TITLE: New models and algorithms for vehicle routing and scheduling

Qie He, UMN


This talk consists of two parts. The first part focuses on speed optimization. Optimizing truck speed significantly reduces energy consumption and greenhouse gas emissions, and becomes viable recently with the arrival of self-driving trucks. We study two models for speed optimization: speed optimization over a fixed route and the joint routing and speed optimization problem over a network. We derive fast algorithms for both problems to obtain provably optimal solutions. In the second part of the talk, we introduce a model that aims to find vehicle routes and service schedules simultaneously to minimize the total transportation and customers' inconvenience cost, where each customer's inconvenience cost is modeled as a convex function of the service start time at that customer. We are able to extend our algorithm derived for the joint routing and speed optimization problem to this new model. Our algorithms significantly outperform state-of-the-art optimization software for all proposed models, and provide an opportunity for fleet managers to optimize fleet operations in real time.


BIO: Dr. Qie He is an assistant professor in the Department of Industrial and Systems Engineering, University of Minnesota. He obtained his PhD degree in Industrial Engineering in 2013 from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, working with Prof. Shabbir Ahmed and Prof. George Nemhauser. His main research interests are to solve large scale planning and dynamic operational problems in transportation and healthcare, with a methodological focus on optimization (integer and combinatorial optimization, global optimization, stochastic programming, etc.).