Modelling and Performance Analysis of Cyclic Systems: 241 (Studies in Systems, Decision and Control)

Striking a balance between state-of-the-art research and practical applications, this book provides a forum for contributions that cover the main research challenges in the cyclic modeling, development, and validation of concurrently acting distributed production systems: systems that employ multi-assortment production in large quantities, are characterized by gradual changes in their product mix, and exclusively manufacture products in a cyclic manner. Similar issues also arise in computer systems, e.g., embedded systems. Cyclic optimization problems that occur in them are unique and under-researched, but are attracting new interest primarily due to their great practical importance and the difficulty involved in obtaining efficient algorithms for solving specific cases with real constraints arising from manufacturing practice. Addressing these and other topics, the book will be of great interest to researchers in computer science, operations management, and production control, as well as practicing managers and engineers.

​Wojciech Bożejko is an Associate Professor at Wrocław University of Science and Technology. He received his M.Sc. from the University of Wroclaw Institute of Computer Science in 1999, his Ph.D. from Wrocław University of Technology Institute of Engineering Cybernetics in 2003, and his D.Sc. (habilitation) from Wrocław University of Technology Faculty of Electronics in 2011. Since 2013, he has been an Associate Professor at the Wrocław University of Science and Technology (Politechnika Wrocławska), Faculty of Electronics, Department of Control Systems and Mechatronics. He is the author of over 230 peer-reviewed papers published in journals and conference proceedings, on parallel processing, scheduling and optimization. His research interests include methods for solving NP-hard problems, especially parallel algorithms, GPU computing, scheduling and discrete optimization, but also group theory and free probability.