Logistic systems, often face big challenges when making operational decisions, mainly due to existence of unpredictable and uncontrollable variables. Obviously, the complexities are more pronounced as problems scale up, often with exponential rates. Large and various datatypes generated from different resources means more variables to calibrate and tune and more models to validate. It demands use and development of state-of-the-art optimization models and machine learning tools, that are robust but also adoptable and transferrable from one application to another. In this presentation, an overview of different distributed mathematical models will be presented, it will be followed by the use cases of these approaches in different transportation problems. Finally, we will discuss how transportation experts, can benefit from the cloud computing and the advantages that it can offer through a practical example.
Dr. Mahdieh Allahviranloo is an associate professor of civil engineering, at the City College of New York (CCNYCUNY). She is also a Visiting Academic at Amazon, working with the Amazon Last Mile Science team, and is currently spending her sabbatical from the university. After getting her civil engineering degree in Iran, she got her PhD in transportation engineering at the University of California, Irvine. In her academic research, she integrates data mining, statistical analysis, and optimization models to study how individuals move in urban areas. During her sabbatical at Amazon, she uses her academic training to solve a wide range of problems in the last mile logistic domain.
