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Since 2019, Matheon's application-oriented mathematical research activities are being continued in the framework of the Cluster of Excellence MATH+
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Prof. Dr. Rolf Möhring

Projects as a project leader

  • 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


    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.
  • 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


    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.
  • 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


    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).