the effectiveness of the proposed method has been demonstrated. Compared to heterogeneous GNNs, the prediction accuracy has improved by 33 %. Additionally, the inter-story drift ratio results align with engineer-designed specifications, and the material usage error is within 1 %.

钢筋混凝土框架结构构件尺寸设计是结构设计的一个重要方面。然而，对手工专业知识的依赖导致设计效率低，质量不稳定。使用启发式优化和人工智能算法，如生成对抗网络（gan）和图神经网络（gnn）可以提高设计质量和效率。然而，启发式优化算法速度慢，gan和gnn的精度不足。因此，本研究提出了一种基于扩散模型的框架尺寸扩散方法来预测钢筋混凝土框架结构的构件尺寸。通过集成多通道掩蔽和梯度加权校正，该模型提高了梁、柱和板构件尺寸预测的精度和鲁棒性。此外，引入了一种新的数据集构建方法，该方法捕获了关键的标准故事特征和地震条件，以促进扩散模型的学习过程。通过综合实验评价和案例分析，验证了该方法的有效性。与异质GNNs相比，预测精度提高了33 %。层间位移比计算结果符合工程师设计规范，材料使用误差在1 %以内。

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引用次数: 0

Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems 迈向可信赖的人工智能决策：知识和数据驱动的人工智能系统的生命周期视角

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-01-01 Epub Date: 2025-10-31 DOI: 10.1016/j.compind.2025.104409

Emiel Miedema, Sabine Waschull, Christos Emmanouilidis

Organisations increasingly use data-driven artificial intelligence (AI) systems in their decision-making processes. These AI systems may operate autonomously, support human decision-makers or increasingly act as collaborative team members. However, data-driven AI systems often function as black boxes, lacking interpretability. This poses a challenge in decision-making, as stakeholders involved in or impacted by the decision-making process frequently need to understand the rationale behind decisions. Moreover, data-driven AI systems operate without leveraging structured domain knowledge. As a result, data-driven AI systems may generate outputs that are misaligned with the decision context, objectives, or constraints, potentially leading to poor decisions or reduced trust in AI systems among users. Consequently, recent years have seen an increasing interest in integrating domain knowledge with data-driven AI. This is evident in neuro-symbolic AI, a subfield of AI that combines neural networks with symbolic AI. While this approach shows promise for enhancing the trustworthiness of AI systems in decision-making, the specific mechanisms by which domain knowledge integration contributes to dimensions of trustworthiness remain insufficiently explored. Therefore, this study reviews and integrates recent knowledge- and data-driven AI literature, along with relevant concepts for decision-making. Building on this foundation, it proposes a lifecycle framework for integrated knowledge- and data-driven AI systems for decision-making, and demonstrates its application through a healthcare application example. It further analyses the dimensions of trustworthiness for knowledge- and data-driven AI systems using the proposed lifecycle framework and application example. In doing so, this study advances the discourse on trustworthy AI for decision-making.

组织越来越多地在决策过程中使用数据驱动的人工智能（AI）系统。这些人工智能系统可以自主运行，支持人类决策者或越来越多地作为协作团队成员。然而，数据驱动的人工智能系统往往像黑盒子一样运作，缺乏可解释性。这对决策提出了挑战，因为参与决策过程或受决策过程影响的利益相关者经常需要了解决策背后的基本原理。此外，数据驱动的人工智能系统在不利用结构化领域知识的情况下运行。因此，数据驱动的人工智能系统可能会产生与决策上下文、目标或约束不一致的输出，从而可能导致糟糕的决策或降低用户对人工智能系统的信任。因此，近年来人们对将领域知识与数据驱动的人工智能相结合的兴趣越来越大。这在神经符号人工智能中很明显，这是人工智能的一个子领域，将神经网络与符号人工智能结合在一起。虽然这种方法有望提高人工智能系统在决策中的可信度，但领域知识集成有助于可信度维度的具体机制仍未得到充分探索。因此，本研究回顾并整合了最新的知识和数据驱动的人工智能文献，以及决策的相关概念。在此基础上，提出了用于决策的集成知识和数据驱动的人工智能系统的生命周期框架，并通过医疗保健应用示例演示了其应用。它使用所提出的生命周期框架和应用示例进一步分析了知识和数据驱动的人工智能系统的可信度维度。在此过程中，本研究推进了关于可信赖的人工智能决策的论述。

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引用次数: 0

Dual-mode guided reinforcement learning for decentralized lifelong path planning of multiple automated guided vehicles in robotic mobile fulfillment systems 机器人移动履约系统中多自动导引车分散终身路径规划的双模引导强化学习

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-01-01 Epub Date: 2025-11-17 DOI: 10.1016/j.compind.2025.104416

Jianbin Xin , Peiyan Guo , Hongbo Li , Andrea D’Ariano , Yanhong Liu

The robotic mobile fulfillment system (RMFS) has revolutionized the manufacturing and logistics industries by enhancing the efficiency of automated storage and order fulfillment through automated guided vehicles (AGVs). However, existing multi-AGV path planning methods in RMFS typically decouple path planning from conflict resolution, thereby simplifying the problem but limiting system performance, especially in dynamic and complex operational environments. To address this challenge, we introduce a novel learning-based hierarchical framework for lifelong multi-AGV path planning. Our framework integrates a dual-mode heuristic global guidance planner with a local reinforcement learning planner, leveraging asynchronous proximal policy optimization and a recurrent neural network to achieve fully decentralized, online navigation. Critically, our dual-mode guidance mechanism adapts to multi-phase transport tasks by enabling unloaded AGVs to travel beneath stationary pods—a key distinction from conventional methods. This approach mitigates congestion in narrow corridors and boosts overall system throughput. Experimental results demonstrate that our method outperforms state-of-the-art centralized and decentralized approaches in large-scale deployments, achieving higher success rates and throughput while significantly reducing computational costs. This research thus offers a scalable and efficient solution to the complex path-planning challenges inherent in RMFS.

机器人移动履行系统（RMFS）通过自动引导车辆（agv）提高自动化存储和订单履行的效率，彻底改变了制造业和物流业。然而，RMFS中现有的多agv路径规划方法通常将路径规划与冲突解决解耦，从而简化了问题，但限制了系统性能，特别是在动态和复杂的操作环境中。为了解决这一挑战，我们引入了一种新的基于学习的分层框架，用于终身多agv路径规划。我们的框架集成了双模式启发式全局导航规划器和局部强化学习规划器，利用异步近端策略优化和循环神经网络来实现完全分散的在线导航。关键的是，我们的双模制导机制通过使卸载的agv在固定吊舱下行驶来适应多相运输任务，这是与传统方法的一个关键区别。这种方法减轻了狭窄走廊的拥堵，提高了整个系统的吞吐量。实验结果表明，我们的方法在大规模部署中优于最先进的集中式和分散式方法，实现了更高的成功率和吞吐量，同时显着降低了计算成本。因此，这项研究为RMFS固有的复杂路径规划挑战提供了一个可扩展和有效的解决方案。

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引用次数: 0

A review of digital twins in smart industries: Concepts, milestones, trends, applications, opportunities and challenges 智能产业中的数字孪生：概念、里程碑、趋势、应用、机遇和挑战

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2026-01-01 Epub Date: 2025-11-18 DOI: 10.1016/j.compind.2025.104398

Peipei Ding , Shi Qiang Liu , Raymond Chiong , Sandeep Dhakal , Dewang Chen , Debiao Li , Hoi-Lam Ma , Sai-Ho Chung

A digital twin (DT) is a real-time, highly accurate, virtual replica that reflects the states and behaviours of physical objects or systems. DTs can enable monitoring, simulation, prediction, optimisation as well as the structured integration of technologies, data flows and functional processes within smart industries. In recent years, the DT technology has emerged as a research hotspot, which has prompted us to conduct a review of its development and application in various industries. We have identified 30 leading journals that have significantly contributed to DT research, with the Computers in Industry (CII) journal ranking second among these 30 journals with more than 80 related publications. After briefly discussing the key concepts and major milestones around the development and rapid adoption of DTs in smart industries, we focus on reviewing and analysing the DT publications from the CII journal from 2018 to present by systematically categorising them into four primary application domains: manufacturing, construction, transportation, and technologies and paradigms. We also discuss potential research opportunities (e.g., life cycle management, cross-disciplinary integration, human-machine collaboration) and challenges from a theoretical perspective, and provide managerial insights (e.g., building open standards, enhancing data access compatibility, extending DTs’ operational functions, applications to more industries) from a practical perspective. This review will be helpful for academic researchers and industrial practitioners to gain a broad understanding of the versatility of DTs, thereby fostering interdisciplinary innovation.

数字孪生（DT）是反映物理对象或系统的状态和行为的实时、高度精确的虚拟复制品。DTs可以实现智能行业内技术、数据流和功能流程的监控、模拟、预测、优化以及结构化集成。近年来，DT技术已经成为一个研究热点，这促使我们对其在各个行业的发展和应用进行了回顾。我们确定了30种对DT研究做出重大贡献的领先期刊，其中工业计算机（CII）期刊在这30种期刊中排名第二，发表了80多篇相关论文。在简要讨论了智能工业中发展和快速采用DT的关键概念和主要里程碑之后，我们重点回顾和分析了2018年以来CII期刊上的DT出版物，并将其系统地分为四个主要应用领域：制造业、建筑业、交通运输业、技术和范式。我们还从理论角度讨论了潜在的研究机会（例如，生命周期管理，跨学科集成，人机协作）和挑战，并从实践角度提供了管理见解（例如，建立开放标准，增强数据访问兼容性，扩展dt的操作功能，应用到更多行业）。这一综述将有助于学术研究者和工业实践者更广泛地了解DTs的多功能性，从而促进跨学科的创新。

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引用次数: 0