Welcome reception

• Time: Sunday, July 23, from 18:00 – 20:00
• Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor
• You must visit the check-in desk in Schreiber (16 E. Pearson Street) to get your badge before you can enter Lewis Towers

Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor

Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor

OPTIMIZATION OPPORTUNITIES AND RESULTS FOR SHARED AUTONOMOUS (AND ALL-ELECTRIC) VEHICLE FLEETS ACROSS U.S. SETTINGS.

Shared autonomous vehicles (SAVs or “driverless taxis”) can complement public transit systems by offering first-mile last-mile connections to line-haul transit. Smart fleets will rely on rapid optimization techniques to improve routing, battery charging, and repositioning decisions in order to deliver more reliable, safe, and cost-effective transportation options.

This presentation will describe how SAV trip requests across the 20-county Chicago region were matched to SAVs, to one another (shared rides), and to time-saving transit stations (for intermodal trips) using routing optimization modules. Joint routing increased transit ridership from 5.4% to 6.3% and SAV utilization levels by 12%, with only a 4% increase in SAV fleet VMT (as compared to routing all SAV trips door-to-door). 

When using all-electric SAEVs, battery-charging decisions become very important for optimal service. A simulation of SAVs serving the 6-county Austin region suggests that optimal SAEV-dispatch decisions lower traveler wait times by 39%, increase fleet use (non-idle periods) by 28%, and lower empty VMT by 1.6% points. If objectives include lowering electricity costs and emissions, optimal charging and dispatch of Austin SAEVs saves $0.79 per SAEV per day on energy costs while avoiding $0.43 in emissions damages. Scheduling charging to lower energy and emissions costs allows each vehicle to serve another trip and net another $8 per day in revenues.

Dr. Kara Kockelman is a registered professional engineer and holds a PhD, MS, and BS in civil engineering, a master’s in city planning, and a minor in economics from the University of California at Berkeley. Dr. Kockelman has been a professor of transportation engineering at the University of Texas at Austin for 25 years. She has authored over 200 journal articles (and two books), and her primary research interests include planning for shared and autonomous vehicle systems, the statistical modeling of urban systems, energy and climate issues, the economic impacts of transport policy, and crash occurrence and consequences. Pre-prints of these articles (and book contents) can be found at www.caee.utexas.edu/prof/kockelman. 

ABSTRACT:

Julia is a relatively new programming language that is rapidly gaining popularity in scientific computing, data analytics and industrial process optimization. Julia takes "walks like Python, runs like C" approach and is a perfect replacement for Matlab, Python and R data science workflow, yet due to its speed it can be also used to implement computation intensive algorithms that are normally implemented in languages such as  C++. One area that has been particularly developed is a rich toolset for mathematical programming models built around JuMP.jl ecosystem. Julia provides a rich, easy to use and very powerful set of packages including for LP, MILP, MINLP, QP, SOCP optimization problems. The JuMP.jl platform provides a single Julia-based domain specific mathematical programming language that supports over 40 solvers including all commercial and major Open Source packages as well as solvers written directly in Julia.

In this 2-hour intense hands-on workshop you will learn how to start building optimization models in Julia and JuMP.jl. No previous Julia programming experience is required. 

About the instructor:

Przemysław Szufel is an Assistant Professor in Warsaw School of Economics, Research lab member, Computational Methods in Industrial Mathematics Laboratory, Fields Institute (located within Toronto University) and an Adjunct professor,  Cybersecurity Research Lab, Toronto Metropolitan University. He is a co-author of the book "Julia 1.0 Programming Cookbook" (translated by O'Reilly to Japanese). Actively participates in the Julia community, maintains three official Julia packages, and holds 2nd place on the StackOveflow portal answering Julia-related questions. For three years he has been using Julia in industrial and academia optimization projects – he has successfully applied JuMP.jl in large scale optimization projects in the areas of production planning, manufacturing process design and intralogistics.

Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor

• Time: Tuesday, July 25, 18:30 – 10:30 with dinner served at 19:30
• Location: The Signature Room at the 95^th, 875 N. Michigan Avenue, Chicago, IL 60611
• Dress code: Thank you for not wearing hats, athleticwear, shorts, or ripped/torn jeans. Flip flops or beach shoes are not permitted. Jackets & ties are optional. Collared shirts on men are required
• Arrival instructions:
  • The main entrance to The Signature Room’s bank of elevators is on the Delaware Place entrance of the building. Delaware is a one way going east off Michigan Avenue and there is a set of revolving doors in between the North Face and Hanig’s Footwear.
  • Let the hosts at the ground level know that you are a guest of the private dining event, and the hosts will place you on the next available elevator to the 95th floor. Private dining events have priority access up the elevators and our hosts will be anticipating your arrival. The ride up is about 46 seconds. Our hosts on the 95th floor will greet you and lead you to the entrance of the event space. Signage will be posted at the hallway leading to the private dining rooms and outside the entrance of the space.

Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor

Location: Regent’s Hall on 16^th floor of Lewis Towers with overflow into Beane Hall on 13^th floor

ENABLING TRANSPORTATION OPTIMIZATION AND RESILIENCE ANALYSES

Transportation infrastructure is critical to freight movements and supply chain performance. Enabling scenario exploration, particularly under potential disruption conditions, is critical to making good decisions about freight movements and resilient infrastructure investments.  Yet there are limited open-source tools available to help supply chain participants and transportation planners evaluate the intersection between transportation infrastructure and freight. Open-source tools using inputs that users typically already possess or can easily acquire are particularly rare for resilience analyses.  The U.S. Department of Transportation has developed two distinct tools to support these kinds of analyses, the Freight and Fuel Transportation Optimization Tool, which optimizes supply chain freight movements across a multimodal transportation network, and the Resilience and Disaster Recovery Tool Suite, which helps estimate transportation network exposure to hazards and evaluate the return on investment for resilience projects aimed at mitigating uncertain future hazard conditions.  Dr. Lewis will discuss the approaches these two tools take to enable transportation optimization and resilience analyses.

Kevin Zhang, PhD, joined the U.S. DOT Volpe Center in 2020 as a data scientist in the Energy Analysis and Sustainability Division. He provides technical support on projects related to resilience of transportation networks, supply chain optimization, and alternative fuels. Zhang also provides support on project work related to the deployment of connected and automated vehicles. Prior to joining the U.S. DOT Volpe Center, Zhang received a doctorate in operations research at MIT. His research focused on developing analytical models for the real-time calibration of traffic simulators. He also worked previously as an operations research analyst at an analytics consulting firm in Boston.