Advances in AI and Optimization for Scheduling Problems in Industry
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 913
Special Issue Editors
Interests: stochastic optimization; AI and machine learning; integrated learning and optimization; distributionally robust optimization; smart service and scheduling
Interests: cyber-physical systems; dependable controllers for dependable mechatronic systems; mechatronic systems design for medical/biomedical applications, wellbeing and/or rehabilitation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In today's rapidly evolving industrial landscape, efficient scheduling remains a critical challenge that significantly impacts productivity and operational effectiveness. The integration of artificial intelligence (AI) and advanced optimization techniques offers promising solutions to these scheduling problems, enabling industries to enhance their decision-making processes and operational efficiency. This Special Issue, "Advances in AI and Optimization for Scheduling Problems in Industry", aims to explore the latest advancements and applications of AI and optimization methods in tackling complex scheduling problems across various industrial sectors.
Industries such as manufacturing, logistics, healthcare, and energy are increasingly leveraging AI-driven optimization to address intricate scheduling challenges. The adoption of technologies such as machine learning (ML), natural language processing (NLP), and cyber–physical systems (CPS) is revolutionizing how scheduling problems are approached and solved. These technologies facilitate real-time data analysis, predictive maintenance, adaptive scheduling, and resource allocation, thereby optimizing the overall production process and reducing downtime.
This Special Issue seeks to highlight the confluence of AI and optimization techniques in industrial scheduling, focusing on innovative research that demonstrates the practical applications and benefits of these technologies. Contributions on a wide range of topics are invited, including but not limited to the use of AI for predictive scheduling, the role of optimization algorithms in dynamic environments, the integration of IoT for smart scheduling, and the impact of AI on workforce management.
We welcome submissions that provide new insights, propose novel methodologies, or present case studies that illustrate successful implementations of AI and optimization in industrial scheduling. This Special Issue aims to serve as a comprehensive resource for researchers, practitioners, and policymakers, fostering a deeper understanding of how AI and optimization are shaping the future of industrial scheduling.
Dr. Ran Ji
Dr. Jose Machado
Dr. Zhengyang Fan
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence (AI)
- optimization techniques
- smart scheduling
- machine learning (ML)
- predictive maintenance
- decision-focused learning
- integrated learning-and-optimization
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