DEMO seminar on "Sustainable public transport" and "Multi-objective robust optimization"

Prof. Anita Schöbel (Professor at the Department of Mathematics, University of Kaiserslautern-Landau and Director of Fraunhofer ITWM, Germany) will visit the Multiobjective Optimization Group on the 30th and 31st of May. She will give a DEMO seminar talk on the 30th (Thursday) from 13:15 to 15:00 in room Ag C234.1. You can also join us online via Zoom: https://jyufi.zoom.us/j/69643413385.

Event information

Event date
-
Event type
Public lectures, seminars and round tables
Event language
English
Event accessibility
Event space is accessible for all
Event organizer
Faculty of Information Technology
Event payment
Free of charge
Event location category
Mattilanniemi

The talk will be divided into two topics:

 

Topic 1: Sustainable public transport

Abstract: Moving travelers efficiently, with low costs, and respecting environmental goals like CO2 emissions is one of the challenging problems our society faces today. In this talk we sketch how optimization approaches can help to reach these goals. We start by sketching some bits of research on optimizing public transportation. Here we focus on line planning and delay management helping to design reliable and efficient public transport systems. Furthermore, we show that integrating the different planning stages may help in further improving public transport. Finally, we argue that for reaching the above mentioned goals, we need also look at other modes of transport besides regular bus or metro transportation. This might include demand-responsive transport, individual cars, sharing modes and active modes like walking and biking. We sketch a first model in which such different transport modes are considered simultaneously.

 

Topic 2: Multi-objective robust optimization: Concepts, Results and Algorithms

 Abstract: Multi-objective optimization deals with the optimization under multiple (maybe conflicting) criteria while robust optimization aims to find solutions which are best in the worst case. In this talk we combine both and consider robust multi-objective optimization. We start with robust single-objective optimization focusing on strict, regret, and light robustness. We then consider multi-objective robust optimization for which we introduce different robustness concepts: scenario-based approaches, set-based approaches and vector-based approaches. We show their properties and develop a method on how to compare them with each other. Finally, we sketch different algorithmic approaches. In more detail we develop two different approaches for combining a multi-objective algorithm (in our case Dichotomic search) with a cutting-plane approach known from robust optimization and present experimental results for their performances.

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