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Dr.Tsegay Tesfay Mezgebe : Industrial and Systems Engineering| Best Researcher AwardΒ
Dr. Tsegay Tesfay Mezgebe is an accomplished academic with a Ph.D. in Automatic Control from the University of Lorraine, France. He earned his Master’s in Production and Industrial Systems Engineering from Mekelle University, Ethiopia, and a Bachelor’s in Industrial Engineering from Bahir Dar University. π His expertise extends to meta-heuristics, cyber-physical production systems, and scientific integrity, as showcased by certifications from renowned institutions in France. Dr. Mezgebe, an Assistant Professor at Mekelle University since 2020, previously held academic roles and demonstrated commitment to quality management and ethical research. ππ¨βπ«
Publication Profile :
Academic Degrees:
Dr. Tsegay Tesfay Mezgebe is an accomplished academic, holding a Ph.D. in Automatic Control from the University of Lorraine, France, awarded on January 30, 2020. His educational journey includes a Master’s degree in Production and Industrial Systems Engineering from Mekelle University, Ethiopia, conferred on October 27, 2011. Dr. Mezgebe’s foundation in academia began with a Bachelor’s degree in Industrial Engineering from Bahir Dar University, Ethiopia, earned on June 06, 2007. π His diverse academic background reflects a deep commitment to engineering and control systems, contributing to his impactful career in academia and research. π¨βππ
Certifications and Trainings :
Dr. Tsegay Tesfay Mezgebe’s commitment to professional development is evident through a series of specialized courses and certifications. In 2019, he delved into “Meta-Heuristics in Optimization” at Ecole MACS, University of Bordeaux, Bordeaux, France, exploring current trends and evolutions. π Following this, he immersed himself in “Cyber-Physical Production Systems” at IUT de Nantes, University of Nantes, Nantes, France, in 2019. π€ Dr. Mezgebe also prioritizes ethical research, having undertaken courses on the “Culture of Scientific Integrity” and the “Ethical and Legislative Framework for Research” at UniversitΓ© de Lorraine, France, in 2018 and 2017, respectively. These initiatives showcase his dedication to staying at the forefront of his field. π¨βπ«π
Computer and other personal skills :
Dr. Tsegay Tesfay Mezgebe possesses versatile language skills, excelling in his mother tongue Tigrigna and demonstrating excellent proficiency in English, both in listening, reading, speaking, and writing. π£οΈπ He is proficient in French, with good capabilities in listening, reading, and speaking, while maintaining satisfactory writing skills. π«π· Additionally, Dr. Mezgebe is fluent in Amharic, showcasing excellent skills across all language components.In the realm of software, his expertise extends to MatLab, Paython, Java, Eclipse, Tex/Latex, AutoCAD, and Solid Works. π» For statistical analysis, he is adept at using tools such as Minitab, MatLab, Click Charts, and SPSS. π His manufacturing skills cover a broad spectrum, including machining, welding, foundry, sheet metal, and woodworks, reflecting a well-rounded proficiency in both theoretical and practical aspects of his field. π§π
Research Focus :
Dr. Tsegay Tesfay Mezgebe’s research spans diverse areas, with a primary focus on intelligent manufacturing systems. His work includes pioneering studies on multi-agent systems negotiation for dynamic scheduling, as seen in the Journal of Intelligent Manufacturing and The International Journal of Advanced Manufacturing Technology. π€ His expertise extends to algorithm design for control systems in the context of Industry 4.0, evident in his contributions to recursive hybrid control architecture. Dr. Mezgebe’s research also delves into economic analyses of lean wastes in textile industries and explores the post-COVID-19 recovery opportunities for Sub-Saharan manufacturing, as showcased in Scientific African. ππ
Publication Top Notes :
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Briquetting of charcoal from sesame stalk
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Economic analysis of lean wastes: case studies of textile and garment industries in Ethiopia
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Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context
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CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation
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Service Orientation in Holonic and Multi-Agent Manufacturing: Proceedings of SOHOMA 2017
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Multicriteria decision-making method for scheduling problem based on smart batches and their quality prediction capability
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Intelligent manufacturing eco-system: A post COVID-19 recovery and growth opportunity for manufacturing industry in Sub-Saharan countries
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A negotiation scenario using an agent-based modelling approach to deal with dynamic scheduling
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A negotiation-based control approach for disturbed industrial context
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The need to localize energy technologies for Africa’s post COVID-19 recovery and growth
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Recursive hybrid control architecture to deal with reactivity in the context of industry 4.0