Tushar Bhoite | Bayesian Networks and Decision Theory | Best Researcher Award

Published on by Statistics

Dr. Tushar Bhoite | Bayesian Networks and Decision Theory | Best Researcher Award

Dr. Tushar Bhoite | MES’s Wadia College of Engineering | India

Dr. Tushar Devidas Bhoite, Ph.D. in Mechanical Technology, M.E. in Machine Design, with over sixteen years of professional experience (including twelve in the automobile industry and four in academics), is a leading researcher and practitioner in integrating advanced IT technologies in auto-component manufacturing. His research interests encompass Industry 4.0, Industrial Internet of Things (IIoT), Digital Twin systems, Generative AI, predictive maintenance, neural and Bayesian networks, real-time monitoring, optimization and efficiency improvements in production environments. To date, he has authored 6 international journal papers and 3 international conference papers, co-authored 2 textbooks, and filed 2 patents, contributing to both theoretical and applied advancements. His master’s project, awarded from RGSTC Mumbai, stands as a landmark in his research portfolio, he has an h-index of 1 and over 1 citations as per his Scopus profile, reflecting strong scholarly impact. In his industrial roles, Dr. Bhoite has successfully led IOT implementations in assembly and press shops, improved Overall Equipment Effectiveness, standardized work systems, deployed TPM and Kaizen methodologies, and developed innovative solutions such as a wet-waste treatment machine. He currently serves as Assistant Professor in Automation & Robotics Engineering at MES’s Wadia College of Engineering, Pune, and continues consulting in production efficiency, resource optimization, and technology integration in manufacturing enterprises.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Bhoite, T. D., Pawar, P. M., & Gaikwad, B. D. FEA based study of effect of radial variation of outer link in a typical roller chain link assembly. International Journal of Mechanical and Industrial Engineering, 1, 65–70.

Bhoite, T. D., & Buktar, R. B. (2025). Productivity enhancement in Indian auto component manufacturing supply chain with IoT using neural networks. Production, 35, e20240047.

Bhoite, T. D., Buktar, R. B., Mahalle, P. N., Khond, M. P., Pise, G. S., & More, Y. Y. (2025). Productivity enhancement in the Indian auto component manufacturing supply chain through IoT, digital twins with generative AI, and stacked encoder-enhanced neural networks. Operations Research Forum, 6(4), 143.

Bhoite, D. T., & Buktar, R. (2025). An investigation into revolutionizing auto component manufacturing: An IoT-based approach for improved productivity and waste elimination. Journal of Information Systems Engineering and Management, 10(9s), 409–429.

Bhoite, D. T., Buktar, R., & Kannukkadan, G. (2024). Leveraging IoT in auto component manufacturing to monitor surface roughness and tool temperature. Journal of Electrical Systems, 20(2s), 563–574.*

Haifa Jammeli | Operations Research and Statistical Optimization | Best Researcher Award

Published on by Statistics

Dr. Haifa Jammeli | Operations Research and Statistical Optimization | Best Researcher Award

Normasys | France

Dr. Haifa Jammeli is a research fellow at the Institut Supérieur de Gestion de Tunis, specializing in business computing and operations research. She holds a PhD from the Higher Institute of Management in Tunisia and a Master’s degree in Logistics and Transportation Sciences from the Higher Institute of Logistics and Transportation, Sousse University. Her academic and professional journey spans over a decade, with significant contributions to supply chain optimization, AI in logistics, and sustainable urban planning. She is a part-time instructor at Paris Nanterre University and NEOMA Business School, teaching courses in supply chain management, operations research, data analysis, and IT project management. Her research focuses on optimizing transportation routes for COVID-19 patients and cash logistics using tools like CPLEX, QGIS, and Python, and she has developed AI models to forecast urban solid waste generation and propose green logistics solutions for household waste collection. With over 70 citations across nine publications, her work has been presented at international conferences and published in journals such as IEEE Transactions on Engineering Management and Annals of Operations Research. Her h-index is 10, reflecting both productivity and impact in her field. She has received awards including the Perficio Award for Best Woman Entrepreneur of the Year and was a finalist for the IFROS Prize for Operational Research in Development. Fluent in English, French, and Arabic, she combines strong technical skills (CPLEX, MATLAB, Python, R, SQL, QGIS) with experience in teaching, project leadership, and applying AI-based decision models to real‐world sustainability challenges.

Profiles: Scopus | Orcid

Featured Publications

Alaya, H., Jammeli, H., Ben Abdelaziz, F., Masmoudi, M., & Verny, J. (2024). Sustainable logistics for transfer of COVID-19 patients: Lesson learned from France. International Transactions in Operational Research.

Jammeli, H., & Verny, J. (2024). A multi-objective model for two-level distribution system in the city of Paris. Annals of Operations Research. (Accepted for publication)

Jammeli, H., Khefacha, A., Sellei, B., & Verny, J. (2023, October 18–21). The impact of AI tools in education environment. In 2023 IEEE ASEE Frontiers in Education Conference, College Station, Texas.

Jammeli, H., Alaya, H., & Verny, J. (2023, October 23–25). An analysis of the role of the Internet of Things and sensor technologies in optimizing waste management in the city of Sousse, Tunisia . World Recycling Convention, Madrid, Spain.

Jammeli, H., & Verny, J. (2022). A literature review for green smart home delivery problem in urban environments. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 756–760). IEEE.