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Routing optimization with stochastic and correlated data

Assistant Profesesor
Lazaridis School of Business & Economics

Date:听June 15, 2022
Time: 1:00-2:00 PM
Venue:听MA 310, Sexton Campus
Online MS Teams:听

Abstract
Interaction between decisions is defined as a situation where the solution cost/benefit corresponding to a decision is affected (i.e., non-additive effects) by other decisions. In many planning problems arising in transportation, supply chain management, and logistics, the interactions between individual events or decisions contain crucial information that cannot be neglected. However, people tend to consider them exclusively when modeling decisions, mainly because it is easier to manage. For example, when decisions are made in the presence of large-scale stochastic data, it is common to pay more attention to the easy-to-see statistics (e.g., mean) instead of the underlying complex correlations. One reason is that it is often much easier to solve a stochastic optimization problem by assuming independence across data. This puts a large gap between the research and reality in situations where significant correlations exist in data.

In this talk, I will focus on routing optimization where travel times among different road segments are highly correlated, e.g., due to traffic congestion propagation. Congestion can cause a significant variation in travel times, especially during peak hours, and thus impact fuel consumption and, consequently, greenhouse gas emissions. For companies offering home delivery services, delays due to congestion directly affect the quality of the customer's experience and their costs. I present a unifying modeling framework to address travel time correlations and show how different stochastic, robust, and distributionally robust optimization models can be represented in this form. I will also show how to use data analytics techniques to improve the tractability of underline optimization algorithms.

Speaker Biography
Dr. Rostami is an Assistant Professor of Operations and Decisions Sciences at Lazaridis School of Business and Economics, Wilfrid Laurier University. He will join the University of Alberta as the CPA chair of business analytics in July 2022. Before joining Laurier, Borzou was a postdoctoral researcher at Polytechnique Montreal and the Technical University of Dortmund, Germany. He holds a PhD in Information Technology from the Polytechnic University of Milan, Italy. His research interests include optimization under interactions and uncertainty and decomposition methods for large-scale mixed-integer nonlinear optimization with applications in supply chain management, transportation and logistics.

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Industrial Engineering seminar series contact person:
Dr. Floris Goerlandt
email: floris.goerlandt@dal.ca

General Enquiry:
Ms. Tara Parker
Tel: 902.494.3281
email: tara.parker@dal.ca