Industrial engineering is one of the earliest fields to utilize computers in research, education, and practice. Over the years, computers and electronic communication have become an integral part of industrial engineering. Computers & Industrial Engineering (CAIE) is aimed at an audience of researchers, educators and practitioners of industrial engineering and associated fields.

It publishes original contributions on the development of new computerized methodologies for solving industrial engineering problems, as well as the applications of those methodologies to problems of interest in the broad industrial engineering and associated communities. The journal encourages submissions that expand the frontiers of the fundamental theories and concepts underlying industrial engineering techniques.

CAIE also serves as a venue for articles evaluating the state-of-the-art of computer applications in various industrial engineering and related topics, and research in the utilization of computers in industrial engineering education. Papers reporting on applications of industrial engineering techniques to real life problems are welcome, as long as they satisfy the criteria of originality in the choice of the problem and the tools utilized to solve it, generality of the approach for applicability to other problems, and significance of the results produced.

A major aim of the journal is to foster international exchange of ideas and experiences among scholars and practitioners with shared interests all over the world.

Special Issue on Towards Smart, Sustainable, and Resilient Systems
截稿日期: 2025-10-31

Guest editors:

Dr. Ripon K. ChakraborttyUniversity of New South Wales Canberra at ADFA, Canberra Bc, Australia

Dr. Hasan Turan
University of New South Wales Canberra at ADFA, Canberra Bc, Australia

Special issue information:

The 51st International Conference on Computers and Industrial Engineering (CIE51) was co-hosted by the Decision Support & Analytics Research Group (DSARG), from the School of Systems & Computing (Syscom) at UNSW Canberra (Australia) and the Center for Sustainable Operations and Supply Chain Resilience (CSOSRC), at the University of Adelaide (Australia), between 9th and 11th December 2024. CIE51 provides an unparalleled opportunity to explore the latest advancements in industrial engineering and supply chain innovation. It fostered collaboration among researchers, practitioners, and industry experts, facilitating meaningful discussions that shape the future of smart, sustainable, and resilient systems (the conference theme for CIE51 was “Towards Smart, Sustainable, and Resilient Systems”). Industrial engineering and supply chain resilience are critical for bolstering Australia’s sovereign capabilities. In recent years, global disruptions have underscored the importance of resilient, locally controlled supply chains. The Australian government’s focus on Sovereign Industrial Capability Priorities highlights the need for innovation in areas such as combat survivability, cyber resilience, and advanced manufacturing. By addressing these challenges, CIE51 plays a pivotal role in supporting national security and economic independence. From "Supply Chain 5.0" to "Enabling Supply Chain Resilience Through Data-Driven Insights," the topics covered in this conference reflect the pressing issues of our time and pave the way for transformative solutions.

This special issue aims to comprise a few very informative and resourceful works seeded during the conference with sufficient extension or technical enhancement to solve the problems towards smart, sustainable, and resilient systems.

The special issue invites authors to contribute around but not limited to the following topics:
1. Smart Manufacturing Systems
2. Industrial Internet of Things
3. Cyber-Physical Systems
4. Humanitarian and Emergency Logistics
5. Simulation Optimization & Applications
6. Manufacturing Innovation
7. Resilient Smart Factories
8. Reliability Engineering and Predictive Maintenance
9. Human Factors Engineering and Ergonomics
10. E-Business and E-Commerce
11. Sustainable and Smart Transportation Systems
12. Quality Control and Management
13. Decision Support Systems
14. Production Planning and Operations Scheduling
15. Technology and Knowledge Management
16. Digital Transformation
17. Advanced Engineering Simulations
18. Defence Technologies
19. Engineering Management
20. Big Data Analytics & Applications
21. Resilience Supply Chains and Logistics Model
22. Operations Research and Optimization
23. Facilities Planning and Design
24. Lean Manufacturing and Six Sigma
25. Design for X
26. Data Mining and Business Intelligence
27. Artificial Intelligence and Machine learning applications
28. Product Process, Service, and Software Design Management
29. Reverse Engineering
30. Smart Cities Management Models
31. Healthcare Management Systems
32. Sustainable Supply Chain Management
33. Project Management and Control
34. Enterprise Resource Planning Systems
35. Circular Economics
36. Optimization of renewable energy resources
37. Vehicle routing

Manuscript submission information:

Submission Due Date: 31 October 2025

Authors are advised to select the article type of 'VSI: Resilient Systems-CIE51' when submitting manuscript via the editorial system of the journal: https://www.editorialmanager.com/caie/default.aspx 

Keywords:

Industrial Engineering; Supply Chain; Resilience; Sustainability; Smart; Computer Science; Artificial Intelligence

Special Issue on Machine Learning and Optimization Methodologies in Railway Transportation
截稿日期: 2025-12-31

Railway optimization problems are inherently complex due to various factors, such as safety requirements, capacity limitations, and large-scale problem sizes. This complexity makes precise modeling and obtaining accurate solutions highly challenging, often necessitating a trade-off between computational performance and solution quality. In recent years, the rapid development of Machine Learning (ML) has driven innovative research in railway transportation, bringing many advantages and solutions. The application of ML enables in-depth analysis and exploration of the massive data generated within railway systems, uncovering hidden patterns and trends. Moreover, ML can also overcome the limitations of traditional methods. For example, when combined with optimization methodologies, ML may optimize the decision-making process, provide an innovative and effective means to significantly enhance the accuracy, timeliness, and scientific nature of railway management decisions. Therefore, the comprehensive application of ML and optimization methodologies holds enormous potential for addressing challenges in railway transportation.

Guest editors:

Prof. Maged Dessouky
University of Southern California, Los Angeles, CA
maged@usc.edu

Prof. Lixing Yang
Beijing Jiaotong University, Beijing, China
lxyang@bjtu.edu.cn

Dr. Xiaoming Xu
Hefei University of Technology, Hefei, China
xmxu@hfut.edu.cn

Special issue information:

This Special Issue on “Machine Learning and Optimization Methodologies in Railway Transportation” aims to provide a unique platform for researchers and practitioners from academia and industry in railway transportation to share their latest developments, methods, and practices in using ML and optimization technologies to address challenges in railways. With a focus on the integration of ML and OR techniques into Railway Transportation, this SI will encompass a wide range of topics, including but not limited to:

    Train scheduling, rescheduling
    Train marshalling, shunting
    Rolling stock scheduling
    Line planning
    Railway maintenance planning
    Railway infrastructure management
    Railway construction project scheduling
    Railway traffic simulation
    Crew scheduling, rostering
    Intelligent railway transportation systems
    Green and low-carbon railway transportation
    Passenger demand prediction in railways
    Big data analytics in transportation
    Traffic safety and risk management

Special Issue on Industrial AI Empowered Smart Manufacturing
截稿日期: 2026-03-15

Guest editors:

Dr Yiwei Wang,
Manager Guest Editor, Associate Professor, Beihang University, Beijing, China
Email: wangyiwei@buaa.edu.cn 

Dr Christian Gogu,
Professor, Institut supérieur de l'aéronautique et de l'espace, ISAE-SUPAERO Toulouse, Toulouse, France
Email: christian.gogu@isae-supaero.fr

Dr Pai Zheng,
Associate Professor, The Hong Kong Polytechnic University, Hong Kong, Hongkong
Email: pai.zheng@polyu.edu.hk

Dr Yang Zhang,
Professor, Dalian University of Technology, Dalian, China
Email: zy2018@dlut.edu.cn

Dr Jiewu Leng,
Professor, Guangdong University of Technology, Guangzhou, China
Email: jwleng@gdut.edu.cn

Special issue information:

The merging and penetrating of AI into various industries is accelerating the new need for new smart manufacturing methodologies and approaches. Industrial AI is an interdisciplinary research field and is redefining manufacturing and service paradigms. Industrial AI deeply integrates industrial scenarios with advanced AI models/methods such that industrial systems have enhanced perception, cognition, learning, organization, self-decision-making, execution, and mutual adaptation capabilities, which empower the entire product life cycle including design, processing (both machining and additive manufacturing), assembly, operation management, maintenance and services to improve manufacturing quality, efficiency, flexibility, and effectiveness, fostering a human-centric, adaptive, and sustainable industrial ecosystem. The integration of Industrial AI in key domains such as human-robot collaboration (HRC), intelligent assembly, smart operation and maintenance has accelerated the evolution of manufacturing. For instance, the collaborative intelligent assembly of large-scale aerospace products with wearable mixed reality devices has transformed traditional manual operations into intelligent, interactive, and guided paradigms. Similarly, predictive maintenance systems based on advanced prognostics and health management architectures are optimizing the operation and maintenance of critical industrial equipment. To advance the adoption and innovation of industrial AI in manufacturing and services, this Special Issue aims to explore the latest developments in theories, methodologies, tools, systems, and case studies that exemplify intelligent empowerment across various industrial applications.

Topic Areas

Relevant topics for the special issue would include, but would not be limited to, the following:

    Generative AI based scheduling and optimization for dynamic and agile production
    Generative AI in learning policy, decision-making, and advanced control for manufacturing systems
    Generative AI based self-sensing/cognizing/planning/execution in Human-Robot interactions and collaboration in assembly and maintenance.
    Generative AI based solutions for resource allocation and workflow management in assembly and maintenance
    Generative AI based prognostics and health management
    Intelligent systems for ergonomic and empathic collaboration
    Reconfigurable manufacturing systems for adaptive production

Manuscript submission information:

Submission Open Date: 15 September 2025
Submission Due Date: 15 March 2026

Authors are advised to select the article type of 'VSI: Industrial AI Empowered Smart Manufacturing' in the editorial system of the journal, to make sure the submissions are grouped under this special issue for the review process. 

Keywords:

Generative AI, Industrial AI, Assembly, Maintenance and Operation, Reconfigurable Manufacturing Systems