CO@Work 2024

Computational Optimization at Work

About

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.

Schedule

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 many 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

Linear Programming is the foundation for many state-of-the-art methods in computational optimization. In this talk we will revisit the basic theory and give an overview over the most important solution methods.
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

Real-world applications and their derived mathematical optimization problems often allow for different, theoretically equivalent formulations. In this talk we will discuss examples for this degree of freedom in the world of mixed-integer linear programming, and formulate some best practices in 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

State-of-the-art MIP solvers consist of a plethora of subroutines that take care of different aspects of the solution process and make the solver computationally efficient: presolving, cut generation and selection, primal heuristics, and dedicated node and variable selection rules (aka branching). The focus of all presentations today is to give an insight into strategies that are actually employed by solvers in practice to give an understanding of how such a solver works internally.

This presentation looks into branching strategies. We present various information that can help with making a good decision on how to split the search space and how to traverse it most efficiently.
10:00 Timo Berthold MIP Solving: Cutting Planes

State-of-the-art MIP solvers consist of a plethora of subroutines that take care of different aspects of the solution process and make the solver computationally efficient: presolving, cut generation and selection, primal heuristics, and dedicated node and variable selection rules (aka branching). The focus of all presentations today is to give an insight into strategies that are actually employed by solvers in practice to give an understanding of how such a solver works internally.

This presentation looks into cutting plane algorithms. We will discuss various algorithms to generate additional valid inequalities for a MIP, given a solution of its LP relaxation in order to strengthen the relaxation. We will also touch on the topic of selecting between different such inequalities which to add to the problem formulation and which to drop.
11:15 Timo Berthold MIP Solving: Primal Heuristics

State-of-the-art MIP solvers consist of a plethora of subroutines that take care of different aspects of the solution process and make the solver computationally efficient: presolving, cut generation and selection, primal heuristics, and dedicated node and variable selection rules (aka branching). The focus of all presentations today is to give an insight into strategies that are actually employed by solvers in practice to give an understanding of how such a solver works internally.

This presentation looks into primal heuristics. We will introduce various ideas to try to quickly generate an ad-hoc solution for a mixed-integer program without an exhaustive search. We will also discuss the orchestration of such heuristic algorithms.
12:00 Timo Berthold MIP Solving: Presolving

State-of-the-art MIP solvers consist of a plethora of subroutines that take care of different aspects of the solution process and make the solver computationally efficient: presolving, cut generation and selection, primal heuristics, and dedicated node and variable selection rules (aka branching). The focus of all presentations today is to give an insight into strategies that are actually employed by solvers in practice to give an understanding of how such a solver works internally.

This presentation looks into presolving techniques. We will describe various techniques to tighten the problem formulation, reduce the search space, and improve the numerics of a given MIP model before starting the actual branch-and-bound search process.
14:15
-17:45		 Bruno Vieira, Mohammed Ghannam, Joao Dionisio Tutorial: Advanced MIP Modelling
Thursday, 19.09.2024 Advanced Mathematical Optimization
9:15 Ksenia Bestuzheva Global Optimization of Mixed-Integer Nonlinear Programs

Mixed-integer nonlinear programming (MINLP) deals with problems that simultaneously contain nonlinearities and integer variables. If the problem involves nonconvex nonlinearities, finding optimal solutions and proving optimality is further complicated. This lecture will explain the fundamentals of MINLP and provide an overview of convex and nonconvex MINLP algorithms, their main aspects and the interaction between these aspects. Further, we will discuss practical topics related to the modelling and solving of MINLPs.
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

Virtually all state-of-the-art MIP solvers use floating-point computation. In order to deal with the resulting roundoff errors consistently, they define feasibility and optimality within numerical tolerances, and use many numerical safeguards in order to avoid incorrect results. In this talk we will first give examples for the most common reasons for numerical errors in floating-point MIP solvers, and provide best practices for avoiding numerically troublesome computations. Second, we will present a numerically safe MIP solver implemented in SCIP that is able to solve MIPs exactly over the rational numbers, and produces certificates of correctness that can be checked independently of the solving process.
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

By taking advantage of SCIP’s plugin design, it is not too cumbersome to implement Branch-and-Price to solve problems that would otherwise be unreasonably difficult. In this tutorial session, you will learn how to implement this yourself using PySCIPOpt, SCIP’s Python interface. The session will also cover some of the theory justifying the effectiveness of the approach.
Friday, 20.09.2024 Interactive Optimization and Learning
9:15 Grégoire Montavon Explainable AI, Learning Objectives, and the Clever Hans Effect

In this talk, I will introduce basic concepts of Explainable AI (XAI) such as the problem and techniques of feature attribution. Connections will be made to the problem of optimizing an ML model, in particular how misspecified ML objectives can lead to the Clever Hans effect or "right for the wrong reasons". Finally, I will show how Explainable AI, together with a human in the loop, provides an effective solution to this problem.
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

It is well known that deep neural networks (DNNs) are sensitive to adversarial attacks. For example, it is often possible to change the classification of an image with a minuscule (not visible) targeted disturbance to an image. Here we refer to DNNs as being robust if a bounded disturbance, with a user-defined radius, to the input cannot result in an undesired change in the output (for example, change the classification). The ability to analyze and improve robustness can be critical for real-world applications, especially for safety-critical applications.

In this talk, we will first explore how mixed-integer programming (MIP) can be used to detect weaknesses in DNNs and find adversarial examples. We will cover the commonly used approaches, and briefly discuss current challenges. Furthermore, we explore how we can utilize adversarial examples to improve robustness. By using classical concepts from mathematical programming, such as cutting planes, quadratic programming, and multi-objective optimization, we can re-train DNNs and greatly improve robustness.
10:00 Timo Berthold Machine Learning inside MIP solvers

As we have seen in the previous week, modern MIP solvers consist of many subroutines that take care of different aspects of the solution process: presolving, cut generation, cut selection, primal heuristics, and so forth. For a given MIP, the solver has to make online decisions on which of multiple alternative instantiations of a subroutine to employ or how to combine them. While it is often hard to beat hand-crafted rules, the use of machine learning models for making those decisions has become more prominent in recent years. In this presentation, we will discuss four projects in which we used ML to improve the performance of the solvers Xpress and SCIP on general MIP benchmarks. Two topics relate to cutting planes, while the other two are concerned with numerical stability.
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 Multi-objective design and operation optimization for district heating networks

Supporting decision-making processes for transforming district heating networks poses a challenge in the energy transition. Exploring transformation pathways for the grid while simultaneously optimizing its operation is vital. We model both design and operational decisions via mixed integer linear programming and combine them in an integrated way. However, the goal for decision-making between possible transformation pathways often involves several objectives, e.g., balancing environmental targets and costs. While solving large energy models efficiently is difficult to achieve in general, solving them for multiple objectives even increases efficiency issues. We, therefore, explore algorithmic approaches to find a relevant subset of Pareto-optimal solutions.
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

We will introduce the standard modeling of periodic timetabling problems in public transport by means of event-activity networks and the Periodic Event Scheduling Problem (PESP). We discuss the mathematical structure and complexity of this problem, and focus on how to use techniques from combinatorial optimization and mathematical programming to compute good-quality timetables.
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

With the European Green Deal, the EU has set itself targets for climate neutrality by 2050. This requires the expansion of electricity grids, taking into account the development of other technologies and infrastructures. In particular, the proportion of renewable energies in Europe is rising steadily. As a result, our electricity generation is becoming more and more dependent on the weather and uncertainty in the grid is increasing. Transmission system operators use cross-sector optimization models to compare different future scenarios. With the help of mathematical optimization, we can nevertheless guarantee grid security as cost-effectively as possible.
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.

Speakers

  • Petra Bauer, Siemens AG
  • Mathieu Besancon, ZIB and Inria Grenoble
  • Timo Berthold, TU Berlin and FICO Xpress
  • Ksenia Bestuzheva, ZIB
  • Ralf Borndörfer, FU Berlin and ZIB
  • Justine Broihan, GAMS
  • Zsolt Csizmádia, Amazon
  • Joao Dionisio, Universidade do Porto
  • Mohammed Ghannam, HTW Berlin and felmo GmbH
  • Ambros Gleixner, HTW Berlin and ZIB
  • Adele Goutes, Zalando
  • Martin Grötschel, BBAW
  • Julian Hall, University of Edinburgh
  • Felix Hennings, Doing the Math
  • Qi Huangfu, Cardinal Operations
  • Kai Hoppmann-Baum, Delivery Hero
  • Tim Januschowski, Databricks
  • Thorsten Koch, TU Berlin and ZIB
  • Jan Kronqvist, KTH
  • Jannis Kurtz, University of Amsterdam
  • Pawel Lichocki, Google
  • Niels Lindner, FU Berlin and ZIB
  • Andrea Lodi, Cornell Tech
  • Marco Lübbecke, RWTH Aachen
  • Nicole Megow, University Bremen
  • Matthias Miltenberger, Gurobi Optimization
  • Ruth Misener, Imperial College London
  • Grégoire Montavon, FU Berlin
  • Jaap Pedersen, ZIB
  • Milena Petkovic, ZIB and Leibniz IKZ
  • Annika Preuß-Vermeulen, BMS
  • Marc Pfetsch, TU Darmstadt
  • Sebastian Pokutta, TU Berlin and ZIB
  • Daniel Rehfeldt, Optibus
  • Daniel Roth, Boeing
  • Jens Schulz, Gesellschaft für Operations Research e.V.
  • Christoph Spiegel, ZIB
  • Anna Thünen und Jennifer Uebbing, d-fine
  • Berkant Turan, ZIB
  • Eduardo Uchoa, Universidade Federal Fluminense
  • Bruno Vieira, FICO Xpress
  • Jakob Witzig, SAP
  • Janina Zittel, ZIB

Registration

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:

  • view all* lectures as a stream in a Zoom Webinar (*=provided the lecturer's permission)
  • ask questions via Zoom's Q&A capabilities
Online participants will not have the opportunity to:
  • join the hand's-on tutorials
  • participate in the networking events or the excursion
  • receive any ECTS certificates
  • enjoy late summer in sunny Berlin
If you would like to join CO@Work online, please register here:

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.

Accommodation

There are only few hotels within less than half an hour walking distance of ZIB, those are listed below. If those are booked, we recommend to look for accommodation close to the subway line U3, the bus line X83 or the bus line 101, all of which have a stop close to ZIB.
Please note that September 28/29 is the weekend of the Berlin Marathon, so it is probably a good idea to book as soon as possible, in particular if you wish to stay beyond the last Friday.


**** Seminaris CampusHotel Berlin
Address: Takustr. 39, 14195 Berlin
Web: https://www.seminaris.de/hotels/berlin/seminaris-campushotel-berlin
Getting to ZIB: 300 metres to walk

Apartment Hotel Dahlem
Address: Clayallee 150-152, 14195 Berlin
Web: https://www.apartment-hotel-berlin.de/en/the-hotel/
Getting to ZIB: 6 minutes by bus X83 plus 600 metres to walk OR 25 minutes to walk

*** Ravenna-Hotel Berlin
Address: Grunewaldstr. 8-9, 12165 Berlin
Web: https://www.novum-hotels.com/en/hotel-ravenna-berlin
Getting to ZIB: 3 minutes by bus X83 plus 400 metres to walk OR 20 minutes to walk

**** Hotel Steglitz International
Address: Albrechtstraße 2, 12165 Berlin
Web: https://www.si-hotel.com/
Getting to ZIB: 9 minutes by bus X83 plus 150 metres to walk

*** Hotel Pension Dahlem
Address: Unter den Eichen 89A, 12205 Berlin
Web: https://www.hotel-dahlem.de/
Getting to ZIB: 4 minutes by bus 101 plus 150 metres to walk OR 20 minutes to walk

Hotel Eckstein
Address: Schildhornstraße 72, 12163 Berlin
Web: https://www.hoteleckstein.de/
Getting to ZIB: 25 minutes to walk

Organizers

Timo Berthold, Ambros Gleixner, Thorsten Koch, and Milena Petkovic.

For any questions, please contact us at coaw@zib.de.

Previous Workshops

  • Berlin 2020
    The first virtual CO@Work was held from September 14 to 25, 2020, with recorded lectures by more than 30 distinguished speakers and interactive sessions organized for different time zones.
  • Berlin 2015
    From September 28 to October 10, 2015 more than 160 students from 29 countries, covering all continents except Antarctica, participated in the course held at Zuse Institute Berlin.
  • Berlin 2009
    From September 21 to October 9, 2009 many students from all over the world participated in the course held at the Zuse Institute Berlin.
  • Berlin 2005
    From October 4-15 more than 100 students out of 10 countries participated in the course held at the Zuse Institute Berlin.
  • Görlitz 2006
    From September 3-15 parts of the course where discussed during the Görlitz summer school of the German National Academic Foundation.
  • Beijing 2006 From September 25 to October 6 more than 40 students from all over China attended the course as part of the Workshop Optimization Methods and Applications at the Morningside Center of Mathematics, Chinese Academy of Sciences.