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Data analytics and optimization for smart industry

Lixin TANG, Ying MENG

《工程管理前沿（英文）》 2021年第8卷第2期页码 157-171 doi: 10.1007/s42524-020-0126-0

摘要： Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

关键词： data analytics system optimization smart industry

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Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

《工程管理前沿（英文）》 2017年第4卷第1期页码 41-48 doi: 10.15302/J-FEM-2017003

摘要： A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. Data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it’s needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

关键词： engineering management project management big data data analytics planning execution

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Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng WU

《工程管理前沿（英文）》 2020年第7卷第2期页码 169-171 doi: 10.1007/s42524-020-0114-4

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CORRECTION to: Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng Wu

《工程管理前沿（英文）》页码 697-697 doi: 10.1007/s42524-020-0139-8

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工业互联网平台：发展趋势与挑战

王晨,宋亮,李少昆

《中国工程科学》 2018年第20卷第2期页码 15-19 doi: 10.15302/J-SSCAE-2018.02.003

摘要：

随着制造业和新一代互联网、信息化技术的融合，工业互联网高速发展。无论是国际制造业的领先企业，还是我国的制造业国家战略都明确了工业互联网平台研发的重要性。本文对工业互联网平台的发展趋势进行了阐释，并对平台在用户生态、开发者生态和数据生态构建中的挑战展开了分析，并有针对性地探讨了工业互联网平台在工业大数据系统与工业数据建模和分析方面所遇到的技术挑战。

关键词：工业互联网平台工业大数据数据分析

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智能过程制造中的数据解析与机器学习——大数据时代的最新进展与展望 Perspective

尚超、 Fengqi You

《工程（英文）》 2019年第5卷第6期页码 1010-1016 doi: 10.1016/j.eng.2019.01.019

摘要：

安全、高效、可持续的运行是工业生产过程控制的主要目标。然而，目前的技术严重依赖人为干预，因此在实际应用中体现出明显的局限性。蓬勃发展的大数据时代对流程工业产生了巨大的影响，为实现智能制造提供了前所未有的机遇。这种新的生产方式不仅要求机器能够帮助人类减轻繁重的体力劳动，还要能有效地承担智力劳动，甚至能够实现自主创新。为了实现这一目标，数据分析与机器学习扮演着不可或缺的角色。在本文中，我们回顾了数据分析和机器学习在工业生产过程监控、控制和优化方面的最新进展，着重分析机器学习模型的可解释性和功能性。通过分析实际需求与研究现状之间的差距，为未来的研究方向给出了建议。

关键词：大数据机器学习智能制造过程系统工程

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SuPoolVisor：矿池监管可视分析系统 Research

Jia-zhi XIA, Yu-hong ZHANG, Hui YE, Ying WANG, Guang JIANG, Ying ZHAO, Cong XIE, Xiao-yan KUI, Sheng-hui LIAO, Wei-ping WANG

《信息与电子工程前沿（英文）》 2020年第21卷第4期页码 507-523 doi: 10.1631/FITEE.1900532

摘要：在过去十年中，以比特币为代表的加密货币充分展示其在支付和货币系统中的巨大优势与潜力。矿池被认为是比特币的来源，也是市场稳定的基石。对矿池的监管可帮助监管机构有效评估比特币的整体健康状况。但是，矿池匿名性和分析海量交易的难度限制了更深入的分析。此外，对多源异构数据直观和全面的监管也是一个挑战。本文设计并实现一个交互式可视分析系统SuPoolVisor，它可对矿池进行可视化监管，并支持使用可视推理对矿池去匿名化。SuPoolVisor支持矿池和地址两个级别的分析。在矿池级别，使用排序的河流图呈现矿池算力随时间演变情况，并在其他两个视图中设计特殊图形以说明矿池的影响范围和矿池成员迁移。在地址级别，使用力导向图和大规模序列视图呈现矿池中的动态地址网络。特别地，这两个视图与Radviz视图的组合，可用于矿池成员去匿名化的迭代可视推理过程，这些视图都提供了用于跨视图分析和标识的交互功能。我们与该领域专家紧密合作完成3个真实案例，并在案例中证明SuPoolVisor的有效性和可用性。

关键词：比特币矿池；可视分析；交易数据；可视推理；金融科技

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A literature review of perishable medical resource management

《工程管理前沿（英文）》页码 710-726 doi: 10.1007/s42524-023-0278-9

摘要： In recent decades, healthcare providers have faced mounting pressure to effectively manage highly perishable and limited medical resources. This article offers a comprehensive review of supply chain management pertaining to such resources, which include transplantable organs and healthcare products. The review encompasses 93 publications from 1990 to 2022, illustrating a discernible upward trajectory in annual publications. The surveyed literature is categorized into three levels: Strategic, tactical, and operational. Key problem attributes and methodologies are analyzed through the assessment of pertinent publications for each problem level. Furthermore, research on service innovation, decision analytics, and supply chain resilience elucidates potential areas for future research.

关键词： perishable medical resources organ transplant healthcare products decision analytics operations management

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Special issue: Operations analytics and optimization for manufacturing, logistics and energy systems

Jiming WANG, Jie LIU, Anlin SHAO, Lixin TANG

《工程管理前沿（英文）》 2017年第4卷第3期页码 239-241 doi: 10.15302/J-FEM-2017109

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数据驱动的信息物理生产系统——迈向安全、高效、分布式智能制造 Perspective

Manu Suvarna, Ken Shaun Yap, 杨文韬, 李君, Yen Ting Ng, 王笑楠

《工程（英文）》 2021年第7卷第9期页码 1212-1223 doi: 10.1016/j.eng.2021.04.021

摘要：

随着工业4.0 和智能制造等概念和系统的普及，传统制造业将见证向新模式的转型，以更好地响应用户的需求并实现安全、高效、智能化的操作。在此背景下，本文聚焦于信息物理生产系统（CPPS）的概念，从整体上阐述了CPPS在这一产业转型中的三个关键驱动作用，即数据驱动的生产系统、分布式的智能制造和保证数据安全的集成区块链技术。通过这三个方面的具体技术和系统实现，基于数据驱动的建模和优化，智能信息物理系统将助力流程工业和制造业转型。同时，分布式的智能制造可以更高效地实现产业升级和低碳化发展。区块链技术可以进一步确保数据共享的可靠性和安全性，实现跨子系统的整合。本文详细分析了最近发表的文献研究和行业相关案例支持，并对现有挑战和发展方向进行了总结展望。

关键词：智能制造信息物理生产系统工业物联网数据分析分布式系统区块链

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APFD：面向移动轨迹大数据的出租车路径推荐方法 Research Article

张文勇1,夏大文1,常国艳5,胡杨2,霍雨佳1,冯夫健1,李艳涛3,李华青4

《信息与电子工程前沿（英文）》 2022年第23卷第10期页码 1494-1510 doi: 10.1631/FITEE.2100530

摘要：

随着数据驱动智能交通系统的迅猛发展，高效的出租车路径推荐方法成为智慧城市的研究热点。基于移动轨迹大数据，提出一种基于人工势场（APF）和Dijkstra方法的出租车路径推荐方法。为提高路径推荐效率，提出一种区域提取方法，该方法通过原点和终点坐标搜索包含最优路径的区域。基于APF方法，提出一种有效的冗余节点去除方法。最后，通过Dijkstra方法推荐最优路径。将APFD方法应用于仿真地图和北京四环的实际路网。在地图上随机选取20对起点和终点坐标，采用APFD方法、蚁群（AC）算法、贪婪算法（A*）、APF、迅速探索随机树（RRT）、非支配排序遗传算法-II（NSGA-II）、粒子群算法（PSO）和Dijkstra算法进行最短路径推荐。在最短路径规划方面，与AC、A*、APF、RRT、NSGA-II和PSO相比，APFD的路径规划能力分别提高了1.45%–39.56%、4.64%–54.75%、8.59%–37.25%、5.06%–45.34%、0.94%–20.40%和2.43%–38.31%。与Dijkstra算法相比，APFD的执行效率提高了1.03–27.75倍。此外，在北京四环实际路网中，APFD推荐最短路径的能力优于AC、A*、APF、RRT、NSGA-II和PSO，且APFD的执行效率高于Dijkstra方法。

关键词：大数据分析；区域提取；人工势场；Dijkstra；路线推荐；出租车GPS轨迹

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因果模型启发的复杂工业过程软传感器自动化特征选择方法 Article

孙衍宁, 秦威, 胡锦华, 许鸿伟, 孙兆辉

《工程（英文）》 2023年第22卷第3期页码 82-93 doi: 10.1016/j.eng.2022.06.019

摘要：

关键性能指标（KPI）的软测量在复杂工业过程决策中起着至关重要的作用。许多研究人员已经使用先进的机器学习（ML）或深度学习（DL）模型开发出了数据驱动的软传感器。其中，特征选择是一个关键的问题，因为一个原始的工业数据集通常是高维的，并不是所有的特征都有利于软传感器的建立。一种完善的特征选择方法不应该依赖于超参数和后续的ML或DL模型。换言之，这种特征选择方法应该能够自动地选择一个特征子集进行软传感器建模，选择的每个特征对工业KPI 都有独特的因果影响。因此，本研究提出了一种受因果模型启发的自动特征选择方法，用于工业KPI 的软测量。首先，受后非线性因果模型的启发，本研究将数据驱动的软传感器与信息论相结合，以量化原始工业数据集中每个特征和KPI之间的因果效应。然后，提出了一种新的特征选择方法，即自动选择具有非零因果效应的特征来构造特征子集。最后，利用所构造的子集，通过AdaBoost 集成策略开发KPI 的软传感器。两个实际工业应用中的实验证实了该方法的有效性。在未来，该方法也可以应用于其他工业过程，以帮助开发更先进的数据驱动的软传感器。

关键词：大数据分析机器智能质量预测软传感器智能制造

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Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

Pai ZHENG, Honghui WANG, Zhiqian SANG, Ray Y. ZHONG, Yongkui LIU, Chao LIU, Khamdi MUBAROK, Shiqiang YU, Xun XU

《机械工程前沿（英文）》 2018年第13卷第2期页码 137-150 doi: 10.1007/s11465-018-0499-5

摘要：

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

关键词： Industry 4.0 smart manufacturing systems Internet of Things cyber-physical systems big data analytics framework

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RCAnalyzer：动态网络中稀有类可视分析系统 Research

Jia-cheng PAN, Dong-ming HAN, Fang-zhou GUO, Da-wei ZHOU, Nan CAO, Jing-rui HE, Ming-liang XU, Wei CHEN

《信息与电子工程前沿（英文）》 2020年第21卷第4期页码 491-506 doi: 10.1631/FITEE.1900310

摘要：动态网络是指其节点和/或链接随时间动态变化的图结构。现有可视化和分析技术主要集中在总结和揭示网络结构的主要演化模式。很少有工作专注于检测动态网络中的异常变化模式，这种情况很少发生，一旦发生，则可能破坏整个结构的发展。本文介绍了第一个可视分析系统RCAnalyzer，用于检测动态网络中子结构的罕见变化。所提系统采用稀有类别检测算法识别异常变化的结构，并在上下文中将其可视化，以帮助专家检查分析结果并标记数据。特别地，引入新的可视化形式，用一系列连接的三角矩阵表达动态网络快照。在每个矩阵上进行层次聚类和最佳树切割，以展示在其周围结构的上下文中检测到的节点和链接的罕见变化。通过案例和用户调研评估该技术。评估结果验证了该系统的有效性。

关键词：稀有类检测；动态网络；可视分析

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Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions

《工程管理前沿（英文）》页码 577-591 doi: 10.1007/s42524-022-0203-7

摘要： Post-acute care (PAC) residents in nursing homes (NHs) are recently hospitalized patients with medically complex diagnoses, ranging from severe orthopedic injuries to cardiovascular diseases. A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes, such as higher community discharge likelihood and lower re/hospitalization risk. Accurate prediction of the PAC residents’ length-of-stay (LOS) with multiple discharge dispositions (e.g., community discharge and re/hospitalization) will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery. Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions, developing an accurate prediction model becomes challenging. Existing predictive analytics methods, such as distribution-/regression-based methods and machine learning methods, either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions. In this work, a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’ characteristics. A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation. A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks. The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes.

关键词： nursing home predictive analytics individualized prediction competing risks health outcomes