The summer school CO@Work 2024 will be held from September 16 to 27, 2024, at Zuse Institute Berlin and is organized jointly by
TU Berlin,
HTW Berlin,
and Zuse Institute Berlin, and co-sponsored by
MATH+,
BMS,
Forschungscampus MODAL,
I²DAMO,
GOR,
DFG Priority Program 2298,
COPT,
d-fine,
FICO,
GAMS,
and
Gurobi.
This block course addresses everyone interested in the use of computational optimization and mathematical programming in concrete applications from practice, in particular advanced masters students, PhD students, and post-docs.
CO@Work2024 will be the seventh incarnation of this workshop series, and the fifth one being held in Berlin.
The following is a tentative schedule, please note that some details could still change at this stage. On most days, we will have breaks 10:45-11:15, 12:45-14:15, and 15:45-16:15. For some talks, abstracts can already be revealed by clicking on the title of the talk.
Monday, 16.09.2024 | Welcome and Introduction | |
---|---|---|
15:30 | Sebastian Pokutta | Opening |
15:40 | Jens Schulz | Welcome from the German OR Society
German Society of Operations Research is a non-profit organization with more than 1,000 members. GOR spreads the word about Operations Research throughout academia and practice as its key mission. |
15:50 | Annika Preuß-Vermeulen | Berlin Mathematical School PhD program
The Berlin Mathematical School (BMS) is a joint graduate school of the mathematics departments of the three major Berlin universities, TU Berlin, FU Berlin, and HU Berlin, and the graduate school of the Cluster of Excellence MATH+. The BMS offers a course program in English, various events and mentoring opportunities and advice in non-academic issues. Besides striving for excellence, BMS is actively pursuing the goals of diversity in gender and country of origin for its student body. |
16:00 | Timo Berthold, Ambros Gleixner | Organization of the Summer School |
16:30 | Martin Grötschel | Optimization and OR: A Sketch of Historical Developments
If one looks back into recorded history or analyses archaeological findings, it is clear that humans have always tried to act, build, and work efficiently – subject to available knowledge and technology. OR and optimization seem to be genuine human features. I plan to briefly sketch some of these historical developments starting in antiquity, but my focus will be on the details of the advancements in optimization (in particular linear and integer programming) in the last 70 years. One part of my lecture will be staged as a quiz. For those interested in preparing for the quiz, some of the answers can be found in the book “Optimization Stories” that I edited in 2012. The PDF of this book can be downloaded from my homepage at https://www.zib.de/userpage//groetschel/publications/OptimizationStories.pdf or from the Webpage of Documenta Mathematica: https://www.elibm.org/article/10011477 |
17:30 | Thorsten Koch | Data Experiment |
18:00 | Welcome BBQ | |
Tuesday, 17.09.2024 | Fundamentals of Linear Programming and Modelling | |
9:15 | Ambros Gleixner | Linear Programming & Polyhedral Theory |
10:00 | Julian Hall | High Performance Computational Techniques for the Simplex Method
When families of related LP problems are to be solved, most notably as subproblems when solving MIPs, efficient implementations of the simplex algorithm are used. This lecture will discuss the reasons for this, and give an overview of the most important algorithmic variants and computational techniques for their implementation. |
11:15 | Qi Huangfu | Linear Programming: Barrier and First Order Methods |
12:00 | Ambros Gleixner | Aspects of MIP Modelling |
14:15 -17:45 |
Bruno Vieira | Tutorial: Basics of MIP Modelling |
Wednesday, 18.09.2024 | Fundamentals of Mixed Integer Programming | |
9:15 | Timo Berthold | MIP Solving: Branch-and-Bound |
10:00 | Timo Berthold | MIP Solving: Cutting Planes |
11:15 | Timo Berthold | MIP Solving: Primal Heuristics |
12:00 | Timo Berthold | MIP Solving: Presolving |
14:15 -17:45 |
Bruno Vieira | Tutorial: Advanced MIP Modelling |
Thursday, 19.09.2024 | Advanced Mathematical Optimization | |
9:15 | Ksenia Bestuzheva | Global Optimization of Mixed-Integer Nonlinear Programs |
10:00 | Marc Pfetsch | Solving Mixed-Integer Semidefinite Programs
Mixed-integer semidefinite programs deal with optimization subject to semidefinite constraints on matrix variables including integrality conditions. We review solving techniques for such problems. This includes presolving and heuristics. An implementation in SCIP-SDP will be demonstrated. |
11:15 | Ambros Gleixner | Numerics in LP & MIP Solvers |
12:00 | Marco Lübbecke | Branch-and-Price Crash Course
Decomposition and refomulation techniques (like Dantzig-Wolfe) can lead to MIP models with many variables. Even only the LP relaxations of such models need to be solved by column generation. We discuss column generation basics and options how to embed this into a branch-and-cut tree |
14:15 -17:45 |
Mohammed Ghannam, Joao Dionisio | Tutorial: Implementing Branch-and-Price |
Friday, 20.09.2024 | Interactive Optimization and Learning | |
9:15 | Grégoire Montavon | Explainable AI |
10:00 | Jannis Kurtz | Deep Learning in Robust Optimization
Deep learning (DL) is one of the most popular approaches used for recent developments in the realm of Artificial Intelligence. On a high level, the goal in DL is to fit a neural network to available training data to solve classification or regression problems. In this talk we will study neural networks from a mixed-integer optimization perspective. We show that, under certain assumptions, the evaluation of an already trained neural network can be modeled as a mixed-integer linear problem. These trained neural networks can be used to support classical optimization tasks in different ways. The focus of this talk will be on robust optimization problems, where the goal is to find an optimal solution of an optimization problems which is robust against uncertainty in the problem parameters. We will show how MIP representations of neural networks can be used in robust optimization to speed up solution algorithms and model uncertainty sets. |
11:15 | Nicole Megow | Learning-Augmented Algorithms for Scheduling
Uncertainty poses a significant challenge on scheduling and planning tasks, where jobs may have unknown processing times or unknown dependencies. However, assuming a complete lack of a priori information is overly pessimistic. With the rise of machine-learning methods and data-driven applications, access to predictions about input data or algorithmic actions becomes feasible. Yet, blindly trusting these predictions might lead to very poor solutions, due to the absence of quality guarantees. In this talk, we explore recent advancements in the popular framework of Algorithms with Predictions, which integrates such error-prone predictions into online algorithm design. We examine various prediction models and error measures, showcasing learning-augmented algorithms for non-clairvoyant scheduling with strong error-dependent performance guarantees. We demonstrate the potential of imperfect predictions to enhance scheduling efficiency and address uncertainty in real-world scenarios. |
12:00 | Christoph Spiegel | The Role of Machine Learning for Mathematics |
14:15 | Berkant Turan | Tutorial: Hands-on Machine Learning |
16:15- 17:45 |
Mathieu Besancon | Tutorial: Hands-on Frank-Wolfe |
19:00 | Conference Dinner (Berlin TV Tower) | |
Saturday, 21.09.2024 | Vehicle Routing | |
10:00 | Eduardo Uchoa | Exact Algorithms for Vehicle Routing: advances, challenges, and perspectives
The vehicle Routing Problem (VRP) is among the most widely studied problems in operations research and combinatorial optimization. The current state-of-the-art exact VRP algorithms employ a combination of column generation and cut separation, known as Branch-Cut-and-Price (BCP) algorithms. This presentation examines notable recent contributions made by various researchers in the field. Additionally, the talk showcases VRPSolver, a very flexible package that implements a BCP algorithm that achieves outstanding performance for many routing, packing, and scheduling problems. Furthermore, VRPSolverEasy, a recent Python application built on top of VRPSolver, is introduced. While heuristic algorithms are likely to remain the dominant approach for practical routing, the availability of exact solutions for reasonably sized instances opens up new possibilities. |
10:45 | Kai Hoppmann-Baum | "Excuse me, Sir, we ordered 31 minutes ago!" - How to address time delays in food delivery |
11:15 | Thorsten Koch | TBA |
11:45 | Lunch (ZIB Foyer) | |
12:45 -15:00 |
Milena Petkovic | Computational Challenge Day 1 |
Sunday, 22.09.2024 | Day off | |
Monday, 23.09.2024 | Applied Machine Learning and Optimization | |
9:15 | Jan Kronqvist | Building upon MIP and non-smooth optimization to learn robust deep neural networks |
10:00 | Timo Berthold | ML inside MIP solvers |
11:15 | Andrea Lodi | TBA |
12:00 | Ruth Misener | Optimal decision-making problems with trained surrogate models embedded
Several of our recent projects (and complementary projects by other groups worldwide) embed data-driven surrogate models into larger optimal decision-making problems. For example, with the chemicals company BASF, we considered solving inverse problems over trained graph neural networks to design new molecules. This presentation discusses some of the mathematical challenges and practical applications we have explored. We also mention software implementations and close with open challenges in the area. |
14:15- 17:45 |
Milena Petkovic | Computational Challenge Day 2 |
Tuesday, 24.09.2024 | Excursions to Volkswagen and TESLA | |
Wednesday, 25.09.2024 | Energy Systems | |
9:15 | Milena Petkovic | TBA |
10:00 | Inci Yüksel-Ergün | Data Preprocessing and Data Quality Assessment for Energy System Optimization |
11:15 | Jaap Pedersen | Quota Steiner Tree Problem and its Application on Wind Farm Planning |
12:00 | Stephanie Riedmüller | Unit Commitment and Investment Planning for District Heating Networks |
14:15- 17:45 |
Milena Petkovic | Computational Challenge Day 3 |
Thursday, 26.09.2024 | Traffic and Logistics | |
9:15 | Ralf Borndörfer | TBA |
10:00 | Niels Lindner | Periodic timetable optimization in public transport |
11:15 | Daniel Rehfeldt | Optimizing vehicle and crew schedules in public transport |
12:00 | Daniel Roth | Using airline planning software to plan ICU personnel |
14:15- 17:45 |
Milena Petkovic | Computational Challenge Day 4 |
Friday, 27.09.2024 | Industry Day | |
9:15 | Zsolt Csizmádia | TBA |
9:45 | Adele Goutes | How to set optimal prices during a sales event steered by humans? |
10:15 | Jakob Witzig | SAP Supply Chain Optimization |
11:15 | Anna Thünen, Jennifer Uebbing | Optimization in practice: from long to short, from planning to operation of (power) grids |
11:45 | Felix Hennings | Dimension Local Energy Hubs to Reduce Grid Congestion |
12:15 | Petra Bauer | Mathematical Optimization @ Siemens
The research group "Operations Research for Decision Support" at Siemens Technology applies Mathematical Optimization to a broad variety of real-life problems from different Siemens domains such as industry, energy, mobility, or healthcare. This talk presents some facts and figures about Siemens, gives insights into the background and competences of our team as well as our everyday work, and showcases two projects, one about a check-in/be-out ticketing solution, the other about solar power plant layout optimization. |
14:15 | Justine Broihan | Managing the Optimization Pipeline |
14:45 | Tim Januschowski | TBA |
15:15 | Networking with Industry | |
16:15 | Pawel Lichocki | Combinatorial Optimization at Google: tools, solvers, and applications |
16:45 | Matthias Miltenberger | Gurobi OptiMods - Painless Optimization Templates
One of the most important aspects of mathematical optimization and Operations Research is getting your data into a form that optimization solvers can understand and work with. The "art of modeling" as it is often referred to, can all too easily get in the way of actually solving the problem at hand. Gurobi's open-source OptiMods are data-driven Python APIs for different common optimization use cases. They enable practitioners and learners alike to compute solutions without requiring extensive modeling experience. This session presents the goals and design of the project and explains how to use and extend it. We will also give an insight into how the Gurobi Experts team works to get the most out of Gurobi for its customers. |
17:15 | Closing session | |
18:00 | Farewell BBQ | |
Monday, 07.10.2024 | Examination |
The examination for all students that participated on-site and wish to obtain a 10 ECTS certificate will be held one week after the summer school, October 7, 15:00-17:00. The exam will be on-site for students from Berlin universities (in room MA004 at TU Berlin's math building) and remotely for other participants.
There are good and bad news. The bad news first: The application for the in-person event is currently closed because we have reached our capacity limit. If spots become available, we will contact people on our waiting list. That being said, if you cannot join CO@Work for any unforeseen reasons, please let us know so we can pass your spot along.
The good news: There will be a limited (!) online version of CO@Work. Online participants will have the possibility to:
Online participation is free. The registration fee for on-site participation will be 100 EUR per participant, waived for students from Berlin universities. The language of the course is English.
Timo Berthold, Ambros Gleixner, Thorsten Koch, and Milena Petkovic.
For any questions, please contact us at coaw@zib.de.