Contatto di riferimento: Prof. Daniele Tarchi
About the speaker
Dr. Angel A. Juan is a Full Professor of Operations Research & Industrial Engineering in the Computer Science, Multimedia, and Telecommunication Department at the Universitat Oberta de Catalunya (Barcelona, Spain). He is also the coordinator of the ICSO research group at the Internet Interdisciplinary Institute (IN3). Dr. Juan holds a PhD in Industrial Engineering and an MSc in Mathematics. He completed a predoctoral internship at Harvard University and postdoctoral internships at the Massachusetts Institute of Technology and the Georgia Institute of Technology. His main research interests include applied optimization and simulation (metaheuristics and simheuristics) in computational transportation and logistics and telecommunication systems. He has published more than 65 articles in JCR-indexed journals and more than 170 documents indexed in Scopus. His website address is http://ajuanp.wordpress.com and his email address is ajuanp@uoc.edu.
Abstract
This presentation introduces the concept of biased-randomized algorithms (BRAs), which has been applied during the last years to solve complex optimization problems in areas such as logistics and transportation, production, computational finance, telecommunication systems, and smart cities. BRAs make use of fast heuristics to generate good-quality solutions to medium- and large-sized optimization problems. These heuristics, which typically follow a deterministic set of rules, are transformed into probabilistic algorithms via the introduction of a biased (non-uniform) random behaviour to better guide the searching process. This is achieved by using a skewed probability distribution, such as a geometric one. These BRAs can be run in parallel, thus leading to "real-time" solutions of high-quality. Some applications in the areas of telecommunication systems and smart city logistics are also reviewed in order to illustrate the potential of these algorithms, which can also be part of more elaborated metaheuristic or simheuristic frameworks.