Webinars

The Promise and the Challenge of Advanced Air Mobility

Event Info

icon_calendar.jpgJune 3, 2022
icon_clock.jpg10am ET
icon_stopwatch.jpgDuration: 1 hour

Speakers

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Hamsa Balakrishnan

I am the William E. Leonhard (1940) Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), where I lead the Dynamics, Infrastructure Networks, and Mobility (DINaMo) Research Group. My current research interests are in the design, analysis, and implementation of control and optimization algorithms for large-scale cyber-physical infrastructures, with an emphasis on air transportation systems. These include airport congestion control algorithms, air traffic routing and airspace resource allocation methods, machine learning for weather forecasts and flight delay prediction, and methods to mitigate environmental impacts. My research spans theory and practice, including both algorithm development and real-world field tests.

ABSTRACT: Advanced Air Mobility (AAM) operations – characterized by electric and hybrid aircraft, and highly-automated operations – are expected to dramatically transform the way in which we transport people and goods, as well as our ability to sense our world from the sky. The deployment of new vehicle types, business models, and aircraft operators will increase competition for already constrained airspace resources. Consequently, fairness and privacy, in addition to the traditional goal of efficiency, will become important considerations. In this talk, I will discuss emerging AAM traffic management challenges, and some of our initial work in overcoming them and realizing the promise of Advanced Air Mobility.

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Warehouse Software: What to Know to Conduct Impactful Research

Event Info

icon_calendar.jpgMarch 17, 2022
icon_clock.jpg10am ET
icon_stopwatch.jpgDuration: 1 hour

Speakers

Chuck Grissom, Ph.D., Chief Technology Officer at Optricity

Jana Boerger, Ph.D. student in Machine Learning at Georgia Tech

Russ Meller, Ph.D. Principal Scientist at Fortna

ABSTRACT: Join us for a panel discussion, in which we will explore the following questions and more:  What is the current state of Warehouse Management Systems, Warehouse Execution Platforms, and specialized warehouse software in practice?  What type of information could a researcher assume would be available as an input to a warehouse algorithm?  What general knowledge or technology gap areas exist in making advances in these software?  Why? How can the INFORMS community work to help bridge this gap? 

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Autonomous Vehicles’ Impacts on Americans’ Long-distance  Travel Choices

Event Info

icon_calendar.jpgFebruary 25, 2022
icon_clock.jpg11am ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

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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. She has been a professor of transportation engineering at the University of Texas at Austin for 23 years, and is the recipient of an NSF CAREER Award, Google Research Award, MIT Technology Review Top 100 Innovators Award, Vulog’s Top 20 of 2020 Influential Women in Mobility, and various ASCE, NARSC, TRF, and WTS awards.  She has authored over 180 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.

ABSTRACT: Self-driving or “autonomous” vehicles (AVs) will have passenger travel, freight trade, and emissions impacts. AVs and shared autonomous vehicles (SAVs) are expected to increase vehicle-miles traveled (VMT) by shifting some air travel to relatively long ground trips and adding more vehicle-trips to all roadways (including trips by those presently unable to drive). A Year 2020 survey of 1,004 Americans explored long-distance travel choices with and without AV options, and was coupled with the recent National Household Travel Survey (NHTS) dataset (for trips over 75 miles each way) and the rJourney synthetic trip data set (for US passenger trips over 50 miles each way).

This presentation will explain how count models to predict travel-party size (persons per long-distance trip) show long commute trips, business and shopping trips (and those destined for more population-dense destinations) carrying fewer persons than those for personal, social/recreational, school, and medical reasons (after controlling for trip distance, respondent demographics, and other factors). Younger, more educated, full-time workers and male drivers are more likely to select AVs and SAVs for their long-distance trips (everything else constant). Nationwide destination-zone choices depend on trip purpose and land use attributes at the trip end (as well as travel costs, across modes). Applications of rJourney’s 1.17 billion long-distance passenger trip data (synthetic for Year 2010) suggest that AVs and SAVs will eventually dominate U.S. passenger travel between 100 and 500 miles (one-way), person-trip distance averages will rise nearly 10 percent, and domestic passenger-miles by air will fall by over 50 percent, ceteris paribus, for an overall 6.7% reduction in domestic passenger-miles travelled (relative to a “business as usual”/no-AVs trend scenario). 

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Exact bidirectional algorithm for the constrained shortest path: stochastic extensions and applications

Event Info

icon_calendar.jpgDecember 10, 2021
icon_clock.jpg10am ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

Andrés Medaglia is full professor of Industrial Engineering at Universidad de los Andes (Bogotá, Colombia) and director of the research center Centro para la Optimización y Probabilidad Aplicada (COPA). He has nearly 20 years of experience developing and applying optimization techniques to transportation and logistics, healthy and sustainable cities, engineering design, and agricultural systems. His research has led to over 60 peer-reviewed publications in operational research (OR).  He currently serves in the editorial boards of Transportation Science, Computers and Operations Research, the European Journal of Industrial Engineering, and TOP (journal of the Spanish OR and Statistics Society). He has served as Secretary and Vicepresident of the Latin-Ibero American Association of Operations Research (ALIO); as Vicepresident of Central/South America for the Institute of Industrial and Systems Engineers (IISE); and as Vicepresident of the Colombian Operational Research Society (ASOCIO). In INFORMS, he currently serves in the Publications Committee. He has been the recipient of several awards, most recently, the Glover-Klingman Prize (2020), the INFORMS/TSL President’s Service Award (2017), the IFORS Prize for OR in Development (Quebec City, Canada, 2017), and the EURO Award for the Best EJOR (Review) Paper in 2015. He was the IFORS Invited Tutorial Lecturer at EURO 2018 (Valencia, Spain); and keynote speaker at the IISE Annual Conference and Expo (2021) and Optimization Days (Montréal, Canada, 2014). More information at: http://wwwprof.uniandes.edu.co/~amedagli

Several applications in the field of transportation and logistics involve the solution of underlying large-scale network problems with shortest path structures. The pulse algorithm implements ideas and strategies that have been available in the playground of network optimization for years, but when used collectively under a single framework they become stronger and are able to solve a wide array of hard shortest path problems. At the core of these hard problems lies the constrained shortest path problem (CSP). In the CSP, we look for a minimum‐cost sequence of arcs on a directed network that satisfies knapsack‐type constraints. We present an exact method for the CSP based on a bidirectional adjustable depth-first (or breadth-first) search leveraged by parallelism and a set of acceleration strategies. We also illustrate the use of the (bidirectional) pulse algorithm as a building block to solve hard shortest path variants in the stochastic domain and other combinatorial problems arising in transportation and logistics.




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Dynamic fleet management of on-demand ridesharing systems with mixed-autonomy: A deep reinforcement learning framework

Event Info

icon_calendar.jpgNovember 18, 2021
icon_clock.jpg8 am ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

Dr. Liu Yang is jointly appointed as an Assistant professor in the Department of Civil and Environmental Engineering and the Department of Industrial Systems Engineering and Management at National University of Singapore. She received her B.S. from Tsinghua University, MPhil from Hong Kong University of Science and Technology, and Ph.D. from Northwestern University. Previously, Dr. Liu worked as a consultant at Cambridge Systematics and provided modeling expertise to public agencies such as the Chicago Department of Transportation. Dr. Liu's research focuses on the areas of urban mobility management, travel demand and congestion management, and data-driven transportation system modeling and analysis. Her work has been published in the major journals in the transportation area, including Transportation Research Part A, Part B, Part C, and Part E. Currently, she serves as a member on the editorial advisory board of Transportation Research Part C and an associate editor in Socio-Economic Planning Sciences. She is a co-chair of WTC Shared Logistics and Transportation Systems Committee, a member of Transportation Research Board Standing Committee on Emerging and Innovative Public Transport and Technologies (AP020) and Transportation Network Modeling (AEP40), a member of the Chinese Overseas Transportation Association (COTA) Board of Directors, and a member of WCTRS Special Interest Group Transport Theory and Modelling. Her research is currently supported by Singapore Ministry of Education and ST Engineering.

With the continuous development of urbanization, the past decades have witnessed the rapid growth of the ridesharing systems as a new business mode. Autonomous vehicles (AVs) are expected to be introduced into ridesharing systems for its advantages in central coordination. We aim to optimize the real-time operation of a ridesharing platform transiting from traditional ridesharing networks to fully automated mobility-on-demand systems, where conventional vehicles (CVs) and AVs coexist. The operator can directly control all the AVs and thus make centralized AV dispatching decisions. Human drivers focusing on their own monetary return, however, tend to gather at hot spots and lead to unbalanced supply and demand. Incorporating distinct characteristics of AVs and CVs, we propose a two-sided multi-agent reinforcement learning based framework to dynamically coordinate mixed fleets in ridesharing systems. Specifically, we model the operator and driver decision making procedure as Markov decision processes (MDP), where the operator and drivers interact with the environment at the same time. The operator makes centralized decisions on AV fleet dispatching and driver commission rates, while drivers make relocation decisions in a decentralized manner. The proposed model is validated using a case study in New York City using real taxi trip dataset. Results demonstrate that our algorithm significantly improve system performance. In particular, we also show that charging dynamic commission fee makes both the platform and the drivers better off, especially in scenarios with higher demand volume.


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Event Info

icon_calendar.jpgJune 11, 2021
icon_clock.jpg9 am ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

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John-Paul Clarke is a professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin, where he holds the Ernest Cockrell Jr. Memorial Chair in Engineering. Previously, he was a faculty member at Georgia Tech and MIT, Vice President of Strategic Technologies at United Technologies Corporation (now Raytheon), and a researcher at Boeing and NASA JPL. He has also co-founded multiple companies, most recently Universal Hydrogen – a company dedicated to the development of a comprehensive carbon-free solution for aviation. Clarke is a leading expert in aircraft trajectory prediction and optimization, especially as it pertains to reducing the environmental impact of aviation, and in the development and use of stochastic models and optimization algorithms to improve the efficiency and robustness of aircraft, airline, airport, and air traffic operations. He is the founding chair of the AIAA Human-Machine Teaming Technical Committee, and was co-chair of the National Academies Committee that developed the US National Agenda for Autonomy Research related to Civil Aviation. Clarke received S.B. (1991), S.M. (1992), and Sc.D. (1997) degrees in aeronautics and astronautics from MIT. He is a Fellow of the AIAA and the RAeS, and is a member of AGIFORS, INFORMS, and Sigma Xi.

The national vision for advanced aerial mobility is an airspace system that can support high-scale flight operations supporting any number of applications, using vehicles small and large, carrying passengers or cargo, and operating over cities or in remote areas. This vision will require greater aircraft and air traffic management (ATM) system autonomy; a synergistic relationship between vertiport locations and flight trajectories to address noise, privacy, and safety concerns; and new certification standards for vehicles, systems, and operators. To this end, I will discuss how the first two challenges may be addressed via simulation and optimization, and present prior and ongoing work on frameworks, algorithms, and policies for autonomous decision-making during approach and landing; highly automated multi-aircraft conflict resolution; and trajectory planning to maximize the mission efficiency, success and survivability of autonomous flight vehicles. I will also propose a framework for the certification of vehicles that must both operate and make decisions autonomously.


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Constructing a Resilient Global Port Network Toward Secure Global Supply Chains

Event Info

icon_calendar.jpgMay 7, 2021
icon_clock.jpg12 pm ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

Elise

Elise Miller-Hooks
Professor & Hazel Chair in Infrastructure Engineering
George Mason University

Dr. Elise Miller-Hooks holds the Bill and Eleanor Hazel Endowed Chair in Infrastructure Engineering at George Mason University. Prior to this appointment, she served as a program director at the U.S. National Science Foundation and was a member of the faculties of the University of Maryland, Pennsylvania State University and Duke University. Dr. Miller-Hooks received her Ph.D. in Civil Engineering from the University of Texas – Austin and B.S. in Civil Engineering from Lafayette College. She is an advisor to the World Bank Group and founding Editor-in-Chief of the new IFORS/Elsevier journal: Sustainability Analytics and Modeling. Her research focuses in: mathematical modeling and optimization for transportation systems; multi-hazard civil infrastructure resilience quantification; emergency/disaster planning and response; intermodal passenger and freight transport; real-time routing and fleet management; paratransit, ridesharing and bikeways; stochastic and dynamic network algorithms; and collaborative and multi-objective decision-making.

Ports are critical components of the global supply chain, providing key connections between land- and maritime-based transport modes. They operate in cooperative, but competitive, co-opetitive, environments wherein the throughput of individual ports is linked through an underlying transshipment network. The ports, as well as supporting rail and roadway system infrastructures, however, are by the nature of their designs and locations inherently vulnerable to rising sea levels, significant precipitation events, storm surges and consequent coastal flooding. They are increasingly automated and, thus, greatly reliant on power and communications technologies. They are also subject to other disruptive events of natural or anthropogenic causes. Investments, thus, are needed to protect this intermodal (IM) system from such disruptive forces, and protective actions are required for business continuity during and immediately following a disruption. This presentation proposes optimization, equilibrium and digital twinning techniques for developing multi-stakeholder, protective investment, and response strategies aimed at enhancing resilience of this marine-based IM system to disruption and securing our global supply chains.


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A Branch-Price-and-Cut Algorithm for a Two-Echelon Vehicle Routing Problem with Time Windows

Event Info

icon_calendar.jpgApril 16, 2021
icon_clock.jpg10:00 am ET
icon_stopwatch.jpgDuration: 1 hour  

Speaker

Guy

Guy Desaulniers received his PhD in mathematics from Polytechnique Montreal, where he is a professor in the department of Mathematics and Industrial Engineering. Between 2015 and 2019, he was the director of the GERAD research center. He has supervised more than 65 graduate students, co-authored more than 110 papers published in academic journals, and co-edited a book on column generation. His main research interests are in the areas of large-scale optimization (in particular, column generation), integer programming, combinatorial optimization, and constrained shortest path problems with applications to vehicle routing and crew scheduling in ground, air, rail, and maritime transportation.

In this talk, we consider the two-echelon vehicle routing problem with time windows (2E-VRPTW). This problem arises in city logistics when high-capacity and low-capacity vehicles are used to transport merchandise from depots to satellites (first echelon), and from satellites to customers (second echelon), respectively. The aim is to determine a set of least-cost first- and second-echelon routes such that the load on the routes respect the capacity of the vehicles, each second-echelon route is supplied by exactly one first-echelon route, and each customer is visited by exactly one second-echelon route within its time window. We model this 2E-VRPTW with a route-based formulation involving first-echelon and second-echelon route variables. We propose to solve it using a branch-price-and-cut algorithm where only the second-echelon routes are generated by column generation. We discuss some specialized components of this algorithm, namely, the labeling algorithm for solving the pricing problems as well as deep dual optimal inequalities that are added to reduce degeneracy. We report computational results that show that our BPC algorithm outperforms a state-of-the-art algorithm. We also present sensitivity analysis results on the different components of our algorithm, and derive managerial insights related to the structure of the first-echelon routes.

Guy Desaulniers, Polytechnique Montreal and GERAD, Canada

Co-authors: Tayeb Mhamedi, Marilène Cherkesly, Henrik Andersson

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Cobotic Order Picking Systems

Event Info

icon_calendar.jpgMarch 12, 2021
icon_clock.jpg10:00 am ET
icon_stopwatch.jpgDuration: 1 hour  

Speaker

rene

René (M.) B.M. de Koster is a professor of Logistics and Operations Management at Rotterdam School of Management, Erasmus University, and chairs the department Technology and Operations Management. He holds a PhD from Eindhoven University of Technology. He is the 2018 honorary Francqui Professor at Hasselt University. His research interests are warehousing, material handling, and behavioral operations. He is the founder of the Material Handling Forum and is author / editor of 8 books and over 240 papers in books and academic journals. He is associate editor of Transportation Science, Service Science, and Operations Research.

The new generation of warehouses will be fully robotized. However, in the nearby future, robots will gradually find their way into the operations and will have to work in close collaboration with manual workers. In my talk, I will discuss two types of cobotic order picking systems, where robots and order pickers work together to fill customer orders: drive-on cobots and walk-along cobots. I will discuss empirical, experiment-based work on the impact on pick performance of robots leading versus robots following the pickers, and analytical modelling research on control strategies and collaboration strategies. Our results show that, with proper deployment, cobots may lead to a substantial increase in picking performance.

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Traffic Management and Control in Fully Automated Vehicle Environment

Event Info

icon_calendar.jpgFebruary 12, 2021
icon_clock.jpg9:30am EST
icon_stopwatch.jpgDuration: 1 hour (plus networking)

Speaker

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Dr. Yafeng Yin is a Professor of Civil and Environmental Engineering and a Professor of Industrial and Operations Engineering at University of Michigan, Ann Arbor. He works in the area of transportation systems analysis and modeling, and has published more than 120 refereed papers in leading academic journals. His current research focuses on connected and automated mobility systems.

In this talk, we present rhythmic traffic control, a new paradigm of controlling vehicles to traverse a traffic facility such as a signal-free intersection and a traffic network, in a fully automated vehicle (AV) environment. The fundamental idea of rhythmic control is to determine an underlying beat for the traffic facility and then require AVs to follow the beat and a design speed to enter and traverse the facility; the start of the beat for each conflicting movement is carefully synchronized so that vehicles will pass through a conflict point in an alternating and collision-free manner without any stop. We show that this new control paradigm is simple, but yields superior performance compared with previous paradigms or proposals, and is computationally tractable and thus scalable.

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On-Demand Multimodal Transit Systems: Capturing Travel Mode Adoption and Assessing Resilience

Event Info

icon_calendar.jpgJanuary 15, 2021
icon_clock.jpg9am EST
icon_stopwatch.jpgDuration: 1 hour

Speaker

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Dr. Pascal Van Hentenryck
H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology

Pascal Van Hentenryck is an A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Van Hentenryck’s research focuses in Artificial Intelligence, Data Science, and Operations Research. His current focus is to develop methodologies, algorithms, and systems for addressing challenging problems in mobility, energy systems, resilience, and privacy.