Successfully completed projects

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  • MI-AP11

    TEAM - Tomorrow's elastic, adaptive mobility

    Prof. Dr. Rolf Möhring

    Project heads: Prof. Dr. Rolf Möhring
    Project members: -
    Duration: 01.11.2012 - 31.03.2016
    Status: completed
    Located at: Technische Universität Berlin

    Description

    TEAM stands for Tomorrow’s Elastic Adaptive Mobility. It turns static into elastic mobility by joining drivers, travellers and infrastructure operators together into one collaborative network. Thereby TEAM explicitly takes into account the needs and constraints of all participants and the network itself.

    The vision is to use mobile devices such as smartphones to significantly improve transportation safety and efficiency, implementing environmental aspects. This includes contribution towards the objective of reducing fatalities in the EU, not only addressing drivers but all road users – including passengers and pedestrians. In this way, drivers, travellers and infrastructure are meant to act as a team, adapting to each other and to the situation, creating optimised mobility conditions.

    The success of the project will be demonstrated and validated via innovative applications for end-users and a Europe-wide mobility experiment to illustrate the systems’ benefits in a pan-European setting.

    The project duration is four years. It has started in November 2013 as a joint initiative of 27 partners (now: 28), ranging from car manufacturers to telecommunication providers, research institutes, road infrastructure operators, traffic managers and more.

    http://www.collaborative-team.eu/
  • MI-AP12

    RobuNet - Robust Network Design for Large Scale Logistics

    Prof. Dr. Rolf Möhring / Prof. Dr. Martin Skutella

    Project heads: Prof. Dr. Rolf Möhring / Prof. Dr. Martin Skutella
    Project members: -
    Duration: 01.10.2012 - 30.09.2015
    Status: completed
    Located at: Technische Universität Berlin

    Description

    Facility location decisions belong to the most important cost drivers in the design of modern logistics networks. Moreover these longterm investments determine the framework for finding cost efficient solutions in tactical and operational planning. This close interrelation between operational cost and longterm investments makes an integrated planning of both aspects desirable.

    This integrated approach is even more complex due to the disparate time horizons of both planning aspects. From mathematical point of view, this belongs to the realm of optimizing over scenarios, since the scenario of demands is unknown at the time of investments and the investments have to be convenient for many scenarios. E.g., fluctuations of fuel prices or differing developments of labor costs in different regions constitute relevant uncertainties in designing logistic networks.

    In practice it is common to firstly ignore uncertainties in input data and to react a-postiori to changes. It has been shown that with this practice already small fluctuations can lead to much worse results as opposed to a robust optimization, a modelling technique that considers the possible range of fluctuations in input data a priori. A large gap between the actual state of research and the logistic practice has to be closed here. On the other hand, it is essential to the research of robust optimization to understand which kinds of uncertainties appear in practice.

    The goal of RobuNet is to develop solutions techniques that are tailored for the use in large scale logistics networks, which requires to link actual mathematical research with practical expertise.

    http://www.coga.tu-berlin.de/v-menue/projekte/robunet/
  • MI-AP13

    Algorithms for Complex Scheduling Problems

    Prof. Dr. Rolf Möhring

    Project heads: Prof. Dr. Rolf Möhring
    Project members: -
    Duration: 01.10.2009 - 31.03.2015
    Status: completed
    Located at: Technische Universität Berlin

    Description

    Real-world scheduling problems are usually much more complex than most of the models that were considered in algorithm theory so far. Typically, optimal solutions cannot be found in reasonable computing time. However in practice, good solutions have to be computed fast. To meet the runtime requirements, mostly (simple) heuristics are established in industry, not taking into account results and techniques that are know for related theoretical problems. We aim to start filling this gap between theory and practice for the following fields of scheduling:
    • Integrated Sequencing and Scheduling, a class of problems typically arising in production planning: For a given set of goods, a minimum cost processing sequence has to determined. The cost of a sequence highly depends on the corresponding (non-trivial) scheduling decisions taken, e.g. the scheduling of setup work.
    • Basis Sequencing aims for a minimum cost sequence of a set of given items. In contrast to the previous problem class, the cost incurred by an item solely depends on the neighboring items or on the item's completion time. Basic sequencing problems often occur as a subproblem in integrated sequencing and scheduling, and hence, insights on these problems are of great value.
    • Turnaround Scheduling, a field of scheduling problems which result from shutting down industrial plants for maintenance. These problems are characterized by time-cost tradeoff, precedence constraints, hiring external resources, resource leveling, different working shifts, and risk analysis.

    We seek for insights into the structure and complexity of these problems, which then need to be transferred into efficient algorithms, aiming to produce provably good solutions also for large real-world problems within an appropriate computing time. Realistic data is available from cooperations with T.A. Cook Consultants, PSI Metals and Salzgitter Flachstahl, and Sachsenmilch, respectively (10.000 - 100.000 activities for turnaround scheduling, and two examples from sequencing and scheduling, one from coil coating with 20-40 coils, and another one from dairy industry with 30-40 jobs).

    http://www.coga.tu-berlin.de/v-menue/projekte/complex_scheduling/
  • MI-AP14

    Algorithm engineering for real-time Scheduling and Routing

    Prof. Dr. Martin Skutella

    Project heads: Prof. Dr. Martin Skutella
    Project members: -
    Duration: 01.12.2011 - 31.10.2014
    Status: completed
    Located at: Technische Universität Berlin

    Description

    While 20 years ago microprocessors have mostly been used in desktop computer workstations and large-scale computers, they have meanwhile become ubiquitous. They are used in nearly all areas of technology and specifically in safety and mission critical applications. The future vision of the automotive and avionics industry is complete, safe and reliable drive-by-wire and fly-by-wire respectively. Such industrial applications gave rise to the field of computer science which is called real-time scheduling and routing. Whereas classical scheduling deals with the distribution of a finite set of tasks to a set of machines, real-time scheduling deals with recurring tasks which are often periodic and thus appear infinitely often. Since the processors in a real-time system communicate by passing messages, the messages have to be routed through the architecture (modelled as a directed graph) additionally.

    The goal of the project is to develop, implement, analyse and test algorithms for real-time scheduling and routing problems using methods of linear and integer programming as well as various scheduling and routing techniques from discrete optimization.

    http://www.coga.tu-berlin.de/v_menue/projects/algorithm_engineering_for_real_time_scheduling_and_routing/
  • MI-AP15

    Multicriteria Optimisation

    Prof. Dr. Ralf Borndörfer / Prof. Dr. Martin Skutella

    Project heads: Prof. Dr. Ralf Borndörfer / Prof. Dr. Martin Skutella
    Project members: -
    Duration: 01.01.2012 - 31.12.2016
    Status: completed
    Located at: Technische Universität Berlin / Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    The project "Multicriteria Optimisation" considers mathematical questions and discrete problems within the CRC 1026 "Sustainable Manufacturing". The three sustainability dimensions "economic", "environmental" and "social", respectively, are considered as different objective functions by the project A5. Hence, discrete problems and mathematical questions are modelled by a feasible space of solutions and several objectives which have to be optimised simultaneously. In contrast to the single-criteria case, it is generally not possible to find a solution which optimises all considered objectives simultaneously. Instead one has to deal with trade-offs. For example, the cheapest way to manufacture a certain amount of bicycle frames might not be the environmentally friendliest. A solution that can be improved in at least one objective without getting worse off in the other is called inefficient and will generally be neglected by a decision maker. Hence, only the efficient solutions are interesting from a decision maker's point of view. Besides the mathematical questions about the existence and number of efficient solutions and the algorithmic approaches of how to compute them, the project A5 is also concerned with the modelling of quantitative problems within the CRC 1026. With respect to models the focus and expertise is on mixed integer programming.

    We have developed PolySCIP, an open-source and freely available solver which aims at solving multicriteria mixed integer programs with an arbitrary number of objectives. With respect to scenario analysis two tools, tech-con and field-con, were implemented. Exemplary applications like the optimization of process chains for bicycle frame manufacturing, the selection of sustainable welding processes and design decision support are documented.

    http://www.sustainable-manufacturing.net/en_GB/subproject-a5
  • MI-AP16

    Stability in Networks

    Prof. Dr. Martin Skutella

    Project heads: Prof. Dr. Martin Skutella
    Project members: -
    Duration: 01.01.2013 - 31.12.2015
    Status: completed
    Located at: Technische Universität Berlin

    Description



  • MI-AP18

    Discrete-Valued Sparse Signals - Theory, Algorithms and Applications

    Prof. Dr. Gitta Kutyniok

    Project heads: Prof. Dr. Gitta Kutyniok
    Project members: -
    Duration: 01.10.2014 - 30.09.2016
    Status: completed
    Located at: Technische Universität Berlin

    Description

    Over the last decade, compressed sensing (CS) has gained enormous attention, both from a theoretical point of view and from its various applications. The key point in compressed sensing is to solve underdetermined systems of linear equations under the assumption that the unknown vector is sparse, i.e., a signal where only a few non-zero components are present. It is very attractive to use ideas and tools developed in compressed sensing in digital communications. Exemplary scenarios are transmitter-side signal optimization (e.g., peak-to-average power ratio reduction), multiple-access schemes with small duty cycles, source coding schemes, and radar applications. However, in these scenarios the vector/the signal to be recovered (from noisy measurements) may not only be sparse, but it is beneficial that its elements are taken from a discrete set. Hence, discrete sparse signals are extremely relevant in digital communication systems and signal processing. Unfortunately, such signals and the respective recovery algorithms are not yet studied adequately---if at all---in the literature. Consequently, this proposal addresses the application of compressed sensing methodology to the analysis of discrete-valued sparse signals. Effort has to be spent to fundamentally understand the problem from the mathematical side. To this end, we aim to develop a comprehensive theory for the recovery of discrete sparse signals, both from a geometric viewpoint and by adopting analytical results and tools. Moreover, we devise tailored recovery algorithms, thereby interpreting discrete compressed sensing as a link between classical compressed sensing and a multiple-input/multiple-output decoding task. Finally, the application of discrete sparse signals in communications, sensor networks, and for the identification of channel operators will be addressed.

    http://gepris.dfg.de/gepris/projekt/257184199?language=en
  • MI-AP20

    Eigenvalue Analysis and Model Reduction in the Treatment of Disc Brake Squeal

    Prof. Dr. Volker Mehrmann

    Project heads: Prof. Dr. Volker Mehrmann
    Project members: -
    Duration: 01.09.2012 - 31.03.2015
    Status: completed
    Located at: Technische Universität Berlin

    Description

    Disc brake squeal is a frequent and annoying phenomenon. It arises from self-excited vibrations caused by friction forces at the pad-rotor interface for an industrial brake model [1] (see Figure 1a and 1b). In order to satisfy customers, the automotive industry has been trying for decades to reduce squeal by changing the design of the brake and the disc. So far, it has found no satisfactory solutions that can be implemented in a systematic way. To improve the situation, several car manufacturers, suppliers, and software companies initiated a joint project, supported by the German Federal Ministry of Economics and Technology, which included two mechanical engineering groups at Technical University (TU) Berlin and TU Hamburg-Harburg, and the numerical analysis group at TU Berlin [1]. The goal of the project was to develop a mathematical model of a brake system with all effects that may cause squeal, to simulate the brake behavior for many different parameters, and to generate a small-scale reduced-order model that can be used for optimization.

    https://sinews.siam.org/DetailsPage/TabId/900/ArtMID/2243/ArticleID/443/Eigenvalue-Analysis-and-Model-Reduction-in-the-Treatment-of-Disc-Brake-Squeal.aspx
  • MI-AP4

    Models, algorithms and complexity for scheduling under uncertainty

    Project heads: -
    Project members: -
    Duration: 01.04.2012 - 31.03.2016
    Status: completed
    Located at: Technische Universität Berlin

    Description

    The vast majority of scheduling research assumes complete information about the problem instance. In most real-world applications, however, uncertain input that is gradually revealed during schedule execution is an omnipresent issue. Unlike its deterministic counterpart, the diverse field of scheduling under uncertainty is not well understood from an algorithmic point of view. Moreover, most current approaches on algorithm’s design assume arbitrary algorithmic flexibility and neglect practice-driven limitations on adaptivity. In this project we design algorithmic and analytic tools for solving important scheduling problems with uncertain input, such as unreliable machines, stochastic job processing times, or unknown job arrival times. Our major goal is to study thoroughly the tradeoff between the performance of an algorithm and the amount of adaptivity it requires. On the one hand, we aim for best possible algorithms which are potentially highly dynamic, i.e., scheduling decisions may adapt arbitrarily to the instantiated problem data. On the other hand, we are interested in good but simple algorithms that respect practice-driven adaptivity restrictions. We analyze what kind and what amount of adaptivity an algorithm needs to achieve a certain performance guarantee. Our main tools come from approximation algorithms, combinatorial optimization, mathematical programming, and probability theory, and our investigations integrate the concepts of universal and robust solu- tions. We study fundamental theoretical questions on practically relevant algorithms for problems from stochastic, online, and real-time scheduling.

    https://www.coga.tu-berlin.de/megow-group/junior_research_group_dr_nicole_megow/

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