Quinlan School of Business, Loyola University Chicago
April 27, 2018, 12:15, Room GC B330 (click here for the map)
Time-expanded networks are a useful tool from both modeling and computational perspectives. In terms of modeling, they enable a natural method for representing decisions that have both a geographic and temporal component. In terms of computation, they yield stronger integer programming formulations than those that represent time with continuous variables, which in turn require less time to solve. A drawback to the use of time-expanded networks is that they require time to be discretized. While finer discretizations yield more precise representations of time, they also lead to larger optimization models which may then require too much time to solve. However, this trade-off is primarily a function of choosing a discretization in a static and a priori manner. In this talk, we will present a method that generates time expanded networks in an iterative and dynamic fashion in the context of solving an optimization model that prescribe actions in both time and space. We will illustrate the use of this method on two classical problems seen in transportation and logistics: (1) the Service Network Design problem, which can be used to model the routing of goods between cities, and, (2) the Traveling Salesman Problem with Time Windows, which can be used to model the routing of goods within a city.
Dr. Hewitt is an Associate Professor in the Information Systems and Supply Chain Management Department in the Quinlan School of Business at Loyola University Chicago, where he also serves as the Director of graduate programs in Supply Chain Management. His research includes developing quantitative models of decisions found in the transportation and supply chain management domains, particularly in freight transportation and home delivery. His work has assisted the decision-making of companies such as Exxon Mobil, Saia Motor Freight, and Yellow Roadway. He has expanded his area of expertise to include workforce planning, including working on multi-disciplinary projects at the intersection of operations management and cognitive psychology. His research has been funded by agencies such as the National Science Foundation, the Material Handling Institute, and the New York State Health Foundation. Before entering the PhD program at Georgia Tech, Dr. Hewitt worked as a software engineer, contributing to the development of software to support consumer set-top boxes and LED signs in mass transit stations.