Karol Suchan (Senior Member, IEEE) is an associate professor in the Faculty of Engineering and Sciences at the Universidad Diego Portales (UDP) and the head of CITYLOG UDP, a center for innovation in transport and logistics. He received his MSc in mathematics from the AGH University (Poland) in 2003, and MSc and PhD in computer science from the University of Orleans (France) in 2003 and 2006, respectively. Before joining the UDP, he was a postdoc at the Department of Mathematical Engineering at the Universidad de Chile (2006-2008) and an associate professor at the Faculty of Engineering and Sciences at the Universidad Adolfo Ibañez (2008-2019). His interests include mathematical modeling and algorithms, and their applications in decision support systems. He has been a principal researcher in diverse projects funded by Conicyt/Anid (Chile) and Inria (France). He also has participated in consulting projects for both the public and private sectors. He currently serves as a director of the Chile Section of the Institute of Electrical and Electronics Engineers (IEEE) and the chair of the Chile Chapter of the IEEE Computer Society.
Abstract: Our study delves into analyzing container flows within a maritime terminal’s yard, focusing on workload distribution and vehicle movements. Our key objective is to create a yard simulator using statistical modeling, enabling us to evaluate diverse vehicle dispatching rules governing gate, storage block, and terminal traffic. Uniform workload distribution is proven to enhance productivity by minimizing unproductive container moves, shortening service queues, and easing personnel and machinery burden. Our investigation extensively analyzed workload distribution at a Chilean maritime terminal, using a dataset spanning 2016 to 2022. We revealed instances of non-uniform distribution, indicating potential for optimization. The terminal’s layout includes storage sections and a break-bulk cargo area, each with unique gates for truck and rail cargo. Our approach heavily relies on statistical modeling to derive insights from data. Future work entails leveraging the simulator to devise strategies for optimizing distribution and ultimately bolstering terminal efficiency.