重要通知 | 2024中国工业工程年会暨第十三届工业工程企业应用与实践高峰论坛会议通知

1. 本次会议征收中英文学术论文到会上交流，将评比出最佳论文和优秀论文若干篇。我们诚邀工业工程相关领域的学者、专家参会并投稿。

2. 本次会议不出版论文集，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。届时，将遴选出部分中英文优秀论文，中文论文有机会推荐到《工业工程》期刊，英文论文有机会推荐到期刊Industrial Management & Data Systems、Applied Soft Computing、Sustainability和IET Collaborative Intelligent Manufacturing 的专刊。

3.投稿截止时间：10月20日，延期至10月31日，评审结果通知时间：10月31日前。投稿邮箱 liushimei@tju.edu.cn，请在邮件正文中写明稿件联系人姓名、电话和单位。

智能制造与工业智能

智慧医疗

质量控制和管理

运筹与优化

先进设计与制造

系统建模与仿真

物流工程与供应链管理

物联网与云计算

双碳格局下的产业变革

生产计划与控制

人因工程

人工智能

企业资源规划

企业数字化运营模式创新

可靠性与维护工程

决策支持系统

精益生产

工业4.0与5.0

工程项目管理

工程经济与成本分析

复杂系统与智能系统

服务科学/管理

大数据分析与应用

产业链韧性与安全

产业价值链分析与优化

产品生命周期管理

安全与风险管理

1.参会费：

2.注册方法：

参会代表可登录中国机械工程学会会议平台进行注册及缴费，网址为：https://meeting.cmes.org/?bust=1718087724141&sid=1109&mid=277&v=100#!c/showNews/a/index/id/3583/label/338 ，亦可微信扫描下方二维码进行注册缴费。                                    

 扫码注册缴费

 珠海暨南大学希尔顿花园酒店

备注：请参会代表自行预定酒店，报“工业工程年会”可享上述优惠价。

（1）珠海机场：

（乘车路线一）距离珠海金湾机场38公里，于珠海机场公交站乘坐机场大巴等公共交通，车程约50分钟，车票约26元，下车后步行约600米,到达希尔顿花园酒店。

（乘车路线二）距离珠海金湾机场38公里，打出租车124元左右。

（2）高铁明珠站

（乘坐路线一）距离珠海明珠站4公里，打出租车15元左右。

（3）高铁前山站

（乘坐路线一）距离珠海前山站2.7公里，乘坐公共交通，路程约5分钟，车票1元。下车后步行约900米,到达希尔顿花园酒店。

（乘坐路线二）距离珠海前山站2.7公里，打出租车12元左右。

（4）高铁珠海站

（乘车路线一）距离高铁珠海站约7公里，乘坐公共交通，车程约29分钟，车票1元。下车后步行约14分钟。

（乘车路线二）距离高铁珠海站约7公里，打出租车20元左右。

（5）九洲港

（乘车路线一）距离九洲客运港月9公里，乘坐公共交通，车程约30分钟，车票2元。下车后步行约8分钟。

（乘车路线二）距离九洲客运港约9公里，打出租车25元左右。

联系人：

陈老师 18818654625，

           306718788@qq.com

刘老师 18892296326，

           liushimei@tju.edu.cn

附件

附件1:

Special issue on Exploring Synchromodality and Co-modality:Pathways to Sustainable and Efficient Transportation System in Industrial Management & Data Systems 

附件2:

Special issue on Intelligent Decision Making, Deep and Machine Learning, Generative AI to Empower Smart Production and Service in Applied Soft Computing.

附件3:

Special Issue on Digital Twin-enabled Smart Production and Logistics Systems in IET Collaborative Intelligent Manufacturing

附件1：

Special issueon

Exploring Synchromodality and Co-modality:

Pathways to Sustainable and Efficient Transportation System in Industrial Management & Data Systems

This special issue aims to expand the horizons of understanding and practical application of synchromodal and co-modal transportation strategies. These approaches, which emphasize the seamless integration of different modes of transport and logistical operations, offer promising avenues for enhancing the efficiency, sustainability, and resilience of our transportation systems. Through a multidisciplinary lens, we endeavor to gather and disseminate theoretical insights, empirical research, and innovative practices that illuminate the potential and challenges of synchromodality and co-modality. By exploring how synchromodal and co-modal approaches can rise to meet these challenges, we aim to pave the way for more resilient and sustainable urban mobility ecosystems. By building upon this foundation, our special issue seeks to push the boundaries of knowledge, uncovering innovative approaches, identifying gaps in current practices, and proposing actionable strategies for a more sustainable future. In all, the special issue is a testament to the collective effort to chart a course towards a brighter, more sustainable future for transportation. 

List of topic areas

Technological Innovations: Exploration of cutting-edge technologies facilitating synchromodal and co-modal transportation, such as IoT, AI, blockchain, and data analytics.

Policy Implications: Analysis of regulatory frameworks, government policies, and incentives shaping the adoption and implementation of integrated transportation strategies.

Operational Efficiency: Examination of operational practices and optimization techniques to enhance efficiency in synchromodal and co-modal transport operations, including route planning,  scheduling, and resource allocation.

Environmental Impact: Assessment of the environmental footprint of synchromodal and co-modal transportation systems, including emissions reduction strategies, green logistics, and sustainable energy sources.

Economic Perspectives: Evaluation of the economic benefits and cost-effectiveness of integrated transportation approaches, including considerations of cost savings, revenue generation, and investment incentives.

Social Equity: Discussion on the social implications of synchromodal and co-modal transportation, including  accessibility, affordability, and inclusivity in urban and rural contexts.

Resilience and Risk Management: Investigation of strategies to enhance the resilience of transportation networks against  disruptions, including natural disasters, geopolitical events, and supply chain shocks.

Case Studies and Best Practices: Examination of real-world implementations of synchromodal and co-modal transportation     systems across different industries and regions, highlighting success     stories, lessons learned, and challenges encountered.

Intermodal Connectivity: Exploration of the integration and interoperability of different modes of transport (e.g.,  road, rail, sea, air) to achieve seamless and efficient freight and  passenger movements.

Collaborative Networks: Analysis of collaborative partnerships, alliances, and consortia driving innovation and cooperation in synchromodal and co-modal transportation ecosystems.

Education and Training: Discussion on the importance of education and training programs to build capacity and expertise in synchromodal and co-modal transportation practices among  stakeholders, including professionals, policymakers, and academia.

Future Trends and Emerging Technologies: Speculation on future trends and emerging technologies that may shape the evolution of   synchromodal and co-modal transportation systems, such as autonomous vehicles, drones, and hyperloop technology.

Please click the following link to get more detailed information:

https://www.emeraldgrouppublishing.com/calls-for-papers/exploring-synchromodality-and-co-modality-pathways-sustainable-and-efficient

附件2：

Special issueon

Intelligent Decision Making, Deep and Machine Learning, Generative AI to Empower Smart Production and Service

in Applied Soft Computing

Deep and machine learning, swarm intelligence, big data analytics, generative artificial intelligence, and LLM (Large Language Model) are increasingly developed for intelligent decision making involved in smart production and service in various industrial contexts. To meet with various needs of different customers, paradigm is shifting from mass production and mass-customization towards personalization and flexibility for smart production and service. In addition to the challenges in real settings, more opportunities are made available by state-of-the-art soft computing and AI technologies to empower novel applications for smart production and service to enhance supply chain resilience and change the business ecosystem.

Scope of this Virtual Special Issue:

Topics to be covered include the application of the following artificial intelligence and soft computing methodologies and interactions between several soft computing techniques:

Ant Colony Optimization  

Artificial Intelligence  

Big Data Analytics  

Convolutional Neural Networks 

Deep and Machine Learning  

Evolutionary Computing  

Fuzzy Computing  

Generative AI  

Hybrid Methods  

Immunological Computing  

Intelligent Decision-Making Technologies  

LLM (Large Language Model)  

Neuro Computing  

Particle Swarm Optimization  

Probabilistic Computing  

Rough Sets  

Wavelet 

to address critical, not restricted to the following aspects of smart production in real settings:

Advanced equipment/process control (AEC/APC)  

Automated material handling systems (AMHS), automatic guided vehicle (AGV)

Intelligent Decision technologies for Real-time  Decision

Equipment diagnosis, Predictive maintenance, and  Tool Health

Factory modeling, analysis and performance evaluation

Flexible Production Planning & Scheduling

Industry 4.0 & Manufacturing Strategy

Intelligent systems & Robots

Manufacturing Intelligence & Manufacturing Informatics

Mass personalization and customization

Predictive Maintenance

Smart Decision for Corporate Resource Planning &  Allocation

Sustainability and circular economy

Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR)

Yield enhancement and e-Diagnosis

Please click the following link to get more detailed information:

https://www.sciencedirect.com/journal/applied-soft-computing/about/call-for-papers#intelligent-decision-making-deep-and-machine-learning-generative-ai-to-empower-smart-production-and-service 

附件3：

Special Issue on

Digital Twin-enabled Smart Production and Logistics Systems

in IET Collaborative Intelligent Manufacturing

Digital Twin (DT) is one the most promising technologies in Industry 4.0 manufacturing. New challenges need to be addressed to fully tap the potential of DT in revolutionizing PL systems.This special issue aims to identify, discuss, and investigate frontier research problems in DT-enabled smart PL systems, explore how DT could be applied to model, simulate, and optimize PL systems for better productivity, cost-efficiency, resilience, and sustainability, as well as to provide fresh insights for both academia and industry.

Topics of interest include, but are not limited to:

DT-enabled production-logistics synchronization

DT-enabled production planning and control

DT-enabled logistics management

DT-enabled workload control

DT-enabled collaborative decision-making

DT-enabled resilient production and logistics

DT-enabled intralogistics

DT-enabled line balancing

DT-enabled material/part feeding

DT-enabled product design

DT-enabled system optimization

DT-enabled human-robot collaboration

DT-enabled sustainable manufacturing

DT-enabled resource allocation

Please click the following link to get more detailed information:

https://ietresearch.onlinelibrary.wiley.com/pb-assets/assets/25168398/Special%20Issues/IET_CIM_CFP_DT-1714092812313.pdf 

Please Be NOTED Submission Deadline is tentatively extended to 2025.1.31.