2023年中国工业工程年会暨第二十七届工业工程与工程管理国际学术会议、第十二届工业工程企业应用与实践高峰论坛将于2023年4月21-23日在江西省南昌市召开。本次会议由188金宝搏网址 工业工程分会主办，南昌航空大学经济管理学院和南昌航空大学土木建筑学院承办。会议主题为数智赋能工业工程，助力产业体系升级。会议旨在为工业工程领域的专家、学者和业界人士提供一个理论研究、成果展示和实践探索的交流平台，以期推动工业工程的发展及应用。会议同期召开188金宝搏网址 工业工程分会委员会议。

本届会议征收中英文学术论文交流。我们期待您的关注和参与。

一、 会议组织

主办单位:

188金宝搏网址 工业工程分会

承办单位:

南昌航空大学经济管理学院

南昌航空大学土木建筑学院

协办单位：

天津大学

清华大学

西安交通大学

上海交通大学

重庆大学

暨南大学

南京航空航天大学

合肥工业大学

广东工业大学

浙江工业大学

上海海事大学

南昌大学

华东交通大学

江西理工大学

南昌工程学院

中国第一汽车股份有限公司

明石创新技术集团股份有限公司

SMC自动化有限公司

富士康科技集团

北京津发科技股份有限公司

江铃汽车集团有限公司

荣誉主席：

汪应洛院士 西安交通大学

杨善林院士 合肥工业大学

大会主席：

罗嗣海 南昌航空大学教授、党委书记

齐二石 天津大学教授

大会执行主席：

何 桢 天津大学教授

大会副主席：

陈 新 广东工业大学教授

易树平 重庆大学教授

郑 力 清华大学教授

汪玉春 原一汽轿车党委书记、副总经理

陈振国 富士康科技集团副总裁

江志斌 上海交通大学教授

李从东 暨南大学教授

苏 秦 西安交通大学教授

王金凤 上海海事大学教授

周德群 南京航空航天大学教授

鲁建厦 浙江工业大学教授

马清海 SMC自动化有限公司总经理

高 峰 明石集团股份有限公司董事长

屈 挺 暨南大学教授

王凯波 清华大学教授

潘尔顺 上海交通大学教授

陈晓慧 重庆大学教授

张 强 合肥工业大学教授

张晓胜 中国第一汽车股份有限公司工程技术部副总经理

程序委员会主席：

黄 蕾 南昌航空大学经济管理学院院长

吕 辉 南昌航空大学土木建筑学院副院长(主持工作)

窦润亮 天津大学教授，188金宝搏网址 工业工程分会总干事

组织委员会主席：

李文川 南昌航空大学经济管理学院副院长

冯良清 南昌航空大学经济管理学院教授

组织委员会副主席：

邱国斌 吴桂平 李建国 蔺宇 郭洪飞 朱光

组织委员会委员：

刘立新 曾郁文 姜江 陈华脉 王浩伦 王娟 欧阳天皓 刘士梅

二、会议时间地点

1.时间：2023年4月21-23日，21日报到。

2.地点：南昌航空大学

三、会议主要模块

模块一 主题报告

模块二 学术、实践特邀报告

模块三 平行分会场报告

模块四 期刊发展论坛(《工业工程》、《工业工程与管理》、《工程管理前沿》(FEM,英文刊)、《管理工程学报》、《管理科学学报》）

模块五 IE学科发展论坛

模块六 学术论文报告

模块七 企业参观（江铃数字化工厂）

模块八 2023年188金宝搏网址 工业工程分会委员会议

四、会议论文投稿

1.我们诚邀工业工程相关领域的专家、学者参会并投稿。本次会议所有投稿将经大会评审委员会评审后推荐到会上宣讲交流。

2.遴选出的优秀中文论文将推荐至期刊《工业工程》或《工业工程与管理》；遴选出的优秀英文论文将推荐至Frontiers of Engineering Management（中国工程院院刊）或Computers & Industrial Engineering（CAIE）、Industrial Management & Data Systems(IMDS)等专刊。

3.投稿截止时间：3月31日，评审结果通知时间：4月7日前。投稿邮箱 chinaie1990@163.com，请在邮件正文中写明稿件联系人姓名、电话和单位。

征稿范围主要议题包括但不限于：

五、会议注册缴费

1.参会费：

2.注册方法：

参会代表可微信扫描下方二维码进行注册缴费；亦可登录188金宝搏网址 会议平台进行注册缴费。

六、联系方式

1.主办单位联系人：刘老师 18892296326，chinaie1990@163.com

2.承办单位联系人：吴老师 13870981495，376833166@qq.com

附件1：会议模块简介

附件2：CAIE和IMDS专刊征稿函

188金宝搏网址 工业工程分会

2023年2月

附件1：会议模块简介

模块一：主题报告嘉宾

1.杨善林，中国工程院院士，合肥工业大学教授

2.王自力，中国工程院院士，北京航空航天大学教授

3.郑力，清华大学教授、副校长, 教育部高等学校工业工程类专业教学指导委员会主任委员

4.刘作仪，国家自然科学基金委员会管理科学部副主任

5.李小军，江铃集团副总经理

模块二：特邀报告嘉宾

1.宗福季，香港科技大学讲座教授，香港科技大学(广州)信息枢纽署理院长

2.宋洁，北京大学工学院党委书记、副院长、长江学者特聘教授

3.马清海，SMC自动化有限公司总经理

4.张晓胜，中国第一汽车股份有限公司工程技术部副总经理

5.赵起超，北京津发科技股份有限公司总经理

模块三：平行分会场报告

分会场1：网络化协调制造

主席：吴锋 教授 西安交通大学，李聪波 教授 重庆大学

分会场2：数智赋能的低碳设计与制造

主席：彭涛 副教授 浙江大学，李新宇 教授 华中科技大学

分会场3：制造服务与智慧供应链

主席：冯良清 教授 南昌航空大学，王康周 教授 兰州大学

分会场4：质量控制与管理

主席：杜世昌 教授 上海交通大学，赵秀杰 副教授 天津大学

分会场5：系统可靠性工程

主席：司书宾 教授 西北工业大学，刘宇 教授 电子科技大学

分会场6：生产物流系统管理

主席：屈挺 教授 暨南大学，唐红涛 教授 武汉理工大学

分会场7：低碳经济与绿色供应链

主席：邱韫哲 助理教授 北京大学,陈庆佳 教授 宁波诺丁汉大学

分会场8：管理中的优化模型与算法

主席：陈彩华 教授 南京大学

分会场9：数据科学与系统创新

主席：宫琳 副教授 北京理工大学

模块四：期刊发展论坛

主持：潘尔顺 教授 上海交通大学

模块五：IE学科发展论坛

主持：周德群 教授 南京航空航天大学

附件2：CAIE和IMDS专刊征稿函

Computers & Industrial Engineering

Special Issue on

Data-driven value chain digital ecosystem of Manufacturing Enterprises

This special issue aims to explore, with the multiple influence of dual carbon policy, the epidemic prevention and so on, how to use intelligent data-driven theories, methods and technologies to promote value integration, value co-creation and value chain upgrading for manufacturing enterprises, to realize enterprises value chain ecological network coordination.

Aims:

With the multiple influence of dual carbon policy, the epidemic prevention and the complex international situation, manufacturing enterprises are in a critical period of accelerating industrial reform and enhancing their core competitiveness. They urgently need to construct value chain digital ecosystem, which extracts insight from data systems to realize intelligent clustering. Based on intelligent data-driven theories and methods, advanced interconnected technologies and tools are used to make various links of the value chain and different entities interact and integrate dynamically according to the principle of optimal overall value, to achieve value integration, value co-creation, value chain upgrading, and to build enterprises value chain ecological network coordination. The value chain digital ecosystem illustrates manufacturing enterprises behavior, rather than rely on domain knowledge, decision-makers experience, or subjective intuition alone. Nowadays, data-driven methods are rapidly reshaping the strategic framework of manufacturing enterprises in all industries and leading a paradigm shift towards an innovation-based economy based on knowledge, data, and the Internet of Things. Modern industrial production and operation processes are recorded by huge amount of heterogeneous data including sensor-based data, image-based data, and other numerical and categorical data related design, manufacturing and operation management process domain. These data are needed to analyze and extract the key information behind the data and then provide useful information and knowledge for decision making such as yield enhancement, cycle time control, defect inspection, demand forecast, fault detection, product design and predictive maintenance. Data-driven methods such as artificial intelligence, big data analytic and machine learning learn from data to drive better decisions, which providing support for building value chain ecosystem. With various data, manufacturing enterprises can uncover patterns and relationships behind the data, which allows decision makers to anticipate outcomes based upon more concrete information than an assumption. Moreover, the interpretable models and results are also important to enhance the efficiency and effectiveness of enterprises collaborations to empower operations excellence, smart manufacturing and value creation. Well established machine learning and mathematical models for production/process engineering must be integrated with data-driven methods for cross-domain knowledge generation and representation. Artificial intelligence and other model-driven methods are main methodologies for considering in problem-oriented industrial management and decision support to empower intelligence excellence, which also facilitate the setup of ecosystem. The above study will play a major role in promoting market standardization, new technology benefits and improving production quality, leading relevant industries to promote development towards high-quality and sustainable direction.

Scope:

This special issue of Computers & Industrial Engineering aims to explore, with the multiple influence of dual carbon policy, the epidemic prevention and so on, how to use intelligent data-driven theories, methods and technologies to promote value integration, value co-creation, and value chain upgrading for manufacturing enterprises, to realize enterprises value chain ecological network coordination. Empirical studies with technical and/or methodological advances to address realistic issues are encouraged. The scope of this special issue covers the aspects of areas that explore the data science for value chain digital ecosystem of manufacturing enterprises in the context of dual carbon. Submissions of scientific results from experts in academia and industry worldwide are strongly encouraged. The topics include but not limited to, are listed below:

Please submit your manuscript before the submission deadline of 31 August 2023. Manuscripts should be submitted through the online system at: https://www.editorialmanager.com/caie/default.asp. Authors must select “VSI: DD of ME" when they reach the “Article Type” step in the submission process. Both the Guide for Authors and the submission portal could be found on the Journal Homepage here: https://www.journals.elsevier.com/computers-and-industrial-engineering. Submissions will be reviewed according to rigorous standards and procedures through double-blind peer review by at least two qualified reviewers.

Keywords:

Value chain, Digital ecosystem, Dual carbon, Data collaborative service, Artificial Intelligence, Big data analytics, and Machine Learning

Publication Schedule:

Deadline for manuscript submission: 31 August 2023.

Review report: 31 October 2023

Revised paper submission deadline: 30 December 2023

Notification of final acceptance: 31 March 2024

Approximate publication date: End of 2024

Guest Editors:

Prof. Runliang Dou (Managing Guest Editor), College of Management and Economics, Tianjin University, China, drl@tju.edu.cn

Prof. Kuo-Yi Lin (Co-Managing Guest Editor), School of Business, Guilin University of Electronic Technology, China, kylink1008@hotmail.com

Prof. Mohammad T. Khasawneh, Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, USA, mkhasawn@binghamton.edu

Prof. Shubin Si, School of Mechanical Engineering, Northwestern Polytechnical University, China, sisb@nwpu.edu.cn

Industrial Management & Data Systems

Special Issue on

Value Chain Collaboration for Complicated Product Innovation and Manufacturing Excellence by Industrial Data-driven Modeling and Optimization

Aims:

Industrial Data-driven value chain digital ecosystem extract insight from manufacturing data systems to enabling intelligent clustering. Data-driven methods such as artificial intelligence, big data analytic and machine learning that learn from data to drive better decisions. According to various data, manufacturing enterprises can uncover patterns and relationships behind the data, which allow decision makers can anticipate outcomes based upon more concrete information than an assumption. Thus, value chain digital ecosystem illustrates manufacturing enterprises behavior, rather than relying on domain knowledge, decision-makers experience, or subjective intuition alone. Moreover, the interpretable models and results are also important to enhance the efficiency and effectiveness of enterprises collaborations to empower operations excellence and smart manufacturing. Well established machine learning, and mathematical models form decision support systems for production/process engineering must be integrated with data-driven methods for cross-domain knowledge generation and representation. In particular, artificial intelligence and other model-driven methods are main methodologies for considering in problem-oriented industrial management and decision support to empower intelligence excellence. Nowadays, data-driven methods is rapidly reshaping the strategic framework of manufacturing enterprises in all industries and leading a paradigm shift towards an innovation-based economy based on knowledge, data, and the Internet of Things. This latest industrial revolution provides firms with ample opportunities for sustainability, but it also brings challenges. Industrial companies are facing the challenge of transferring the concept of the sustainable value chain digital ecosystem, Internet of Things, cyber-physical system into real applications, threatening established business models, changing processes of value creation, creating new security risks, and intensifying innovation competition. Modern industrial production and operation processes are recorded by huge of heterogeneous data including sensor-based data, image-based data, and other numerical and categorical data related design and manufacturing process domain. These data are needed to analyze and extract the key information behind the data and then provide useful information and knowledge for decision making such as yield enhancement, cycle time control, defect inspection, demand forecast, fault detection, product design and predictive maintenance. Additionally, digital twin, which is like a virtual model of a product, process, and service, provide the analysis and monitor information to physical system for understanding the variation. The value chain digital ecosystem of manufacturing enterprises focuses on the data management issue for data-driven methods.

Scope:

This special issue of the Industrial Management & Data Systems aims to address emergent research issues driven by the needs of intelligent applications in various industries, such as smart manufacturing and service. Empirical studies with technical and/or methodological advances to address realistic issues are encouraged. The scope of this special issue covers the aspects of areas that explore the data science for value chain digital ecosystem of manufacturing enterprises in the context of dual carbon. Submissions of scientific results from experts in academia and industry worldwide are strongly encouraged. The topics include but not limited to, are listed below:

l The data integration, information fusion, and business collaboration for Physical-space, social-space, and information-space

l Scaled Heterogeneous swarm intelligence for cooperation and decision making

l Data driven industrial Value Chain, especial across value chain coupling and modeling

l Data driven for multi-agent collaboration and value discovery

l Industrial Internet-based product integration and innovation

l Industrial Internet-based production management and quality enhancement

l Industrial Data Governance, trusted security exchange, data quality assurance

l Spatial-Temporal correlation extraction and evolution for Industrial Internet

l Deep Sensing and cooperative forecast model with substance Flow and energy transfer

l Data driven-based optimization and decision framework for production and industrial chains

Submission Guidelines:

All papers must be original, high quality and have not published, submitted and/or are currently under review elsewhere. Manuscripts Submissions to Industrial Management & Data Systems are made using ScholarOne Registration and access is available at http://mc.manuscriptcentral.com/imds. Please follow the instructions described in the “Author Guidelines”, given on the main page. Please make sure you select “Special Issue” as Article Type and “MIDT” as Section/Category. In preparing their manuscript, the authors are asked to closely follow the “Author Guidelines”. Submissions will be reviewed according to rigorous standards and procedures through double-blind peer review by at least two qualified reviewers. Accepted papers become the property of the publisher Emerald.

Publication Schedule:

Deadline for manuscript submission: 30 August 2023.

Guest Editors:

Prof. Runliang Dou (managing guest editor), College of Management and Economics, Tianjin University, drl@tju.edu.cn

Prof. Kuo-Yi Lin, School of Business, Guilin University of Electronic Technology, kylink1008@hotmail.com

Prof. Chia-Yu Hsu, Department of Industrial Management, Taiwan University of Science and Technology, cyhsu@mail.ntust.edu.tw

Prof. Mohammad T. Khasawneh, Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, mkhasawn@binghamton.edu