Computers & Industrial Engineering最新文献_第7页

Online process monitoring under quality data scarcity: Self-starting truncated EWMA schemes for time between events 高质量数据稀缺下的在线过程监控：事件间隔时间的自启动截断EWMA方案

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-22 DOI: 10.1016/j.cie.2025.111777

FuPeng Xie , JingWei Liu , JiaCai Huang , YuXing Dai , Quan Sun , XueLong Hu , Philippe Castagliola

This study proposes one-sided self-starting truncated EWMA (SST-EWMA) control charts for effective high-quality process monitoring in situations where extensive in-control time-between-events (TBE) observations are unavailable. By constructing a pivot quantity and establishing variable mappings, a self-starting framework specifically tailored for Gamma distributed TBE observations is developed. The integration of a variable truncation mechanism into this framework further enhances sensitivity to small to moderate process shifts. To investigate the detection properties of the proposed schemes, simulation were conducted to examine the effects of the shape parameter

α

, the number of reference TBE observations

M

, and the smoothing parameter

λ

on the average time to signal (

ATS

). Based on the simulation results, guidelines are provided for achieving

ATS

performance comparable to that of the corresponding known-parameter schemes. Comparative analysis demonstrates that, although slightly inferior to the one-sided TEWMA TBE charts under known parameters, the proposed charts exhibit superior adaptability in scenarios with scarce TBE data, and also outperform the existing self-starting EWMA TBE chart, validating the effectiveness of the variable truncation mechanism. Finally, two case studies are presented to illustrate the practical implementation of the proposed control charts in industrial engineering applications.

本研究提出了单侧自启动截断EWMA （SST-EWMA）控制图，用于在无法获得大量控制时间间隔（TBE）观测的情况下进行有效的高质量过程监控。通过构造一个枢轴量和建立变量映射，开发了一个专门为Gamma分布TBE观测量身定制的自启动框架。将可变截断机制集成到该框架中，进一步提高了对小到中等过程移位的敏感性。为了研究所提方案的检测特性，通过仿真研究了形状参数α、参考TBE观测数M和平滑参数λ对平均到信号时间（ATS）的影响。根据仿真结果，给出了实现ATS性能与相应的已知参数方案相当的准则。对比分析表明，虽然在已知参数下，本文提出的图略逊于单侧的EWMA TBE图，但在TBE数据稀缺的场景下，该图表现出更强的适应性，也优于现有的自启动EWMA TBE图，验证了变量截断机制的有效性。最后，提出了两个案例来说明所提出的控制图在工业工程应用中的实际实现。

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引用次数: 0

A novel process monitoring method for multivariate autocorrelated mixed-type data based on transformer model integrated with the feature-enhancement learning method 一种基于变压器模型与特征增强学习方法相结合的多变量自相关混合型数据过程监测方法

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-22 DOI: 10.1016/j.cie.2025.111776

Li Xue , Sen Feng , Tianci Zhao , Zhen He , Yuanzhong Jia , Tianye He

In the context of big data-driven intelligent manufacturing, process data often exhibit high dimensionality, multiple variables, and complex correlations. When these data comprise multiple variable types with unknown distributions, traditional parametric control charts struggle to address these challenges effectively. This study leverages the transformer model to monitor mixed continuous, count, and categorical data types in multivariate autocorrelated processes, proposing a feature-enhancement learning monitoring method. The experimental results demonstrate that the transformer model integrated with the proposed feature-enhancement learning method outperforms traditional monitoring approaches, such as residual control charts and

T^{2}

control charts, as well as other models, such as back propagation (BP), convolutional neural network (CNN), recurrent neural network (RNN), gated recurrent unit (GRU) and long short-term memory (LSTM), and also two traditional control charts constructed based on statistical methods for monitoring mixed-type data. The method’s effectiveness is further validated through a case study in semiconductor manufacturing. This study provides a theoretical foundation for applying deep learning technology to monitor multivariate autocorrelated processes.

在大数据驱动的智能制造背景下，过程数据往往呈现高维、多变量、复杂关联的特征。当这些数据包含具有未知分布的多种变量类型时，传统的参数控制图难以有效地解决这些挑战。本研究利用变压器模型监测多元自相关过程中的混合连续、计数和分类数据类型，提出了一种特征增强学习监测方法。实验结果表明，结合特征增强学习方法的变压器模型优于残差控制图和T2控制图等传统监测方法，也优于反向传播（BP）、卷积神经网络（CNN）、递归神经网络（RNN）、门控递归单元（GRU）和长短期记忆（LSTM）等其他模型。基于统计方法构造了两种传统的控制图，用于监测混合类型数据。通过半导体制造的实例研究，进一步验证了该方法的有效性。本研究为应用深度学习技术监测多变量自相关过程提供了理论基础。

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引用次数: 0

Multi-objective scheduling for complex assembly shops considering multiple human factors 考虑多人为因素的复杂装配车间多目标调度

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-20 DOI: 10.1016/j.cie.2025.111773

Huiting Li , Jiapeng Zhang , Xiaodi Chen , Haoxin Guo , Jianhua Liu , Cunbo Zhuang

The advancement of Industry 5.0 has driven a growing body of research that examines the impact of human factors on production processes. However, studies that simultaneously consider multiple types of human factors remain scarce. In this study, a comprehensive set of human factors, including workers’ skill proficiency, fatigue levels, interpersonal dynamics, and work experience, is incorporated into the assembly scheduling framework. Based on these considerations, the multi-objective scheduling problem in complex product assembly shops with parallel teams is investigated, with optimization objectives including makespan, transportation time, total waiting time, and team workload imbalance. To address this problem, an improved non-dominated sorting genetic algorithm is proposed. The algorithm features enhancement strategies, such as a destruction-reconstruction approach for optimizing the initial population and an improved evolutionary process. The proposed algorithm is evaluated against alternative algorithms using four case studies derived from actual production scenarios. The results demonstrate that the proposed method achieves superior solution quality and efficiency.

工业5.0的进步推动了越来越多的研究，研究人为因素对生产过程的影响。然而，同时考虑多种人为因素的研究仍然很少。本研究将工人的技能熟练程度、疲劳程度、人际关系动态和工作经验等人为因素纳入装配调度框架。在此基础上，研究了具有并行团队的复杂产品装配车间的多目标调度问题，优化目标包括完工时间、运输时间、总等待时间和团队工作量不平衡。为了解决这一问题，提出了一种改进的非支配排序遗传算法。该算法具有增强策略，如用于优化初始种群的破坏-重建方法和改进的进化过程。该算法通过从实际生产场景中导出的四个案例研究，对备选算法进行了评估。结果表明，该方法具有较好的求解质量和求解效率。

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引用次数: 0

Demand prediction for bike-sharing systems: A spatial and semantic modeling approach for enhanced accuracy and operational efficiency 自行车共享系统的需求预测：提高准确性和运行效率的空间和语义建模方法

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-20 DOI: 10.1016/j.cie.2025.111775

Juntao Wu , Jiahui Feng , Jie Fang , Hefu Liu

The exponential growth of Bike-Sharing Systems (BSS) has introduced complex challenges in supply–demand management, where imbalances frequently lead to resource wastage and reduced user satisfaction. While Graph Neural Networks (GNNs) have become a mainstream tool for demand forecasting, existing methodologies predominantly rely on static geographic proximity, failing to capture the latent semantic dependencies driven by actual riding behaviors. To bridge this gap, this paper proposes a novel Spatial-Semantic Graph Attention Neural Network (SSGAN). Unlike traditional models, SSGAN constructs a semantic adjacency matrix using DTW to quantify the shape similarity between station inflow and outflow patterns, thereby capturing non-Euclidean correlations beyond physical distance. Furthermore, a Gated Multi-Head Attention mechanism is designed to dynamically weigh these semantic relationships by integrating external covariates (e.g., weather), allowing the model to adapt to time-varying contexts. Crucially, to align prediction accuracy with decision effectiveness, the model employs a dual-stream architecture that fuses inflow and outflow features to better reflect net inventory changes. Empirical experiments on large-scale real-world datasets from Citi Bike and Divvy demonstrate that SSGAN not only achieves state-of-the-art prediction accuracy but also significantly reduces operational costs compared to baseline models. This study provides a generalized, decision-oriented computerized methodology for optimizing BSS rebalancing operations.

共享单车系统（BSS）的指数级增长给供需管理带来了复杂的挑战，其中不平衡经常导致资源浪费和用户满意度降低。虽然图神经网络（gnn）已经成为需求预测的主流工具，但现有的方法主要依赖于静态地理邻近性，无法捕获由实际骑行行为驱动的潜在语义依赖性。为了弥补这一缺陷，本文提出了一种新的空间语义图注意神经网络（SSGAN）。与传统模型不同，SSGAN使用DTW构建语义邻接矩阵来量化站点流入和流出模式之间的形状相似性，从而捕获超越物理距离的非欧几里得相关性。此外，设计了一个门控多头注意机制，通过整合外部协变量（如天气）来动态权衡这些语义关系，使模型能够适应时变的上下文。至关重要的是，为了使预测准确性与决策有效性保持一致，该模型采用了双流架构，融合了流入和流出特征，以更好地反映净库存变化。来自Citi Bike和Divvy的大规模真实数据集的实证实验表明，与基线模型相比，SSGAN不仅达到了最先进的预测精度，而且显著降低了运营成本。本研究为优化BSS再平衡操作提供了一种通用的、决策导向的计算机化方法。

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引用次数: 0

Nation’s fight against illegal fishing: Research opportunities in operations research and management science 国家打击非法捕鱼：运筹学和管理科学的研究机会

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-19 DOI: 10.1016/j.cie.2025.111756

N. Orkun Baycik , Canan G. Corlu , J. Gregory McDaniel , Alyssa Pierson

Illegal, unreported, and unregulated (IUU) fishing, representing about 26 million tons of fish caught annually, destroys our environment, economy, and society. The United Nations Food and Agriculture Organization recognizes IUU fishing as a global problem due to its impact on the environment and sustainability, global food security, and its association with other organized crimes. The goal of this paper is to present research opportunities that, when pursued, will benefit government agencies and non-profit organizations to tackle this complex societal problem. Through a comprehensive review of the literature and consultation with nonprofit organizations, we propose the first steps to develop new facility location and covering, network flow, and interdiction models to address IUU fishing. We highlight that collaborative efforts using operations research and analytics are necessary. The proposed models can contribute to practice by improving surveillance, understanding criminal operations, and disrupting illegal networks.

非法、不报告和不管制（IUU）捕鱼，每年捕捞约2600万吨鱼，破坏了我们的环境、经济和社会。联合国粮食及农业组织承认IUU捕鱼是一个全球性问题，因为它对环境和可持续性、全球粮食安全产生影响，并与其他有组织犯罪有关联。本文的目标是提供研究机会，当追求时，将有利于政府机构和非营利组织解决这一复杂的社会问题。通过对文献的全面审查和与非营利组织的磋商，我们提出了开发新的设施位置和覆盖、网络流量和拦截模型以解决IUU捕鱼问题的第一步。我们强调使用运筹学和分析的协作努力是必要的。提出的模型可以通过改进监视、了解犯罪活动和破坏非法网络来促进实践。

引用次数: 0

Predicting non-recurrent congestion impact: A pattern-based approach for speed drop ratio prediction using weighted K-nearest neighbors 预测非经常性拥堵影响：一种基于模式的速度下降比预测方法，使用加权k近邻

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-18 DOI: 10.1016/j.cie.2025.111769

YongKyung Oh , Jiin Kwak , Sungil Kim

Traffic congestion remains a major challenge in developed countries, disrupting mobility and affecting economic and social activities. Among its various types, non-recurrent congestion — caused by unexpected events such as accidents, maintenance, or debris — remains difficult to predict due to its irregular spatio-temporal dynamics. While existing models effectively forecast recurrent traffic, they are less applicable to non-recurrent events characterized by abrupt and anomalous patterns. This study presents a pattern-based framework that integrates the weighted K-nearest neighbor (WK-NN) algorithm with dynamic time warping (DTW) for similarity-based prediction of non-recurrent congestion impact. The framework estimates speed drop ratios (SDRs) and propagates the predicted effects to neighboring road segments, enabling a network-level assessment of disruption. By identifying historical patterns most similar to the current incident, the proposed approach enhances interpretability and traceability for operational use. We evaluate the method using 2780 real-world traffic incident records combining data from the Korean National Police Agency and NAVER Corporation. Experimental results demonstrate that the proposed framework achieves consistent and competitive performance compared with benchmark machine learning and deep learning models. These findings suggest the framework’s potential for supporting practical decision-making in traffic control centers through timely and interpretable congestion impact forecasts.

交通拥堵仍然是发达国家面临的一个重大挑战，它扰乱了流动性，影响了经济和社会活动。在各种类型的拥堵中，由意外事件（如事故、维修或碎片）引起的非经常性拥堵由于其不规则的时空动态而难以预测。虽然现有的模型可以有效地预测经常性交通，但它们对以突发性和异常模式为特征的非经常性事件的适用性较差。本研究提出了一个基于模式的框架，该框架将加权k -最近邻（WK-NN）算法与动态时间规整（DTW）相结合，用于基于相似性的非周期性拥塞影响预测。该框架估计速度下降比（sdr），并将预测的影响传播到邻近路段，从而实现网络级的中断评估。通过识别与当前事件最相似的历史模式，建议的方法增强了操作使用的可解释性和可追溯性。我们结合韩国警察厅和NAVER公司的数据，利用2780个真实交通事故记录对该方法进行了评估。实验结果表明，与基准机器学习和深度学习模型相比，该框架具有一致性和竞争力。这些发现表明，该框架有潜力通过及时和可解释的拥堵影响预测来支持交通控制中心的实际决策。

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引用次数: 0

Cross-silo human training in operational assembly: Integrating machine feedback for enhanced efficiency 操作装配中的跨仓人力培训：整合机器反馈以提高效率

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-18 DOI: 10.1016/j.cie.2025.111774

Kosuke Nakamura , Taro Ueyama , Masafumi Nishimura , Takayuki Nakano , Takahiro Aoki , Yoshitaka Yamamoto

In low-volume, multi-product manufacturing, workers must respond quickly and flexibly to changes in various work operations. However, there is currently a shortage of skilled workers, necessitating an effective method for training a wide range of workers with diverse characteristics. In this study, we first evaluated the feasibility of machine learning (ML) models for recognising complex assembly works. We next constructed the Feedback Integrated Expert Level Description System (FIELDS), which incorporates an ML model and functions for data collection, management, and user feedback. FIELDS can collect real-time work data from an action camera attached to trainees, analyse their assembly work from the data using the ML model, and provide feedback based on the analysis result. We evaluated the effect of the feedback using three metrics, the number of reduced missing processes, the distance from the regular processes, and the total work time. The feedback from the ML model was shown to enhance the trainees’ awareness of their proficiency and foster improvement. This result reveals a potential power of machine feedback for improving the efficiency of worker training. Consequently, this study contributes to offer a visible solution to enhance productivity and adaptability through cross-silo worker training in manufacturing environments.

在小批量、多产品制造中，工人必须快速灵活地响应各种工作操作的变化。然而，目前技术工人短缺，需要一种有效的方法来培训具有不同特点的广泛工人。在这项研究中，我们首先评估了机器学习（ML）模型识别复杂装配工作的可行性。接下来，我们构建了反馈集成专家级描述系统（FIELDS），该系统结合了ML模型和数据收集、管理和用户反馈的功能。FIELDS可以从附着在学员身上的动作相机收集实时工作数据，使用ML模型分析他们的组装工作数据，并根据分析结果提供反馈。我们使用三个度量来评估反馈的效果，减少缺失过程的数量，与常规过程的距离，以及总工作时间。从机器学习模型的反馈显示，提高学员的意识，他们的熟练程度和促进改进。这一结果揭示了机器反馈在提高工人培训效率方面的潜在力量。因此，本研究有助于提供一个可见的解决方案，以提高生产力和适应性，通过跨筒仓工人培训在制造环境。

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引用次数: 0

Integration of compliant disassembly strategy and state recognition for robotic peg-hole disassembly 机器人钉孔拆卸柔性拆卸策略与状态识别的集成

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-18 DOI: 10.1016/j.cie.2025.111772

Jiayi Liu , Xiaolong Zhang , Xiaofei Tu , Wenjun Xu , Zude Zhou

Robotic peg-hole disassembly is a common task in automated product disassembly, where compliant strategies are essential to prevent damage from excessive forces. Traditionally, recognizing the state of the peg and hole requires additional operations after disassembly. However, the forces and moments generated during the compliant disassembly process itself can be utilized for state recognition, eliminating the need for additional steps. This paper integrates compliant disassembly and state recognition of peg-hole into a single operation. A soft actor-critic algorithm is employed to minimize disassembly forces. Afterwards, a double Long Short-Term Memory Transformer algorithm is introduced to recognize the state of the peg and the hole. Experiments results show that the converged soft actor-critic model maintains disassembly forces at approximately 3 N, significantly outperforming other algorithms in force reduction. The double Long Short-Term Memory Transformer algorithm exhibits superior accuracy of 93.38 % in state recognition. This integrated approach improves end-of-life product recycling efficiency by combining operational synergy with enhanced performance.

机器人钉孔拆卸是自动化产品拆卸中常见的任务，其中合规策略是必不可少的，以防止过度的力量造成损坏。传统上，识别钉和孔的状态需要在拆卸后进行额外的操作。然而，在柔性拆卸过程中产生的力和力矩可以用于状态识别，从而消除了额外步骤的需要。本文将销孔的柔性拆卸和状态识别集成到一个操作中。采用软角色评判算法最小化拆装力。在此基础上，提出了一种双长短期记忆变压器算法来实现对钉和孔的状态识别。实验结果表明，收敛的软角色-评论家模型将拆装力维持在约3 N，在拆装力减小方面明显优于其他算法。双长短期记忆变压器算法在状态识别方面的准确率达到93.38%。这种综合方法通过将操作协同作用与增强的性能相结合，提高了报废产品的回收效率。

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引用次数: 0

Integration optimization of commuter metro lines: train timetable, passenger flow control strategy and short-turning scheme 通勤地铁线路一体化优化：列车时刻表、客流控制策略、短转方案

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-18 DOI: 10.1016/j.cie.2025.111768

Fuya Yuan , Huijun Sun , Chuang Zhu , Weibin Deng

The persistent expansion of urban rail transit networks and escalating passenger demands have intensified the challenge of imbalanced supply and demand, particularly during peak hours on commuter metro lines. Research indicates that this congestion is not uniformly distributed but concentrates at specific stations and sections. This study presents a novel integrated approach to jointly optimize the train timetable, passenger flow control strategy, and short-turning scheme for commuter metro lines. An integrated optimization model is developed, aiming to minimize both stranded passengers (outside stations and on platforms) and total train operating distance and time, while explicitly incorporating nonlinear relationships. To solve the model efficiently, it is reformulated into a Mixed-Integer Linear Programming (MILP) model using linearization techniques. The proposed method is applied to Beijing Metro Line 5. The results demonstrate that a refined and collaborative implementation of passenger flow control and train short-turning is achieved. Crucially, the optimized strategy significantly reduces operational costs: total train operating distance is decreased by approximately 33%, and total running time is shortened by 39.8%. This improvement is achieved despite a 7.5% increase in passengers waiting outside stations (a trade-off managed by the flow control strategy). The integrated optimization framework delivers substantial cost savings for transit enterprises by dramatically reducing train kilometers and runtime. The study successfully balances operational efficiency with passenger flow management, highlighting the effectiveness of coordinated strategies for mitigating peak-hour congestion in commuter metro systems.

城市轨道交通网络的持续扩张和不断上升的乘客需求加剧了供需不平衡的挑战，特别是在通勤地铁线路的高峰时段。研究表明，这种拥堵不是均匀分布的，而是集中在特定的车站和路段。本文提出了一种新的综合方法来共同优化通勤地铁的列车时刻表、客流控制策略和短转弯方案。建立了一个综合优化模型，旨在最大限度地减少滞留旅客（站外和站台）和列车总运行距离和时间，同时明确地纳入非线性关系。为了有效求解该模型，利用线性化技术将其重新表述为混合整数线性规划（MILP）模型。并以北京地铁5号线为例进行了实例分析。结果表明，实现了客流控制与列车短转向的精细化协同实施。最重要的是，优化后的策略显著降低了运营成本：列车总运行距离缩短了约33%，总运行时间缩短了39.8%。尽管在车站外等候的乘客增加了7.5%（通过流量控制策略进行权衡），但仍取得了这一改善。集成优化框架通过大幅减少列车公里数和运行时间，为运输企业节省了大量成本。该研究成功地平衡了运营效率和客流管理，突出了协调策略在缓解通勤地铁系统高峰时段拥堵方面的有效性。

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引用次数: 0

An activity-driven temporal multilayer network framework to support consensus in group decision making 一个活动驱动的时间多层网络框架，以支持群体决策中的共识

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-17 DOI: 10.1016/j.cie.2025.111736

Zijian Ling, Youlong Yang, An Huang

Social network group decision-making (SNGDM) provides valuable support for describing the opinion exchange in the decision-making process by using the connected social relationships among decision makers (DMs). With the expansion of social media, DMs are interconnected through various types of links. In this cases, interaction of DMs are no longer confined to single-type binary relationships but exhibit complex multiplexing and high-order dynamic characteristics. To this end, this study develops a consensus model based on multilayer network for improving the reliability of decision-making. First, we construct an attributed multilayer network by utilizing multiple social relationships and decision information, in which attributes serve as auxiliary information to establish additional exotic connectivity patterns. Then, the natural interaction of DMs shows a specific high-order correlation, where some activities occurring over the links of a layer depend on the dynamics of certain links on other layers. We propose an interactive joint random walk model to map this co-evolution into an activity-driven network dynamics process. To accurately capture hidden collective structure, state-based non-columnar communities and physical-based overlapping communities are detected. The reinforcement effects generated in these two types of communities can identify influential nodes and communities, guiding decision aggregation to reach higher consensus level. Finally, a numerical example is presented, and simulation experiments and comparative analysis are performed to validate the effectiveness and superiority of proposed model.

社会网络群体决策（Social network group decision， SNGDM）利用决策者之间的关联社会关系，为描述决策过程中的意见交换提供了有价值的支持。随着社交媒体的扩展，dm通过各种类型的链接相互连接。在这种情况下，dm的相互作用不再局限于单一类型的二元关系，而是表现出复杂的多路复用和高阶动态特性。为此，本研究开发了基于多层网络的共识模型，以提高决策的可靠性。首先，我们利用多种社会关系和决策信息构建了一个带有属性的多层网络，其中属性作为辅助信息来建立额外的外部连接模式。然后，dm的自然相互作用显示出特定的高阶相关性，其中在一层的链接上发生的一些活动依赖于其他层上某些链接的动态。我们提出了一个交互式联合随机行走模型，将这种共同进化映射为一个活动驱动的网络动力学过程。为了准确捕获隐藏的集体结构，检测基于状态的非柱状群落和基于物理的重叠群落。这两类群体产生的强化效应可以识别有影响的节点和群体，引导决策聚集达到更高的共识水平。最后给出了一个数值算例，并进行了仿真实验和对比分析，验证了所提模型的有效性和优越性。

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引用次数: 0