Dr. Ayoub Chakroun | Industrial engineering | Most Reader’s Article Award

Researcher at Université paris 8, France

Dr. Ayoub Chakroun is an accomplished industrial engineer with expertise in production engineering, manufacturing optimization, and Industry 4.0 technologies. He holds a Ph.D. in Productique et Génie Industriel from the University of Paris 8 and ENIS, Sfax, where he conducted groundbreaking research on decision support systems for dynamic production scheduling and maintenance using digital twinning. With a rich academic background and extensive professional experience, Dr. Chakroun has made significant contributions to the field of industrial engineering. He has published numerous research papers and articles in reputable journals, focusing on topics such as digital transformation, predictive maintenance, and facility layout design in the context of Industry 4.0. In addition to his research endeavors, Dr. Chakroun has been actively involved in teaching and mentoring students at various academic institutions, including the University of Lorraine and IUT de Montreuil, Université Paris 8. He has also contributed to several industrial projects aimed at improving operational efficiency and implementing advanced manufacturing technologies.

Professional Profiles:

Education:

Ayoub Chakroun pursued his academic journey with a Bachelor’s Degree in Electromechanical Engineering from ENI Sfax, Tunisia (ENIS), where he specialized in Productique et Génie Industriel. Following this, he continued his studies at the University of Paris 8, Saint Denis, and ENIS, Sfax, completing a Ph.D. in Production Engineering and Industrial Engineering. During his doctoral studies, Ayoub focused on interdisciplinary research, delving into topics such as manufacturing processes, optimization techniques, and industrial management. His thesis, titled “Implementation of a Decision Support System for Dynamic Bi-Scheduling of Production and Maintenance via Digital Twinning,” reflects his commitment to advancing knowledge in production engineering. Prior to his doctoral studies, Ayoub completed his preparatory classes for Grandes Écoles at IPEI Sfax-Tunisia, where he received intensive training in mathematics, physics, and engineering fundamentals. This preparatory phase laid the groundwork for his subsequent academic pursuits and instilled in him a strong analytical mindset crucial for success in higher education. Ayoub’s secondary education was completed at Lycée Mohamed Ali in Sfax, Tunisia, where he obtained his High School Diploma with honors in Mathematics. This formative period not only sharpened his analytical and problem-solving skills but also fueled his passion for pursuing a career in engineering. Overall, Ayoub’s educational background spans both theoretical and practical aspects of engineering, providing him with a robust foundation for his professional endeavors in the field of production engineering and industrial management.

Professional Experience:

Ayoub Chakroun has gained extensive professional experience across various roles, contributing to his expertise in production engineering, industrial management, and academia. As an Assistant Professor at the University of Lorraine, UFR MIM, Ayoub is actively involved in delivering high-quality educational content in his areas of expertise, including production management, operational research, and production optimization. He also oversees research projects such as “R5BD” and “InterLud” and plays a key role in evaluating students’ project outcomes. Previously, Ayoub served as a Research and Industrialization Engineer at the University of Paris 8, where he led a significant project focused on implementing a decision support system for dynamic bi-scheduling of production and maintenance through digital twinning. In this capacity, he managed project teams, provided training in project management methodologies, and conducted manufacturing analysis. Ayoub has also contributed to academia as an Adjunct Lecturer at various institutions, including the IUT de Montreuil, Université Paris 8, and IUT de Tremblay, Université Paris 8. His teaching responsibilities have included practical workshops in supply chain and production management, mathematics tutoring, and instruction in digital skills and certification programs.

Honors:

Ayoub Chakroun possesses a comprehensive skill set encompassing various domains of engineering, management, and technical expertise. In the realm of engineering, Ayoub demonstrates proficiency in mechanical design, modeling, and simulation, utilizing software tools such as SolidWorks, Catia V5, and AutoCAD. His expertise extends to production engineering, including optimization techniques and lean manufacturing principles, ensuring efficient and cost-effective manufacturing processes. Additionally, Ayoub is well-versed in electromechanical systems and power electronics, with practical knowledge in the design and implementation of industrial automation solutions. Ayoub’s managerial skills encompass project management, strategic planning, and quality management practices. He has demonstrated leadership abilities as a project team leader, overseeing the successful execution of research initiatives and industrial projects. His experience in lean management and continuous improvement methodologies has enabled him to drive operational excellence and enhance productivity in manufacturing environments. Moreover, Ayoub possesses strong programming and development skills, particularly in languages such as Python and Flexscript, along with experience in utilizing optimization software like Cplex and ILOG. His proficiency in machine learning techniques contributes to advanced data analysis and predictive modeling capabilities, facilitating informed decision-making processes.In addition to technical and managerial competencies, Ayoub excels in communication and collaboration, enabling effective teamwork and project coordination. He has a proven track record of mentoring and teaching, fostering the development of students’ skills and knowledge in engineering and digital technologies. Overall, Ayoub Chakroun’s diverse skill set, spanning engineering, management, and technology, positions him as a versatile professional capable of driving innovation and success across various domains.

Research Interests:

Ayoub Chakroun’s research interests lie at the intersection of production engineering, industrial automation, and decision support systems. With a focus on leveraging digital technologies to optimize manufacturing processes, Ayoub explores innovative approaches to enhance production efficiency, reduce operational costs, and improve overall performance. One of Ayoub’s primary research areas involves the development of decision support systems for dynamic scheduling in production and maintenance activities. Through the integration of digital twinning and real-time data analytics, he seeks to enable proactive decision-making, ensuring optimal resource allocation and scheduling to minimize downtime and enhance productivity. Furthermore, Ayoub is interested in exploring the application of blockchain technology in supply chain management and reverse logistics. By harnessing the decentralized and transparent nature of blockchain, he aims to improve traceability, transparency, and efficiency in supply chain operations, particularly in the context of reverse logistics processes such as product returns, refurbishment, and recycling. Additionally, Ayoub investigates the implementation of Industry 4.0 technologies, including the Internet of Things (IoT), artificial intelligence (AI), and cyber-physical systems (CPS), to create smart and interconnected manufacturing environments. Through the integration of IoT sensors, AI algorithms, and advanced analytics, he aims to enable predictive maintenance, real-time monitoring, and adaptive control of manufacturing systems, leading to increased agility and responsiveness. Overall, Ayoub Chakroun’s research interests reflect his commitment to advancing the field of production engineering through the application of emerging technologies and data-driven decision-making approaches. By addressing key challenges in manufacturing and supply chain management, he seeks to contribute to the development of more efficient, sustainable, and resilient industrial systems.

📚Publications :

A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0 Authors: A Chakroun, Y Hani, A Elmhamedi, F Masmoudi Citations: 8 Year: 2022

Digital Transformation Process of a Mechanical Parts Production workshop to fulfil the Requirements of Industry 4.0 Authors: A Chakroun, Y Hani, A Elmhamedi, F Masmoudi Citations: 5 Year: 2022

Facility Layout Design through Integration of Lean Manufacturing in Industry 4.0 context Authors: A Chakroun, H Zribi, Y Hani, A Elmhamedi, F Masmoudi Citations: 5 Year: 2022

A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant Authors: AEFM Ayoub Chakroun, Yasmina Hani Citations: 3 Year: 2024

Development of Predictive Maintenance Models for a Packaging Robot Based on Machine Learning Authors: A Chakroun, Y Hani, S Turki, N Rezg, A Elmhamedi Citations: 1 Year: 2023

Application of Machine Learning for Predictive and Prognostic Reliability in Flexible Shop Floor Authors: A Chakroun, N Rezg Year: 2024

The establishment of a decision support system for dynamic scheduling of production and maintenance via digital twinning. Author: A Chakroun Year: 2023

Dynamic scheduling of a flexible manufacturing system driven by digital twins Authors: A Chakroun, Y Hani, A Elmhamedi, F Masmoudi Conference: ROADEF 2023 Year: 2023

SAGIP 2023 Predictive model for the evaluation of the health of an assembly unit based on machine learning in the context of industry 4.0 Authors: A Chakroun, Y Hani, A Elmhamedi, F Masmoudi

Dr. Yuanju Qu: Industrial engineering

Associate Researcher at Shenzhen University, china

Dr. Yuanju Qu is an accomplished researcher and Associate at Shenzhen University, specializing in Smart Manufacturing and Operations Research. With a Ph.D. in Mechanical Engineering from Shanghai Jiaotong University, he has a rich academic background. His diverse roles include serving as a Quality Engineer at IES Hengfeng Technology. Yuanju’s work spans publications in esteemed journals, multiple patents, and active involvement in cutting-edge projects, emphasizing his expertise in areas like dynamic production scheduling and IoT-based intelligent systems. His commitment to advancing Smart Manufacturing is evident in his ongoing research and contributions to the field.

Professional Profiles:

Scopus Profile

Orcid Profile

GoogleScholar profile

ResearchGate profile

Work Experience:

Current: Associate Researcher at Shenzhen University since Dec. 2020. Quality Engineer at IES Hengfeng Technology (Dongguan) Limited, Sep 2007 – Sep 2013.

Educational Background:

Ph.D. in Mechanical Engineering (Industrial Engineering), Shanghai Jiaotong University, China (QS: 43), Sep 2014 – Sep 2020. Master in Mechanical Manufacturing and Automation, Shenyang Ligong University, China, Sep 2009 – Jun 2013. Bachelor in Mechanical Manufacturing and Automation, Henan University of Science and Technology, China, Sep 2005 – Jul 2007. Mechatronics, Kaifeng University, China, Sep 2002 – Jul 2005.

Professional Experience:

Engaged in various research projects focusing on product quality prediction, quality traceability, and design of smart manufacturing systems. Holds expertise in areas such as process optimization, system requirements analysis, and enterprise information systems in smart manufacturing.

Patents:

Holder of multiple patents, including inventions related to dynamic production scheduling, root canal monitoring, IoT intelligent toilets, and software copyrights for air compressor quality prediction.

Research Interests:

Smart Manufacturing, Operations Research, Optimization, Prognostics and Health Management

Research Focus:

Yuanju Qu’s research focuses on advancing Smart Manufacturing and Operations Research. His work, including “Self-decision Mechanisms of Smart Production Systems” and “Multistakeholder’s Sustainable Requirement Analysis for Smart Manufacturing Systems,” highlights his commitment to innovation. With a keen interest in emerging technologies, his contributions to “Smart manufacturing systems: state of the art and future trends” demonstrate foresight into the industry’s trajectory. Additionally, his expertise extends to integrating fuzzy models and developing frameworks for online Prognostic and Health Management, showcasing a holistic approach to Industry 4.0. Yuanju Qu excels in creating efficient processes through Closed-loop Kaizen and discrete event simulation, contributing significantly to manufacturing excellence.