Machine Learning for Industrial Optimization and Predictive Control

Dear Colleagues,

The advent of Industry 4.0 and the concomitant rapid advancement of smart manufacturing have made machine learning (ML) into a fundamental technology driving innovation across a wide range of industrial applications. Such applications include process optimization, energy management, supply chain logistics, predictive maintenance, quality assurance, and intelligent transportation systems. The incorporation of ML in these domains has illustrated the significant potential to enhance operational efficiency, reduce costs, and elevate product quality, thereby conferring a competitive advantage for industries on a global scale. Despite the growing adoption of ML in industrial settings, applications of this technology present a number of critical challenges that require focused research and innovative solutions. Of principal concern is the guaranteed dependability and intelligibility of ML models, particularly in intricate and evolving contexts. Moreover, the integration of ML algorithms into existing industrial control systems, as well as the real-time monitoring and optimization of these systems, remain significant technical challenges. Another urgent issue is the development of robust and efficient ML algorithms that can perform effectively in scenarios where data are sparse, noisy, or of low quality.

This Special Issue aims to advance the state of the art in this field, pushing the boundaries of what ML can achieve in industrial applications. It will focus on publishing high-quality original research studies that address the challenges mentioned above by consolidating the latest advancements and innovative techniques in ML for industrial optimization and predictive control.

We welcome submissions of original research articles and review papers. Research topics of interest include, but are not limited to, the following:

• ML for manufacturing process optimization;
• Predictive maintenance and fault diagnosis;
• Supply chain and logistics optimization;
• Energy management and efficiency improvement;
• Real-time process monitoring and control;
• Reinforcement learning in industrial control;
• Deep learning applications in quality control;
• ML in the Industrial Internet of Things;
• Integration of digital twin technology with ML;
• Interpretability and reliability of ML models.

Prof. Dr. Chien-Chih Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

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.