Designing Smart Manufacturing Systems

Daniel Alejandro Rossit, PhD is a Researcher of CONICET (National Research Council of Argentina) and Adjunct Professor in the Engineering Department of the Universidad Nacional del Sur, Bahía Blanca, Argentina. He has an Industrial Engineering degree and a PhD in Engineering. His research focuses on production problems, operations research and engineering systems optimization. He has published in journals such as Omega, International Journal of Production Research, The International Journal of Advanced Manufacturing, Computers and Electronics in Agriculture, and International Journal of Computer Integrated Manufacturing. He is a member of the editorial board for IET Collaborative Intelligent Manufacturing and Operational Research in Engineering Sciences: Theory and Applications. He has served as editor in of Evolution and Trends of Sustainable Approaches: Latest Development and Innovations in Science and Technology Applications and Designing Smart Manufacturing Systems. Currently, he advises Master's, Ph.D., and Postdoctoral students and has led technology development projects for industrial companies.Prof. Chaudhery Mustansar Hussain, PhD, is an adjunct professor and director of laboratories in the Department of Chemistry & Environmental Sciences at the New Jersey Institute of Technology (NJIT), Newark, New Jersey, United States. His research focuses on sustainability, the applications of nanotechnology and advanced materials, environmental management, analytical chemistry and other various industries. Dr. Hussain is the author of numerous papers in peer-reviewed journals as well as a prolific author and editor of around 200 books, including scientific monographs and handbooks in his research areas. He has published with ELSEVIER, American Chemical Society, Royal Society of Chemistry, John Wiley & Sons, CRC Press, and Springer.

• I Smart manufacturing design1. Cloud manufacturing implementation for smart manufacturing networks2. Improving Brazilian Engineering Education: real engineering challenges in an IIoT undergraduate courseII Industry 4.0 information technology developments3. New verification and validation tools for Industry 4.0 software4. Stepping stone to smarter supervision: a human-centered multidisciplinary frameworkIII Industry 4.0 business developments5. How to define a business-specific smart manufacturing solution6. Assessment of the competitiveness and effectiveness of the business model 4.07. Sustainable Business Models in the context of Industry 4.08. Understanding Digital Transformation challenges: evidence from Brazilian and British manufacturers9. Smart green supply chain management: a configurational approach for reaching sustainable performance goals and decreasing COVID-19 impact10. Multicriteria Decision Making approach for selection and prioritization of projects into the Digital Transformation journeyIV Industry 4.0 production planning and decision making11. Smart manufacturing scheduling with Petri nets12. Characterizing nervousness at the shop-floor level in the context of Industry 4.013. Digital and smart production planning and control14. Simulation-based generation of rescheduling knowledge using a cognitive architecture